title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k ⌀ | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 ⌀ | updated_at stringlengths 20 20 |
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ENH: Timestamp: Support addl datetime classmethods | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index a4d7c3969f056..4274eb5c9ec58 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -43,6 +43,7 @@ Enhancements
- Added ability to export Categorical data to Stata (:issue:`8633`).
- Added ability to export Categorical data to to/from HDF5 (:issue:`7621`). Queries work the same as if it was an object array. However, the ``category`` dtyped data is stored in a more efficient manner. See :ref:`here <io.hdf5-categorical>` for an example and caveats w.r.t. prior versions of pandas.
+- Added support for ``utcfromtimestamp()``, ``fromtimestamp()``, and ``combine()`` on `Timestamp` class (:issue:`5351`).
.. _whatsnew_0152.performance:
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index bf1f0d31e8d3e..e6b4bf23e806f 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -1,4 +1,5 @@
# pylint: disable-msg=E1101,W0612
+import calendar
from datetime import datetime, time, timedelta
import sys
import operator
@@ -3291,6 +3292,16 @@ def compare(x, y):
compare(Timestamp.now('UTC'), datetime.now(timezone('UTC')))
compare(Timestamp.utcnow(), datetime.utcnow())
compare(Timestamp.today(), datetime.today())
+ current_time = calendar.timegm(datetime.now().utctimetuple())
+ compare(Timestamp.utcfromtimestamp(current_time),
+ datetime.utcfromtimestamp(current_time))
+ compare(Timestamp.fromtimestamp(current_time),
+ datetime.fromtimestamp(current_time))
+
+ date_component = datetime.utcnow()
+ time_component = (date_component + timedelta(minutes=10)).time()
+ compare(Timestamp.combine(date_component, time_component),
+ datetime.combine(date_component, time_component))
def test_class_ops_dateutil(self):
tm._skip_if_no_dateutil()
@@ -3303,6 +3314,16 @@ def compare(x,y):
compare(Timestamp.now('UTC'), datetime.now(tzutc()))
compare(Timestamp.utcnow(),datetime.utcnow())
compare(Timestamp.today(),datetime.today())
+ current_time = calendar.timegm(datetime.now().utctimetuple())
+ compare(Timestamp.utcfromtimestamp(current_time),
+ datetime.utcfromtimestamp(current_time))
+ compare(Timestamp.fromtimestamp(current_time),
+ datetime.fromtimestamp(current_time))
+
+ date_component = datetime.utcnow()
+ time_component = (date_component + timedelta(minutes=10)).time()
+ compare(Timestamp.combine(date_component, time_component),
+ datetime.combine(date_component, time_component))
def test_basics_nanos(self):
val = np.int64(946684800000000000).view('M8[ns]')
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 3834e4e6b24d9..aed6dea264be6 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -228,6 +228,17 @@ class Timestamp(_Timestamp):
def utcnow(cls):
return cls.now('UTC')
+ @classmethod
+ def utcfromtimestamp(cls, ts):
+ return cls(datetime.utcfromtimestamp(ts))
+
+ @classmethod
+ def fromtimestamp(cls, ts):
+ return cls(datetime.fromtimestamp(ts))
+
+ @classmethod
+ def combine(cls, date, time):
+ return cls(datetime.combine(date, time))
def __new__(cls, object ts_input, object offset=None, tz=None, unit=None):
cdef _TSObject ts
| - `utcfromtimestamp`,`timestamp`, and `combine` now supported
Closes #5351.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8837 | 2014-11-17T04:13:57Z | 2014-11-17T10:56:44Z | 2014-11-17T10:56:44Z | 2014-11-17T10:56:49Z |
FIX: Correct Categorical behavior in StataReader | diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index 3d7b41a1c4c24..a7091d6ab38fb 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -546,7 +546,8 @@ Getting Data In/Out
Writing data (`Series`, `Frames`) to a HDF store that contains a ``category`` dtype was implemented
in 0.15.2. See :ref:`here <io.hdf5-categorical>` for an example and caveats.
-Writing data to/from Stata format files was implemented in 0.15.2.
+Writing data to and reading data from *Stata* format files was implemented in
+0.15.2. See :ref:`here <io.stata-categorical>` for an example and caveats.
Writing to a CSV file will convert the data, effectively removing any information about the
categorical (categories and ordering). So if you read back the CSV file you have to convert the
diff --git a/doc/source/io.rst b/doc/source/io.rst
index bd6400787ae58..9686a72d43cf8 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -3204,8 +3204,8 @@ format store like this:
.. ipython:: python
store_export = HDFStore('export.h5')
- store_export.append('df_dc', df_dc, data_columns=df_dc.columns)
- store_export
+ store_export.append('df_dc', df_dc, data_columns=df_dc.columns)
+ store_export
.. ipython:: python
:suppress:
@@ -3240,8 +3240,8 @@ number of options, please see the docstring.
legacy_store
# copy (and return the new handle)
- new_store = legacy_store.copy('store_new.h5')
- new_store
+ new_store = legacy_store.copy('store_new.h5')
+ new_store
new_store.close()
.. ipython:: python
@@ -3651,14 +3651,14 @@ You can access the management console to determine project id's by:
.. _io.stata:
-STATA Format
+Stata Format
------------
.. versionadded:: 0.12.0
.. _io.stata_writer:
-Writing to STATA format
+Writing to Stata format
~~~~~~~~~~~~~~~~~~~~~~~
The method :func:`~pandas.core.frame.DataFrame.to_stata` will write a DataFrame
@@ -3753,6 +3753,53 @@ Alternatively, the function :func:`~pandas.io.stata.read_stata` can be used
import os
os.remove('stata.dta')
+.. _io.stata-categorical:
+
+Categorical Data
+~~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.15.2
+
+``Categorical`` data can be exported to *Stata* data files as value labeled data.
+The exported data consists of the underlying category codes as integer data values
+and the categories as value labels. *Stata* does not have an explicit equivalent
+to a ``Categorical`` and information about *whether* the variable is ordered
+is lost when exporting.
+
+.. warning::
+
+ *Stata* only supports string value labels, and so ``str`` is called on the
+ categories when exporting data. Exporting ``Categorical`` variables with
+ non-string categories produces a warning, and can result a loss of
+ information if the ``str`` representations of the categories are not unique.
+
+Labeled data can similarly be imported from *Stata* data files as ``Categorical``
+variables using the keyword argument ``convert_categoricals`` (``True`` by default).
+By default, imported ``Categorical`` variables are ordered according to the
+underlying numerical data. However, setting ``order_categoricals=False`` will
+import labeled data as ``Categorical`` variables without an order.
+
+.. note::
+
+ When importing categorical data, the values of the variables in the *Stata*
+ data file are not generally preserved since ``Categorical`` variables always
+ use integer data types between ``-1`` and ``n-1`` where ``n`` is the number
+ of categories. If the original values in the *Stata* data file are required,
+ these can be imported by setting ``convert_categoricals=False``, which will
+ import original data (but not the variable labels). The original values can
+ be matched to the imported categorical data since there is a simple mapping
+ between the original *Stata* data values and the category codes of imported
+ Categorical variables: missing values are assigned code ``-1``, and the
+ smallest original value is assigned ``0``, the second smallest is assigned
+ ``1`` and so on until the largest original value is assigned the code ``n-1``.
+
+.. note::
+
+ *Stata* suppots partially labeled series. These series have value labels for
+ some but not all data values. Importing a partially labeled series will produce
+ a ``Categorial`` with string categories for the values that are labeled and
+ numeric categories for values with no label.
+
.. _io.perf:
Performance Considerations
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 61d18da45e5f0..fedd3ddabf045 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -41,10 +41,11 @@ API changes
Enhancements
~~~~~~~~~~~~
-- Added ability to export Categorical data to Stata (:issue:`8633`).
+- Added ability to export Categorical data to Stata (:issue:`8633`). See :ref:`here <io.stata-categorical>` for limitations of categorical variables exported to Stata data files.
- Added ability to export Categorical data to to/from HDF5 (:issue:`7621`). Queries work the same as if it was an object array. However, the ``category`` dtyped data is stored in a more efficient manner. See :ref:`here <io.hdf5-categorical>` for an example and caveats w.r.t. prior versions of pandas.
- Added support for ``utcfromtimestamp()``, ``fromtimestamp()``, and ``combine()`` on `Timestamp` class (:issue:`5351`).
- Added Google Analytics (`pandas.io.ga`) basic documentation (:issue:`8835`). See :ref:`here<remote_data.ga>`.
+- Added flag ``order_categoricals`` to ``StataReader`` and ``read_stata`` to select whether to order imported categorical data (:issue:`8836`). See :ref:`here <io.stata-categorical>` for more information on importing categorical variables from Stata data files.
.. _whatsnew_0152.performance:
@@ -73,6 +74,7 @@ Bug Fixes
+- Imported categorical variables from Stata files retain the ordinal information in the underlying data (:issue:`8836`).
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index ab9d330b48988..45d3274088c75 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -29,7 +29,8 @@
def read_stata(filepath_or_buffer, convert_dates=True,
convert_categoricals=True, encoding=None, index=None,
- convert_missing=False, preserve_dtypes=True, columns=None):
+ convert_missing=False, preserve_dtypes=True, columns=None,
+ order_categoricals=True):
"""
Read Stata file into DataFrame
@@ -58,11 +59,14 @@ def read_stata(filepath_or_buffer, convert_dates=True,
columns : list or None
Columns to retain. Columns will be returned in the given order. None
returns all columns
+ order_categoricals : boolean, defaults to True
+ Flag indicating whether converted categorical data are ordered.
"""
reader = StataReader(filepath_or_buffer, encoding)
return reader.data(convert_dates, convert_categoricals, index,
- convert_missing, preserve_dtypes, columns)
+ convert_missing, preserve_dtypes, columns,
+ order_categoricals)
_date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
@@ -1136,7 +1140,8 @@ 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, columns=None):
+ convert_missing=False, preserve_dtypes=True, columns=None,
+ order_categoricals=True):
"""
Reads observations from Stata file, converting them into a dataframe
@@ -1161,6 +1166,8 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None,
columns : list or None
Columns to retain. Columns will be returned in the given order.
None returns all columns
+ order_categoricals : boolean, defaults to True
+ Flag indicating whether converted categorical data are ordered.
Returns
-------
@@ -1228,7 +1235,7 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None,
for col, typ in zip(data, self.typlist):
if type(typ) is int:
- data[col] = data[col].apply(self._null_terminate, convert_dtype=True,)
+ data[col] = data[col].apply(self._null_terminate, convert_dtype=True)
cols_ = np.where(self.dtyplist)[0]
@@ -1288,19 +1295,25 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None,
col = data.columns[i]
data[col] = _stata_elapsed_date_to_datetime_vec(data[col], self.fmtlist[i])
- if convert_categoricals:
- cols = np.where(
- lmap(lambda x: x in compat.iterkeys(self.value_label_dict),
- self.lbllist)
- )[0]
- for i in cols:
- col = data.columns[i]
- labeled_data = np.copy(data[col])
- labeled_data = labeled_data.astype(object)
- for k, v in compat.iteritems(
- self.value_label_dict[self.lbllist[i]]):
- labeled_data[(data[col] == k).values] = v
- data[col] = Categorical.from_array(labeled_data)
+ if convert_categoricals and self.value_label_dict:
+ value_labels = list(compat.iterkeys(self.value_label_dict))
+ cat_converted_data = []
+ for col, label in zip(data, self.lbllist):
+ if label in value_labels:
+ # Explicit call with ordered=True
+ cat_data = Categorical(data[col], ordered=order_categoricals)
+ value_label_dict = self.value_label_dict[label]
+ categories = []
+ for category in cat_data.categories:
+ if category in value_label_dict:
+ categories.append(value_label_dict[category])
+ else:
+ categories.append(category) # Partially labeled
+ cat_data.categories = categories
+ cat_converted_data.append((col, cat_data))
+ else:
+ cat_converted_data.append((col, data[col]))
+ data = DataFrame.from_items(cat_converted_data)
if not preserve_dtypes:
retyped_data = []
diff --git a/pandas/io/tests/data/stata10_115.dta b/pandas/io/tests/data/stata10_115.dta
new file mode 100755
index 0000000000000..b917dde5ad47d
Binary files /dev/null and b/pandas/io/tests/data/stata10_115.dta differ
diff --git a/pandas/io/tests/data/stata10_117.dta b/pandas/io/tests/data/stata10_117.dta
new file mode 100755
index 0000000000000..b917dde5ad47d
Binary files /dev/null and b/pandas/io/tests/data/stata10_117.dta differ
diff --git a/pandas/io/tests/data/stata11_115.dta b/pandas/io/tests/data/stata11_115.dta
new file mode 100755
index 0000000000000..cfcd250f1cd9f
Binary files /dev/null and b/pandas/io/tests/data/stata11_115.dta differ
diff --git a/pandas/io/tests/data/stata11_117.dta b/pandas/io/tests/data/stata11_117.dta
new file mode 100755
index 0000000000000..79dfffd94483f
Binary files /dev/null and b/pandas/io/tests/data/stata11_117.dta differ
diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py
index d97feaea2658a..a99bcf741792f 100644
--- a/pandas/io/tests/test_stata.py
+++ b/pandas/io/tests/test_stata.py
@@ -14,6 +14,7 @@
import pandas as pd
from pandas.compat import iterkeys
from pandas.core.frame import DataFrame, Series
+from pandas.core.common import is_categorical_dtype
from pandas.io.parsers import read_csv
from pandas.io.stata import (read_stata, StataReader, InvalidColumnName,
PossiblePrecisionLoss, StataMissingValue)
@@ -81,6 +82,11 @@ def setUp(self):
self.dta18_115 = os.path.join(self.dirpath, 'stata9_115.dta')
self.dta18_117 = os.path.join(self.dirpath, 'stata9_117.dta')
+ self.dta19_115 = os.path.join(self.dirpath, 'stata10_115.dta')
+ self.dta19_117 = os.path.join(self.dirpath, 'stata10_117.dta')
+
+ self.dta20_115 = os.path.join(self.dirpath, 'stata11_115.dta')
+ self.dta20_117 = os.path.join(self.dirpath, 'stata11_117.dta')
def read_dta(self, file):
# Legacy default reader configuration
@@ -817,6 +823,72 @@ def test_categorical_with_stata_missing_values(self):
written_and_read_again = self.read_dta(path)
tm.assert_frame_equal(written_and_read_again.set_index('index'), original)
+ def test_categorical_order(self):
+ # Directly construct using expected codes
+ # Format is is_cat, col_name, labels (in order), underlying data
+ expected = [(True, 'ordered', ['a', 'b', 'c', 'd', 'e'], np.arange(5)),
+ (True, 'reverse', ['a', 'b', 'c', 'd', 'e'], np.arange(5)[::-1]),
+ (True, 'noorder', ['a', 'b', 'c', 'd', 'e'], np.array([2, 1, 4, 0, 3])),
+ (True, 'floating', ['a', 'b', 'c', 'd', 'e'], np.arange(0, 5)),
+ (True, 'float_missing', ['a', 'd', 'e'], np.array([0, 1, 2, -1, -1])),
+ (False, 'nolabel', [1.0, 2.0, 3.0, 4.0, 5.0], np.arange(5)),
+ (True, 'int32_mixed', ['d', 2, 'e', 'b', 'a'], np.arange(5))]
+ cols = []
+ for is_cat, col, labels, codes in expected:
+ if is_cat:
+ cols.append((col, pd.Categorical.from_codes(codes, labels)))
+ else:
+ cols.append((col, pd.Series(labels, dtype=np.float32)))
+ expected = DataFrame.from_items(cols)
+
+ # Read with and with out categoricals, ensure order is identical
+ parsed_115 = read_stata(self.dta19_115)
+ parsed_117 = read_stata(self.dta19_117)
+ tm.assert_frame_equal(expected, parsed_115)
+ tm.assert_frame_equal(expected, parsed_117)
+
+ # Check identity of codes
+ for col in expected:
+ if is_categorical_dtype(expected[col]):
+ print(col)
+ tm.assert_series_equal(expected[col].cat.codes,
+ parsed_115[col].cat.codes)
+ tm.assert_index_equal(expected[col].cat.categories,
+ parsed_115[col].cat.categories)
+
+ def test_categorical_sorting(self):
+ parsed_115 = read_stata(self.dta20_115)
+ parsed_117 = read_stata(self.dta20_117)
+ # Sort based on codes, not strings
+ parsed_115 = parsed_115.sort("srh")
+ parsed_117 = parsed_117.sort("srh")
+ # Don't sort index
+ parsed_115.index = np.arange(parsed_115.shape[0])
+ parsed_117.index = np.arange(parsed_117.shape[0])
+ codes = [-1, -1, 0, 1, 1, 1, 2, 2, 3, 4]
+ categories = ["Poor", "Fair", "Good", "Very good", "Excellent"]
+ expected = pd.Series(pd.Categorical.from_codes(codes=codes,
+ categories=categories))
+ tm.assert_series_equal(expected, parsed_115["srh"])
+ tm.assert_series_equal(expected, parsed_117["srh"])
+
+ def test_categorical_ordering(self):
+ parsed_115 = read_stata(self.dta19_115)
+ parsed_117 = read_stata(self.dta19_117)
+
+ parsed_115_unordered = read_stata(self.dta19_115,
+ order_categoricals=False)
+ parsed_117_unordered = read_stata(self.dta19_117,
+ order_categoricals=False)
+ for col in parsed_115:
+ if not is_categorical_dtype(parsed_115[col]):
+ continue
+ tm.assert_equal(True, parsed_115[col].cat.ordered)
+ tm.assert_equal(True, parsed_117[col].cat.ordered)
+ tm.assert_equal(False, parsed_115_unordered[col].cat.ordered)
+ tm.assert_equal(False, parsed_117_unordered[col].cat.ordered)
+
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| Ensure that category codes have the same order as the underlying Stata data.
xref #8816
| https://api.github.com/repos/pandas-dev/pandas/pulls/8836 | 2014-11-17T04:09:58Z | 2014-11-19T11:20:03Z | 2014-11-19T11:20:03Z | 2014-11-19T17:15:00Z |
DOC GH3508 (bis) added basic documentation of google analytics in remot... | diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst
index f4c9d6a24cea2..ac9b6c9aecc4a 100644
--- a/doc/source/remote_data.rst
+++ b/doc/source/remote_data.rst
@@ -27,14 +27,14 @@ Remote Data Access
.. _remote_data.data_reader:
-Functions from :mod:`pandas.io.data` extract data from various Internet
-sources into a DataFrame. Currently the following sources are supported:
+Functions from :mod:`pandas.io.data` and :mod:`pandas.io.ga` extract data from various Internet sources into a DataFrame. Currently the following sources are supported:
- - Yahoo! Finance
- - Google Finance
- - St. Louis FED (FRED)
- - Kenneth French's data library
- - World Bank
+ - :ref:`Yahoo! Finance<remote_data.yahoo>`
+ - :ref:`Google Finance<remote_data.google>`
+ - :ref:`St.Louis FED (FRED)<remote_data.fred>`
+ - :ref:`Kenneth French's data library<remote_data.ff>`
+ - :ref:`World Bank<remote_data.wb>`
+ - :ref:`Google Analytics<remote_data.ga>`
It should be noted, that various sources support different kinds of data, so not all sources implement the same methods and the data elements returned might also differ.
@@ -330,7 +330,62 @@ indicators, or a single "bad" (#4 above) country code).
See docstrings for more info.
+.. _remote_data.ga:
+Google Analytics
+----------------
+The :mod:`~pandas.io.ga` module provides a wrapper for
+`Google Analytics API <https://developers.google.com/analytics/devguides>`__
+to simplify retrieving traffic data.
+Result sets are parsed into a pandas DataFrame with a shape and data types
+derived from the source table.
+Configuring Access to Google Analytics
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The first thing you need to do is to setup accesses to Google Analytics API. Follow the steps below:
+
+#. In the `Google Developers Console <https://console.developers.google.com>`__
+ #. enable the Analytics API
+ #. create a new project
+ #. create a new Client ID for an "Installed Application" (in the "APIs & auth / Credentials section" of the newly created project)
+ #. download it (JSON file)
+#. On your machine
+ #. rename it to ``client_secrets.json``
+ #. move it to the ``pandas/io`` module directory
+
+The first time you use the :func:`read_ga` funtion, a browser window will open to ask you to authentify to the Google API. Do proceed.
+
+Using the Google Analytics API
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following will fetch users and pageviews (metrics) data per day of the week, for the first semester of 2014, from a particular property.
+
+.. code-block:: python
+
+ import pandas.io.ga as ga
+ ga.read_ga(
+ account_id = "2360420",
+ profile_id = "19462946",
+ property_id = "UA-2360420-5",
+ metrics = ['users', 'pageviews'],
+ dimensions = ['dayOfWeek'],
+ start_date = "2014-01-01",
+ end_date = "2014-08-01",
+ index_col = 0,
+ filters = "pagePath=~aboutus;ga:country==France",
+ )
+
+The only mandatory arguments are ``metrics,`` ``dimensions`` and ``start_date``. We can only strongly recommend you to always specify the ``account_id``, ``profile_id`` and ``property_id`` to avoid accessing the wrong data bucket in Google Analytics.
+
+The ``index_col`` argument indicates which dimension(s) has to be taken as index.
+
+The ``filters`` argument indicates the filtering to apply to the query. In the above example, the page has URL has to contain ``aboutus`` AND the visitors country has to be France.
+
+Detailed informations in the followings:
+
+* `pandas & google analytics, by yhat <http://blog.yhathq.com/posts/pandas-google-analytics.html>`__
+* `Google Analytics integration in pandas, by Chang She <http://quantabee.wordpress.com/2012/12/17/google-analytics-pandas/>`__
+* `Google Analytics Dimensions and Metrics Reference <https://developers.google.com/analytics/devguides/reporting/core/dimsmets>`_
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index a4d7c3969f056..84610716bd91f 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -43,6 +43,7 @@ Enhancements
- Added ability to export Categorical data to Stata (:issue:`8633`).
- Added ability to export Categorical data to to/from HDF5 (:issue:`7621`). Queries work the same as if it was an object array. However, the ``category`` dtyped data is stored in a more efficient manner. See :ref:`here <io.hdf5-categorical>` for an example and caveats w.r.t. prior versions of pandas.
+- Added Google Analytics (`pandas.io.ga`) basic documentation (:issue:`8835`). See :ref:`here<remote_data.ga>`.
.. _whatsnew_0152.performance:
| added the GA doc to the correct file: "remote_data.rst"
closes #3508
| https://api.github.com/repos/pandas-dev/pandas/pulls/8835 | 2014-11-16T21:31:19Z | 2014-11-17T11:05:18Z | null | 2014-11-18T20:12:59Z |
BUG: Bug in csv parsing when passing dtype and names and the parsed data is a different data type (GH8833) | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 3f72d5d44f870..b51da47563b1b 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -64,6 +64,7 @@ Bug Fixes
- Bug in packaging pandas with ``py2app/cx_Freeze`` (:issue:`8602`, :issue:`8831`)
- 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 and when it receives no data from Yahoo (:issue:`8761`), (:issue:`8783`).
+- Unclear error message in csv parsing when passing dtype and names and the parsed data is a different data type (:issue:`8833`)
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
- ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`).
diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py
index 8440d45ffb4be..7b8bdeb1f38b8 100644
--- a/pandas/io/tests/test_parsers.py
+++ b/pandas/io/tests/test_parsers.py
@@ -485,7 +485,6 @@ def test_index_col_named(self):
h = "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n"
data = h + no_header
- # import pdb; pdb.set_trace()
rs = self.read_csv(StringIO(data), index_col='ID')
xp = self.read_csv(StringIO(data), header=0).set_index('ID')
tm.assert_frame_equal(rs, xp)
@@ -2864,8 +2863,8 @@ def test_empty_lines(self):
def test_whitespace_lines(self):
data = """
-\t \t\t
- \t
+\t \t\t
+ \t
A,B,C
\t 1,2.,4.
5.,NaN,10.0
@@ -3110,8 +3109,8 @@ def test_empty_lines(self):
def test_whitespace_lines(self):
data = """
-\t \t\t
- \t
+\t \t\t
+ \t
A,B,C
\t 1,2.,4.
5.,NaN,10.0
@@ -3154,6 +3153,39 @@ def test_passing_dtype(self):
self.assertRaises(TypeError, self.read_csv, path, dtype={'A' : 'timedelta64', 'B' : 'float64' },
index_col=0)
+ def test_dtype_and_names_error(self):
+
+ # GH 8833
+ # passing both dtype and names resulting in an error reporting issue
+
+ data = """
+1.0 1
+2.0 2
+3.0 3
+"""
+ # base cases
+ result = self.read_csv(StringIO(data),sep='\s+',header=None)
+ expected = DataFrame([[1.0,1],[2.0,2],[3.0,3]])
+ tm.assert_frame_equal(result, expected)
+
+ result = self.read_csv(StringIO(data),sep='\s+',header=None,names=['a','b'])
+ expected = DataFrame([[1.0,1],[2.0,2],[3.0,3]],columns=['a','b'])
+ tm.assert_frame_equal(result, expected)
+
+ # fallback casting
+ result = self.read_csv(StringIO(data),sep='\s+',header=None,names=['a','b'],dtype={'a' : int})
+ expected = DataFrame([[1,1],[2,2],[3,3]],columns=['a','b'])
+ tm.assert_frame_equal(result, expected)
+
+ data = """
+1.0 1
+nan 2
+3.0 3
+"""
+ # fallback casting, but not castable
+ with tm.assertRaisesRegexp(ValueError, 'cannot safely convert'):
+ self.read_csv(StringIO(data),sep='\s+',header=None,names=['a','b'],dtype={'a' : int})
+
def test_fallback_to_python(self):
# GH 6607
data = 'a b c\n1 2 3'
diff --git a/pandas/parser.pyx b/pandas/parser.pyx
index 5f56bd312b9a3..eb80a51728765 100644
--- a/pandas/parser.pyx
+++ b/pandas/parser.pyx
@@ -1002,8 +1002,12 @@ cdef class TextReader:
else:
col_dtype = np.dtype(col_dtype).str
- return self._convert_with_dtype(col_dtype, i, start, end,
- na_filter, 1, na_hashset, na_flist)
+ col_res, na_count = self._convert_with_dtype(col_dtype, i, start, end,
+ na_filter, 1, na_hashset, na_flist)
+
+ # fallback on the parse (e.g. we requested int dtype, but its actually a float)
+ if col_res is not None:
+ return col_res, na_count
if i in self.noconvert:
return self._string_convert(i, start, end, na_filter, na_hashset)
@@ -1020,6 +1024,25 @@ cdef class TextReader:
if col_res is not None:
break
+ # we had a fallback parse on the dtype, so now try to cast
+ # only allow safe casts, eg. with a nan you cannot safely cast to int
+ if col_res is not None and col_dtype is not None:
+ try:
+ col_res = col_res.astype(col_dtype,casting='safe')
+ except TypeError:
+
+ # float -> int conversions can fail the above
+ # even with no nans
+ col_res_orig = col_res
+ col_res = col_res.astype(col_dtype)
+ if (col_res != col_res_orig).any():
+ raise ValueError("cannot safely convert passed user dtype of "
+ "{col_dtype} for {col_res} dtyped data in "
+ "column {column}".format(col_dtype=col_dtype,
+ col_res=col_res_orig.dtype.name,
+ column=i))
+
+
return col_res, na_count
cdef _convert_with_dtype(self, object dtype, Py_ssize_t i,
@@ -1033,8 +1056,9 @@ cdef class TextReader:
if dtype[1] == 'i' or dtype[1] == 'u':
result, na_count = _try_int64(self.parser, i, start, end,
na_filter, na_hashset)
- if user_dtype and na_count > 0:
- raise Exception('Integer column has NA values')
+ if user_dtype and na_count is not None:
+ if na_count > 0:
+ raise Exception('Integer column has NA values')
if dtype[1:] != 'i8':
result = result.astype(dtype)
| closes #8833
```
In [5]: data = """1.0,a
...: nan,b
...: 3.0,c
...: """
In [8]: read_csv(StringIO(data),sep=',',names=['A','B'])
Out[8]:
A B
0 1 a
1 NaN b
2 3 c
In [9]: read_csv(StringIO(data),sep=',',names=['A','B'],dtype={'A' : int})
ValueError: cannot safely convert passed user dtype of <i8 for float64 dtyped data in column 0
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8834 | 2014-11-16T19:23:30Z | 2014-11-17T12:54:59Z | 2014-11-17T12:54:59Z | 2014-11-17T12:54:59Z |
Fix unrecognized 'Z' UTC designator | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 1e84762b60caa..3aa50ad609064 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -151,9 +151,9 @@ Bug Fixes
- Bug in `pd.infer_freq`/`DataFrame.inferred_freq` that prevented proper sub-daily frequency inference
when the index contained DST days (:issue:`8772`).
- Bug where index name was still used when plotting a series with ``use_index=False`` (:issue:`8558`).
-
- Bugs when trying to stack multiple columns, when some (or all)
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`)
+- Regression in ``Timestamp`` does not parse 'Z' zone designator for UTC (:issue:`8771`)
diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c
index 3f09de851e231..44363fd930510 100644
--- a/pandas/src/datetime/np_datetime_strings.c
+++ b/pandas/src/datetime/np_datetime_strings.c
@@ -363,7 +363,8 @@ convert_datetimestruct_local_to_utc(pandas_datetimestruct *out_dts_utc,
* to be cast to the 'unit' parameter.
*
* 'out' gets filled with the parsed date-time.
- * 'out_local' gets whether returned value contains timezone. 0 for UTC, 1 for local time.
+ * 'out_local' gets set to 1 if the parsed time contains timezone,
+ * to 0 otherwise.
* 'out_tzoffset' gets set to timezone offset by minutes
* if the parsed time was in local time,
* to 0 otherwise. The values 'now' and 'today' don't get counted
@@ -785,11 +786,15 @@ parse_iso_8601_datetime(char *str, int len,
/* UTC specifier */
if (*substr == 'Z') {
- /* "Z" means not local */
+ /* "Z" should be equivalent to tz offset "+00:00" */
if (out_local != NULL) {
- *out_local = 0;
+ *out_local = 1;
}
+ if (out_tzoffset != NULL) {
+ *out_tzoffset = 0;
+ }
+
if (sublen == 1) {
goto finish;
}
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index 9adcbb4ea4a41..6c358bd99e620 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -6,7 +6,7 @@
import datetime
from pandas.core.api import Timestamp, Series
-from pandas.tslib import period_asfreq, period_ordinal
+from pandas.tslib import period_asfreq, period_ordinal, get_timezone
from pandas.tseries.index import date_range
from pandas.tseries.frequencies import get_freq
import pandas.tseries.offsets as offsets
@@ -298,6 +298,9 @@ def test_barely_oob_dts(self):
# One us more than the maximum is an error
self.assertRaises(ValueError, Timestamp, max_ts_us + one_us)
+ def test_utc_z_designator(self):
+ self.assertEqual(get_timezone(Timestamp('2014-11-02 01:00Z').tzinfo), 'UTC')
+
class TestDatetimeParsingWrappers(tm.TestCase):
def test_does_not_convert_mixed_integer(self):
| closes #8771
| https://api.github.com/repos/pandas-dev/pandas/pulls/8832 | 2014-11-16T14:29:01Z | 2014-11-27T17:48:12Z | 2014-11-27T17:48:12Z | 2014-11-27T17:48:15Z |
BLD: fix odd usage of cython imports, xref (GH8602) | diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 30c18e2d5d594..3834e4e6b24d9 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -1633,16 +1633,16 @@ class Timedelta(_Timedelta):
if value is None:
if not len(kwargs):
raise ValueError("cannot construct a TimeDelta without a value/unit or descriptive keywords (days,seconds....)")
-
+
def _to_py_int_float(v):
if is_integer_object(v):
return int(v)
elif is_float_object(v):
return float(v)
raise TypeError("Invalid type {0}. Must be int or float.".format(type(v)))
-
+
kwargs = dict([ (k, _to_py_int_float(v)) for k, v in iteritems(kwargs) ])
-
+
try:
value = timedelta(**kwargs)
except TypeError as e:
@@ -2753,8 +2753,7 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None):
result_b.fill(NPY_NAT)
# left side
- idx_shifted = ensure_int64(
- np.maximum(0, trans.searchsorted(vals - DAY_NS, side='right') - 1))
+ idx_shifted = (np.maximum(0, trans.searchsorted(vals - DAY_NS, side='right') - 1)).astype(np.int64)
for i in range(n):
v = vals[i] - deltas[idx_shifted[i]]
@@ -2765,8 +2764,7 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None):
result_a[i] = v
# right side
- idx_shifted = ensure_int64(
- np.maximum(0, trans.searchsorted(vals + DAY_NS, side='right') - 1))
+ idx_shifted = (np.maximum(0, trans.searchsorted(vals + DAY_NS, side='right') - 1)).astype(np.int64)
for i in range(n):
v = vals[i] - deltas[idx_shifted[i]]
@@ -2850,10 +2848,6 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, object ambiguous=None):
return result
-import pandas.algos as algos
-ensure_int64 = algos.ensure_int64
-
-
cdef inline bisect_right_i8(int64_t *data, int64_t val, Py_ssize_t n):
cdef Py_ssize_t pivot, left = 0, right = n
| closes #8602
easy enough, that import should not have been there. we don't import from other pyx files unless its explicity exported (which is in very few cases).
| https://api.github.com/repos/pandas-dev/pandas/pulls/8830 | 2014-11-16T13:40:44Z | 2014-11-17T00:34:05Z | null | 2014-11-17T00:34:05Z |
BUG: query modifies the frame when you compare with `=` | diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py
index b6a1fcbec8339..73ccd11e966d9 100644
--- a/pandas/computation/expr.py
+++ b/pandas/computation/expr.py
@@ -589,6 +589,7 @@ def visitor(x, y):
_python_not_supported = frozenset(['Dict', 'Call', 'BoolOp', 'In', 'NotIn'])
_numexpr_supported_calls = frozenset(_reductions + _mathops)
+_query_not_supported = frozenset(['Assign'])
@disallow((_unsupported_nodes | _python_not_supported) -
@@ -602,6 +603,17 @@ def __init__(self, env, engine, parser,
super(PandasExprVisitor, self).__init__(env, engine, parser, preparser)
+@disallow((_unsupported_nodes | _python_not_supported | _query_not_supported) -
+ (_boolop_nodes | frozenset(['BoolOp', 'Attribute', 'In', 'NotIn',
+ 'Tuple'])))
+class PandasQueryExprVisitor(BaseExprVisitor):
+
+ def __init__(self, env, engine, parser,
+ preparser=partial(_preparse, f=compose(_replace_locals,
+ _replace_booleans))):
+ super(PandasQueryExprVisitor, self).__init__(env, engine, parser, preparser)
+
+
@disallow(_unsupported_nodes | _python_not_supported | frozenset(['Not']))
class PythonExprVisitor(BaseExprVisitor):
@@ -659,4 +671,5 @@ def names(self):
return frozenset(term.name for term in com.flatten(self.terms))
-_parsers = {'python': PythonExprVisitor, 'pandas': PandasExprVisitor}
+_parsers = {'python': PythonExprVisitor, 'pandas': PandasExprVisitor,
+ 'pandas_query': PandasQueryExprVisitor}
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index bb192aeca5b6d..163a78d0a28a8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1935,6 +1935,7 @@ def query(self, expr, **kwargs):
>>> df[df.a > df.b] # same result as the previous expression
"""
kwargs['level'] = kwargs.pop('level', 0) + 1
+ kwargs['parser'] = kwargs.pop('parser', 'pandas_query')
res = self.eval(expr, **kwargs)
try:
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index cfa0ed1a11772..eda6d833f2b64 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -15210,7 +15210,7 @@ class TestDataFrameQueryPythonPandas(TestDataFrameQueryNumExprPandas):
def setUpClass(cls):
super(TestDataFrameQueryPythonPandas, cls).setUpClass()
cls.engine = 'python'
- cls.parser = 'pandas'
+ cls.parser = 'pandas_query'
cls.frame = _frame.copy()
def test_query_builtin(self):
@@ -15225,6 +15225,16 @@ def test_query_builtin(self):
result = df.query('sin > 5', engine=engine, parser=parser)
tm.assert_frame_equal(expected, result)
+ def test_query_with_assign_statement(self):
+ df = DataFrame({'a': [1, 2, 3], 'b': ['a', 'b', 'c']})
+ a_before = df['a'].copy()
+ self.assertRaisesRegexp(
+ NotImplementedError, "'Assign' nodes are not implemented",
+ df.query, 'a=1', engine=self.engine, parser=self.parser
+ )
+ a_after = df['a'].copy()
+ assert_series_equal(a_before, a_after)
+
class TestDataFrameQueryPythonPython(TestDataFrameQueryNumExprPython):
| Fix for #8664. The simplest way to fix this involved adding an `assignment_allowed=True` arg to the internal `eval()` function. All existing behaviour should be preserved. If adding the arg isn't okay I'm not quite sure how else to do it, as it's only once we reach the internal eval function that the expression is actually parsed and we know that it includes the assignment.
It also seems like a pretty bad side effect if query can silently overwrite values (obviously only if you accidentally include an assignment), so hopefully that's a good argument in favour of having the
`assignment_allowed` arg.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8828 | 2014-11-16T05:01:57Z | 2015-12-09T15:18:25Z | null | 2022-10-13T00:16:15Z |
BUG: sql_schema does not generate dialect-specific DDL | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 83e465df7b891..6adb91b088fe6 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -63,7 +63,7 @@ Bug Fixes
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
- ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`).
-
+- ``sql_schema`` now generates dialect appropriate ``CREATE TABLE`` statements (:issue:`8697`)
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 0ae82bec38e26..9baae0330926d 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -621,7 +621,7 @@ def exists(self):
def sql_schema(self):
from sqlalchemy.schema import CreateTable
- return str(CreateTable(self.table))
+ return str(CreateTable(self.table).compile(self.pd_sql.engine))
def _execute_create(self):
# Inserting table into database, add to MetaData object
diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index 74f1602a0f603..2a7aec30e7c50 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -1155,6 +1155,19 @@ def test_to_sql_save_index(self):
def test_transactions(self):
self._transaction_test()
+ def test_get_schema_create_table(self):
+ self._load_test2_data()
+ tbl = 'test_get_schema_create_table'
+ create_sql = sql.get_schema(self.test_frame2, tbl, con=self.conn)
+ blank_test_df = self.test_frame2.iloc[:0]
+
+ self.drop_table(tbl)
+ self.conn.execute(create_sql)
+ returned_df = sql.read_sql_table(tbl, self.conn)
+ tm.assert_frame_equal(returned_df, blank_test_df)
+ self.drop_table(tbl)
+
+
class TestSQLiteAlchemy(_TestSQLAlchemy):
"""
Test the sqlalchemy backend against an in-memory sqlite database.
| Closes #8697 .
sqlalchemy `CreateTable` needs to be called slightly differently to create dialect-specific SQL. See http://stackoverflow.com/questions/2128717/sqlalchemy-printing-raw-sql-from-create .
| https://api.github.com/repos/pandas-dev/pandas/pulls/8827 | 2014-11-15T22:51:12Z | 2014-11-17T08:31:13Z | 2014-11-17T08:31:12Z | 2014-12-05T09:07:50Z |
ENH: Allow get_dummies to return SparseDataFrame | diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt
index b80e341d4156a..70fd6979ba5f9 100644
--- a/doc/source/whatsnew/v0.16.1.txt
+++ b/doc/source/whatsnew/v0.16.1.txt
@@ -48,6 +48,7 @@ Enhancements
df.drop(['A', 'X'], axis=1, errors='ignore')
- Allow conversion of values with dtype ``datetime64`` or ``timedelta64`` to strings using ``astype(str)`` (:issue:`9757`)
+- ``get_dummies`` function now accepts ``sparse`` keyword. If set to ``True``, the return DataFrame is sparse. (:issue:`8823`)
.. _whatsnew_0161.api:
diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py
index 291a73778197a..af98e533cb5b7 100644
--- a/pandas/core/reshape.py
+++ b/pandas/core/reshape.py
@@ -9,6 +9,10 @@
from pandas.core.series import Series
from pandas.core.frame import DataFrame
+from pandas.core.sparse import SparseDataFrame, SparseSeries
+from pandas.sparse.array import SparseArray
+from pandas._sparse import IntIndex
+
from pandas.core.categorical import Categorical
from pandas.core.common import (notnull, _ensure_platform_int, _maybe_promote,
isnull)
@@ -932,7 +936,7 @@ def melt_stub(df, stub, i, j):
return newdf.set_index([i, j])
def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
- columns=None):
+ columns=None, sparse=False):
"""
Convert categorical variable into dummy/indicator variables
@@ -953,6 +957,8 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
Column names in the DataFrame to be encoded.
If `columns` is None then all the columns with
`object` or `category` dtype will be converted.
+ sparse : bool, default False
+ Whether the returned DataFrame should be sparse or not.
Returns
-------
@@ -1039,16 +1045,17 @@ def check_len(item, name):
with_dummies = [result]
for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):
- dummy = _get_dummies_1d(data[col], prefix=pre,
- prefix_sep=sep, dummy_na=dummy_na)
+ dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep,
+ dummy_na=dummy_na, sparse=sparse)
with_dummies.append(dummy)
result = concat(with_dummies, axis=1)
else:
- result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na)
+ result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,
+ sparse=sparse)
return result
-def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False):
+def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False, sparse=False):
# Series avoids inconsistent NaN handling
cat = Categorical.from_array(Series(data), ordered=True)
levels = cat.categories
@@ -1059,19 +1066,17 @@ def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False):
index = data.index
else:
index = np.arange(len(data))
- return DataFrame(index=index)
-
- number_of_cols = len(levels)
- if dummy_na:
- number_of_cols += 1
-
- dummy_mat = np.eye(number_of_cols).take(cat.codes, axis=0)
+ if not sparse:
+ return DataFrame(index=index)
+ else:
+ return SparseDataFrame(index=index)
+ codes = cat.codes.copy()
if dummy_na:
+ codes[codes == -1] = len(cat.categories)
levels = np.append(cat.categories, np.nan)
- else:
- # reset NaN GH4446
- dummy_mat[cat.codes == -1] = 0
+
+ number_of_cols = len(levels)
if prefix is not None:
dummy_cols = ['%s%s%s' % (prefix, prefix_sep, v)
@@ -1084,7 +1089,31 @@ def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False):
else:
index = None
- return DataFrame(dummy_mat, index=index, columns=dummy_cols)
+ if sparse:
+ sparse_series = {}
+ N = len(data)
+ sp_indices = [ [] for _ in range(len(dummy_cols)) ]
+ for ndx, code in enumerate(codes):
+ if code == -1:
+ # Blank entries if not dummy_na and code == -1, #GH4446
+ continue
+ sp_indices[code].append(ndx)
+
+ for col, ixs in zip(dummy_cols, sp_indices):
+ sarr = SparseArray(np.ones(len(ixs)), sparse_index=IntIndex(N, ixs),
+ fill_value=0)
+ sparse_series[col] = SparseSeries(data=sarr, index=index)
+
+ return SparseDataFrame(sparse_series, index=index, columns=dummy_cols)
+
+ else:
+ dummy_mat = np.eye(number_of_cols).take(codes, axis=0)
+
+ if not dummy_na:
+ # reset NaN GH4446
+ dummy_mat[codes == -1] = 0
+
+ return DataFrame(dummy_mat, index=index, columns=dummy_cols)
def make_axis_dummies(frame, axis='minor', transform=None):
diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py
index 66f5110830c72..346c9e2598985 100644
--- a/pandas/tests/test_reshape.py
+++ b/pandas/tests/test_reshape.py
@@ -151,6 +151,8 @@ def test_multiindex(self):
class TestGetDummies(tm.TestCase):
+ sparse = False
+
def setUp(self):
self.df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
@@ -163,20 +165,20 @@ def test_basic(self):
expected = DataFrame({'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0},
'c': {0: 0.0, 1: 0.0, 2: 1.0}})
- assert_frame_equal(get_dummies(s_list), expected)
- assert_frame_equal(get_dummies(s_series), expected)
+ assert_frame_equal(get_dummies(s_list, sparse=self.sparse), expected)
+ assert_frame_equal(get_dummies(s_series, sparse=self.sparse), expected)
expected.index = list('ABC')
- assert_frame_equal(get_dummies(s_series_index), expected)
+ assert_frame_equal(get_dummies(s_series_index, sparse=self.sparse), expected)
def test_just_na(self):
just_na_list = [np.nan]
just_na_series = Series(just_na_list)
just_na_series_index = Series(just_na_list, index = ['A'])
- res_list = get_dummies(just_na_list)
- res_series = get_dummies(just_na_series)
- res_series_index = get_dummies(just_na_series_index)
+ res_list = get_dummies(just_na_list, sparse=self.sparse)
+ res_series = get_dummies(just_na_series, sparse=self.sparse)
+ res_series_index = get_dummies(just_na_series_index, sparse=self.sparse)
self.assertEqual(res_list.empty, True)
self.assertEqual(res_series.empty, True)
@@ -188,12 +190,13 @@ def test_just_na(self):
def test_include_na(self):
s = ['a', 'b', np.nan]
- res = get_dummies(s)
+ res = get_dummies(s, sparse=self.sparse)
exp = DataFrame({'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0}})
assert_frame_equal(res, exp)
- res_na = get_dummies(s, dummy_na=True)
+ # Sparse dataframes do not allow nan labelled columns, see #GH8822
+ res_na = get_dummies(s, dummy_na=True, sparse=self.sparse)
exp_na = DataFrame({nan: {0: 0.0, 1: 0.0, 2: 1.0},
'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0}}).reindex_axis(['a', 'b', nan], 1)
@@ -201,7 +204,7 @@ def test_include_na(self):
exp_na.columns = res_na.columns
assert_frame_equal(res_na, exp_na)
- res_just_na = get_dummies([nan], dummy_na=True)
+ res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse)
exp_just_na = DataFrame(Series(1.0,index=[0]),columns=[nan])
assert_array_equal(res_just_na.values, exp_just_na.values)
@@ -210,21 +213,21 @@ def test_unicode(self): # See GH 6885 - get_dummies chokes on unicode values
e = 'e'
eacute = unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
s = [e, eacute, eacute]
- res = get_dummies(s, prefix='letter')
+ res = get_dummies(s, prefix='letter', sparse=self.sparse)
exp = DataFrame({'letter_e': {0: 1.0, 1: 0.0, 2: 0.0},
u('letter_%s') % eacute: {0: 0.0, 1: 1.0, 2: 1.0}})
assert_frame_equal(res, exp)
def test_dataframe_dummies_all_obj(self):
df = self.df[['A', 'B']]
- result = get_dummies(df)
+ result = get_dummies(df, sparse=self.sparse)
expected = DataFrame({'A_a': [1., 0, 1], 'A_b': [0., 1, 0],
'B_b': [1., 1, 0], 'B_c': [0., 0, 1]})
assert_frame_equal(result, expected)
def test_dataframe_dummies_mix_default(self):
df = self.df
- result = get_dummies(df)
+ result = get_dummies(df, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3], 'A_a': [1., 0, 1],
'A_b': [0., 1, 0], 'B_b': [1., 1, 0],
'B_c': [0., 0, 1]})
@@ -235,7 +238,7 @@ def test_dataframe_dummies_prefix_list(self):
prefixes = ['from_A', 'from_B']
df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
- result = get_dummies(df, prefix=prefixes)
+ result = get_dummies(df, prefix=prefixes, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3], 'from_A_a': [1., 0, 1],
'from_A_b': [0., 1, 0], 'from_B_b': [1., 1, 0],
'from_B_c': [0., 0, 1]})
@@ -243,10 +246,10 @@ def test_dataframe_dummies_prefix_list(self):
'from_B_c']]
assert_frame_equal(result, expected)
- def test_datafrmae_dummies_prefix_str(self):
+ def test_dataframe_dummies_prefix_str(self):
# not that you should do this...
df = self.df
- result = get_dummies(df, prefix='bad')
+ result = get_dummies(df, prefix='bad', sparse=self.sparse)
expected = DataFrame([[1, 1., 0., 1., 0.],
[2, 0., 1., 1., 0.],
[3, 1., 0., 0., 1.]],
@@ -256,40 +259,40 @@ def test_datafrmae_dummies_prefix_str(self):
def test_dataframe_dummies_subset(self):
df = self.df
result = get_dummies(df, prefix=['from_A'],
- columns=['A'])
+ columns=['A'], sparse=self.sparse)
expected = DataFrame({'from_A_a': [1., 0, 1], 'from_A_b': [0., 1, 0],
'B': ['b', 'b', 'c'], 'C': [1, 2, 3]})
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_sep(self):
df = self.df
- result = get_dummies(df, prefix_sep='..')
+ result = get_dummies(df, prefix_sep='..', sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3], 'A..a': [1., 0, 1],
'A..b': [0., 1, 0], 'B..b': [1., 1, 0],
'B..c': [0., 0, 1]})
expected = expected[['C', 'A..a', 'A..b', 'B..b', 'B..c']]
assert_frame_equal(result, expected)
- result = get_dummies(df, prefix_sep=['..', '__'])
+ result = get_dummies(df, prefix_sep=['..', '__'], sparse=self.sparse)
expected = expected.rename(columns={'B..b': 'B__b', 'B..c': 'B__c'})
assert_frame_equal(result, expected)
- result = get_dummies(df, prefix_sep={'A': '..', 'B': '__'})
+ result = get_dummies(df, prefix_sep={'A': '..', 'B': '__'}, sparse=self.sparse)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_bad_length(self):
with tm.assertRaises(ValueError):
- get_dummies(self.df, prefix=['too few'])
+ get_dummies(self.df, prefix=['too few'], sparse=self.sparse)
def test_dataframe_dummies_prefix_sep_bad_length(self):
with tm.assertRaises(ValueError):
- get_dummies(self.df, prefix_sep=['bad'])
+ get_dummies(self.df, prefix_sep=['bad'], sparse=self.sparse)
def test_dataframe_dummies_prefix_dict(self):
prefixes = {'A': 'from_A', 'B': 'from_B'}
df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
- result = get_dummies(df, prefix=prefixes)
+ result = get_dummies(df, prefix=prefixes, sparse=self.sparse)
expected = DataFrame({'from_A_a': [1., 0, 1], 'from_A_b': [0., 1, 0],
'from_B_b': [1., 1, 0], 'from_B_c': [0., 0, 1],
'C': [1, 2, 3]})
@@ -298,7 +301,7 @@ def test_dataframe_dummies_prefix_dict(self):
def test_dataframe_dummies_with_na(self):
df = self.df
df.loc[3, :] = [np.nan, np.nan, np.nan]
- result = get_dummies(df, dummy_na=True)
+ result = get_dummies(df, dummy_na=True, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3, np.nan], 'A_a': [1., 0, 1, 0],
'A_b': [0., 1, 0, 0], 'A_nan': [0., 0, 0, 1], 'B_b': [1., 1, 0, 0],
'B_c': [0., 0, 1, 0], 'B_nan': [0., 0, 0, 1]})
@@ -306,14 +309,14 @@ def test_dataframe_dummies_with_na(self):
'B_nan']]
assert_frame_equal(result, expected)
- result = get_dummies(df, dummy_na=False)
+ result = get_dummies(df, dummy_na=False, sparse=self.sparse)
expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_with_categorical(self):
df = self.df
df['cat'] = pd.Categorical(['x', 'y', 'y'])
- result = get_dummies(df)
+ result = get_dummies(df, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3], 'A_a': [1., 0, 1],
'A_b': [0., 1, 0], 'B_b': [1., 1, 0],
'B_c': [0., 0, 1], 'cat_x': [1., 0, 0],
@@ -322,6 +325,11 @@ def test_dataframe_dummies_with_categorical(self):
'cat_x', 'cat_y']]
assert_frame_equal(result, expected)
+
+class TestGetDummiesSparse(TestGetDummies):
+ sparse = True
+
+
class TestLreshape(tm.TestCase):
def test_pairs(self):
| For dataframes with a large number of unique values, `get_dummies` can use enormous amounts of memory. This provides a `sparse` flag to `get_dummies` which returns a much more memory-efficient structure.
For example:
```
import pandas as pd
df1 = pd.get_dummies(pd.DataFrame({'a':range(1000)}, dtype='category').a, sparse=False)
print 'dense:', df1.memory_usage().sum()
df2 = pd.get_dummies(pd.DataFrame({'a':range(1000)}, dtype='category').a, sparse=True)
print 'sparse:', df2.memory_usage().sum()
pd.util.testing.assert_frame_equal(df1, df2)
```
returns
```
dense: 8000000
sparse: 8000
```
Performance could probably be improved a lot.
Fails `pandas/tests/test_reshape.py:TestGetDummiesSparse.test_include_na` due to #8822 .
| https://api.github.com/repos/pandas-dev/pandas/pulls/8823 | 2014-11-15T03:13:47Z | 2015-04-13T13:24:45Z | null | 2015-04-13T13:24:45Z |
BUG: Error when creating sparse dataframe with nan column label | diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt
index 9f989b2cf0ea9..3d03a2b5f0c4c 100644
--- a/doc/source/whatsnew/v0.16.1.txt
+++ b/doc/source/whatsnew/v0.16.1.txt
@@ -98,3 +98,6 @@ Bug Fixes
- Bug in ``read_csv`` and ``read_table`` when using ``skip_rows`` parameter if blank lines are present. (:issue:`9832`)
- Bug in ``read_csv()`` interprets ``index_col=True`` as ``1`` (:issue:`9798`)
+
+- Bug in which ``SparseDataFrame`` could not take `nan` as a column name (:issue:`8822`)
+
diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py
index 30b06c8a93142..bc022fcb6542b 100644
--- a/pandas/sparse/frame.py
+++ b/pandas/sparse/frame.py
@@ -100,7 +100,7 @@ def __init__(self, data=None, index=None, columns=None,
mgr = self._init_mgr(
data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy)
elif data is None:
- data = {}
+ data = DataFrame()
if index is None:
index = Index([])
@@ -115,7 +115,7 @@ def __init__(self, data=None, index=None, columns=None,
index=index,
kind=self._default_kind,
fill_value=self._default_fill_value)
- mgr = dict_to_manager(data, columns, index)
+ mgr = df_to_manager(data, columns, index)
if dtype is not None:
mgr = mgr.astype(dtype)
@@ -155,7 +155,7 @@ def _init_dict(self, data, index, columns, dtype=None):
kind=self._default_kind,
fill_value=self._default_fill_value,
copy=True)
- sdict = {}
+ sdict = DataFrame()
for k, v in compat.iteritems(data):
if isinstance(v, Series):
# Force alignment, no copy necessary
@@ -181,7 +181,7 @@ def _init_dict(self, data, index, columns, dtype=None):
if c not in sdict:
sdict[c] = sp_maker(nan_vec)
- return dict_to_manager(sdict, columns, index)
+ return df_to_manager(sdict, columns, index)
def _init_matrix(self, data, index, columns, dtype=None):
data = _prep_ndarray(data, copy=False)
@@ -228,12 +228,12 @@ def _unpickle_sparse_frame_compat(self, state):
else:
index = idx
- series_dict = {}
+ series_dict = DataFrame()
for col, (sp_index, sp_values) in compat.iteritems(series):
series_dict[col] = SparseSeries(sp_values, sparse_index=sp_index,
fill_value=fv)
- self._data = dict_to_manager(series_dict, columns, index)
+ self._data = df_to_manager(series_dict, columns, index)
self._default_fill_value = fv
self._default_kind = kind
@@ -737,13 +737,13 @@ def applymap(self, func):
"""
return self.apply(lambda x: lmap(func, x))
-def dict_to_manager(sdict, columns, index):
- """ create and return the block manager from a dict of series, columns, index """
+def df_to_manager(sdf, columns, index):
+ """ create and return the block manager from a dataframe of series, columns, index """
# from BlockManager perspective
axes = [_ensure_index(columns), _ensure_index(index)]
- return create_block_manager_from_arrays([sdict[c] for c in columns], columns, axes)
+ return create_block_manager_from_arrays([sdf[c] for c in columns], columns, axes)
def stack_sparse_frame(frame):
diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py
index f187e7f883e11..454cbcd5320e9 100644
--- a/pandas/sparse/tests/test_sparse.py
+++ b/pandas/sparse/tests/test_sparse.py
@@ -1663,6 +1663,12 @@ def test_as_blocks(self):
self.assertEqual(list(df_blocks.keys()), ['float64'])
assert_frame_equal(df_blocks['float64'], df)
+ def test_nan_columnname(self):
+ # GH 8822
+ nan_colname = DataFrame(Series(1.0,index=[0]),columns=[nan])
+ nan_colname_sparse = nan_colname.to_sparse()
+ self.assertTrue(np.isnan(nan_colname_sparse.columns[0]))
+
def _dense_series_compare(s, f):
result = f(s)
| Right now the following raises an exception:
```
from pandas import DataFrame, Series
from numpy import nan
nan_colname = DataFrame(Series(1.0,index=[0]),columns=[nan])
nan_colname_sparse = nan_colname.to_sparse()
```
This is because sparse dataframes use a dictionary to store information about columns, with the column label as the key. `nan`'s do not equal themselves and create problems as dictionary keys. This avoids the issue by uses a dataframe to store this information.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8822 | 2014-11-15T01:33:18Z | 2015-04-11T18:35:12Z | 2015-04-11T18:35:12Z | 2015-04-11T18:35:21Z |
StataReader: Support sorting categoricals | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index b1fa6dbed442d..7041352971407 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -27,6 +27,7 @@ API changes
Enhancements
~~~~~~~~~~~~
+- StataReader: Properly support sorting categorical variables read from stata files.
.. _whatsnew_0152.performance:
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index c2542594861c4..36ac705e4e3ed 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -1139,12 +1139,17 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None,
)[0]
for i in cols:
col = data.columns[i]
- labeled_data = np.copy(data[col])
- labeled_data = labeled_data.astype(object)
- for k, v in compat.iteritems(
- self.value_label_dict[self.lbllist[i]]):
- labeled_data[(data[col] == k).values] = v
- data[col] = Categorical.from_array(labeled_data)
+ codes = np.nan_to_num(data[col])
+ codes = codes.astype(int)
+ codes = codes-1
+ categories = []
+ labeldict = self.value_label_dict[self.lbllist[i]]
+ for j in range(max(labeldict.keys())):
+ try:
+ categories.append(labeldict[j+1])
+ except:
+ categories.append(j+1)
+ data[col] = Categorical.from_codes(codes, categories, ordered=True)
if not preserve_dtypes:
retyped_data = []
diff --git a/pandas/io/tests/data/stata10_117.dta b/pandas/io/tests/data/stata10_117.dta
new file mode 100644
index 0000000000000..79dfffd94483f
Binary files /dev/null and b/pandas/io/tests/data/stata10_117.dta differ
diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py
index 2cb7809166be5..10df5cc91558e 100644
--- a/pandas/io/tests/test_stata.py
+++ b/pandas/io/tests/test_stata.py
@@ -13,6 +13,7 @@
import pandas as pd
from pandas.compat import iterkeys
+from pandas.core.categorical import Categorical
from pandas.core.frame import DataFrame, Series
from pandas.io.parsers import read_csv
from pandas.io.stata import (read_stata, StataReader, InvalidColumnName,
@@ -81,6 +82,8 @@ def setUp(self):
self.dta18_115 = os.path.join(self.dirpath, 'stata9_115.dta')
self.dta18_117 = os.path.join(self.dirpath, 'stata9_117.dta')
+ self.dta19_117 = os.path.join(self.dirpath, 'stata10_117.dta')
+
def read_dta(self, file):
# Legacy default reader configuration
@@ -744,6 +747,12 @@ def test_drop_column(self):
columns = ['byte_', 'int_', 'long_', 'not_found']
read_stata(self.dta15_117, convert_dates=True, columns=columns)
+ def test_categorical_sorting(self):
+ dataset = read_stata(self.dta19_117)
+ dataset = dataset.sort("srh")
+ expected = Categorical.from_codes(codes=[-1, -1, 0, 1, 1, 1, 2, 2, 3, 4], categories=["Poor", "Fair", "Good", "Very good", "Excellent"])
+ tm.assert_equal(True, (np.asarray(expected)==np.asarray(dataset["srh"])).all())
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| https://api.github.com/repos/pandas-dev/pandas/pulls/8816 | 2014-11-14T13:07:03Z | 2014-11-18T13:23:22Z | null | 2014-11-18T13:23:22Z | |
VIS: plot with `use_index=False` shouldn't plot the index name | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 83e465df7b891..036efa97c1257 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -93,3 +93,4 @@ Bug Fixes
- Bug in `pd.infer_freq`/`DataFrame.inferred_freq` that prevented proper sub-daily frequency inference
when the index contained DST days (:issue:`8772`).
+- Bug where index name was still used when plotting a series with ``use_index=False`` (:issue:`8558`).
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index 7469abdbd6658..74ec6d22ca4cd 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -569,6 +569,16 @@ def test_line_area_nan_series(self):
ax = _check_plot_works(d.plot, kind='area', stacked=False)
self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
+ def test_line_use_index_false(self):
+ s = Series([1, 2, 3], index=['a', 'b', 'c'])
+ s.index.name = 'The Index'
+ ax = s.plot(use_index=False)
+ label = ax.get_xlabel()
+ self.assertEqual(label, '')
+ ax2 = s.plot(kind='bar', use_index=False)
+ label2 = ax2.get_xlabel()
+ self.assertEqual(label2, '')
+
@slow
def test_bar_log(self):
expected = np.array([1., 10., 100., 1000.])
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 9065e0a340aaa..cb669b75e5c96 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -1685,7 +1685,7 @@ def _post_plot_logic(self):
self.rot = 30
format_date_labels(ax, rot=self.rot)
- if index_name is not None:
+ if index_name is not None and self.use_index:
ax.set_xlabel(index_name)
@@ -1874,7 +1874,7 @@ def _post_plot_logic(self):
ax.set_xticklabels(str_index)
if not self.log: # GH3254+
ax.axhline(0, color='k', linestyle='--')
- if name is not None:
+ if name is not None and self.use_index:
ax.set_xlabel(name)
elif self.kind == 'barh':
# horizontal bars
@@ -1882,7 +1882,7 @@ def _post_plot_logic(self):
ax.set_yticks(self.tick_pos)
ax.set_yticklabels(str_index)
ax.axvline(0, color='k', linestyle='--')
- if name is not None:
+ if name is not None and self.use_index:
ax.set_ylabel(name)
else:
raise NotImplementedError(self.kind)
| Fixes #8558. As far as I can tell the two plot types where this applies are line and bar plots, if there any others they should also be easy to fix. Bar and line plots no longer include the index name:
``` python
import pandas as pd
from pandas import Series
import matplotlib.pyplot as plt
s = Series([1, 2, 3], index=['a', 'b', 'c'])
s.index.name = 'The Index'
ax = s.plot(use_index=False)
plt.show()
```

``` python
ax2 = s.plot(kind='bar', use_index=False)
plt.show()
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8812 | 2014-11-14T03:42:40Z | 2014-11-16T14:09:36Z | 2014-11-16T14:09:36Z | 2014-11-16T14:09:39Z |
DOC: Added get_group() example to grouby.html | diff --git a/doc/README.rst b/doc/README.rst
index 2143fa6c922ca..7f001192f5d73 100644
--- a/doc/README.rst
+++ b/doc/README.rst
@@ -132,7 +132,7 @@ If you want to do a full clean build, do::
python make.py build
-Staring with 0.13.1 you can tell ``make.py`` to compile only a single section
+Starting with 0.13.1 you can tell ``make.py`` to compile only a single section
of the docs, greatly reducing the turn-around time for checking your changes.
You will be prompted to delete `.rst` files that aren't required, since the
last committed version can always be restored from git.
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index 1b21c5d7291e5..db19e0de3d788 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -338,6 +338,21 @@ In the case of grouping by multiple keys, the group name will be a tuple:
It's standard Python-fu but remember you can unpack the tuple in the for loop
statement if you wish: ``for (k1, k2), group in grouped:``.
+Selecting a group
+-----------------
+
+A single group can be selected using ``GroupBy.get_group()``:
+
+.. ipython:: python
+
+ grouped.get_group('bar')
+
+Or for an object grouped on multiple columns:
+
+.. ipython:: python
+
+ df.groupby(['A', 'B']).get_group(('bar', 'one'))
+
.. _groupby.aggregate:
Aggregation
| Added a get_group example in the Iterating through groups section of grouby.html to demonstrate selecting
single group from a DataFrameGrouBy object
Fixed typo in README
| https://api.github.com/repos/pandas-dev/pandas/pulls/8811 | 2014-11-14T01:36:36Z | 2014-11-15T09:55:31Z | 2014-11-15T09:55:31Z | 2014-11-15T09:55:31Z |
TST: Skip hist testing for matplotlib <= 1.2 | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index fe1454d4d2582..da5aad42b51e4 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -63,3 +63,4 @@ Bug Fixes
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
- ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`).
+- Skip testing of histogram plots for matplotlib <= 1.2 (:issue:`8648`).
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index a5d203d688b16..7469abdbd6658 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -2041,6 +2041,9 @@ def test_kde_missing_vals(self):
@slow
def test_hist_df(self):
+ if self.mpl_le_1_2_1:
+ raise nose.SkipTest("not supported in matplotlib <= 1.2.x")
+
df = DataFrame(randn(100, 4))
series = df[0]
| Not a huge issue, just skipping an incompatible test when testing with matplotlib 1.2.
closes #8648.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8810 | 2014-11-14T01:16:21Z | 2014-11-14T12:45:36Z | 2014-11-14T12:45:36Z | 2014-11-14T12:45:52Z |
BUG: Passing multiple levels to stack when having mixed integer/string level names (#8584) | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 3f72d5d44f870..9659f670280ad 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -98,3 +98,6 @@ Bug Fixes
- Bug in `pd.infer_freq`/`DataFrame.inferred_freq` that prevented proper sub-daily frequency inference
when the index contained DST days (:issue:`8772`).
- Bug where index name was still used when plotting a series with ``use_index=False`` (:issue:`8558`).
+
+- Bugs when trying to stack multiple columns, when some (or all)
+ of the level names are numbers (:issue:`8584`).
diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py
index 5cbf392f246ed..d576f788a831f 100644
--- a/pandas/core/reshape.py
+++ b/pandas/core/reshape.py
@@ -525,10 +525,10 @@ def stack(frame, level=-1, dropna=True):
raise ValueError(msg)
# Will also convert negative level numbers and check if out of bounds.
- level = frame.columns._get_level_number(level)
+ level_num = frame.columns._get_level_number(level)
if isinstance(frame.columns, MultiIndex):
- return _stack_multi_columns(frame, level=level, dropna=dropna)
+ return _stack_multi_columns(frame, level_num=level_num, dropna=dropna)
elif isinstance(frame.index, MultiIndex):
new_levels = list(frame.index.levels)
new_levels.append(frame.columns)
@@ -595,19 +595,43 @@ def stack_multiple(frame, level, dropna=True):
return result
-def _stack_multi_columns(frame, level=-1, dropna=True):
+def _stack_multi_columns(frame, level_num=-1, dropna=True):
+ def _convert_level_number(level_num, columns):
+ """
+ Logic for converting the level number to something
+ we can safely pass to swaplevel:
+
+ We generally want to convert the level number into
+ a level name, except when columns do not have names,
+ in which case we must leave as a level number
+ """
+ if level_num in columns.names:
+ return columns.names[level_num]
+ else:
+ if columns.names[level_num] is None:
+ return level_num
+ else:
+ return columns.names[level_num]
+
this = frame.copy()
# this makes life much simpler
- if level != frame.columns.nlevels - 1:
+ if level_num != frame.columns.nlevels - 1:
# roll levels to put selected level at end
roll_columns = this.columns
- for i in range(level, frame.columns.nlevels - 1):
- roll_columns = roll_columns.swaplevel(i, i + 1)
+ for i in range(level_num, frame.columns.nlevels - 1):
+ # Need to check if the ints conflict with level names
+ lev1 = _convert_level_number(i, roll_columns)
+ lev2 = _convert_level_number(i + 1, roll_columns)
+ roll_columns = roll_columns.swaplevel(lev1, lev2)
this.columns = roll_columns
if not this.columns.is_lexsorted():
- this = this.sortlevel(0, axis=1)
+ # Workaround the edge case where 0 is one of the column names,
+ # which interferes with trying to sort based on the first
+ # level
+ level_to_sort = _convert_level_number(0, this.columns)
+ this = this.sortlevel(level_to_sort, axis=1)
# tuple list excluding level for grouping columns
if len(frame.columns.levels) > 2:
@@ -660,9 +684,9 @@ def _stack_multi_columns(frame, level=-1, dropna=True):
new_labels = [np.arange(N).repeat(levsize)]
new_names = [this.index.name] # something better?
- new_levels.append(frame.columns.levels[level])
+ new_levels.append(frame.columns.levels[level_num])
new_labels.append(np.tile(np.arange(levsize), N))
- new_names.append(frame.columns.names[level])
+ new_names.append(frame.columns.names[level_num])
new_index = MultiIndex(levels=new_levels, labels=new_labels,
names=new_names, verify_integrity=False)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 312a5df475d6e..fc031afe728dc 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -12110,6 +12110,70 @@ def test_stack_ints(self):
df_named.stack(level=1).stack(level=1)
)
+ def test_stack_mixed_levels(self):
+ columns = MultiIndex.from_tuples(
+ [('A', 'cat', 'long'), ('B', 'cat', 'long'),
+ ('A', 'dog', 'short'), ('B', 'dog', 'short')],
+ names=['exp', 'animal', 'hair_length']
+ )
+ df = DataFrame(randn(4, 4), columns=columns)
+
+ animal_hair_stacked = df.stack(level=['animal', 'hair_length'])
+ exp_hair_stacked = df.stack(level=['exp', 'hair_length'])
+
+ # GH #8584: Need to check that stacking works when a number
+ # is passed that is both a level name and in the range of
+ # the level numbers
+ df2 = df.copy()
+ df2.columns.names = ['exp', 'animal', 1]
+ assert_frame_equal(df2.stack(level=['animal', 1]),
+ animal_hair_stacked, check_names=False)
+ assert_frame_equal(df2.stack(level=['exp', 1]),
+ exp_hair_stacked, check_names=False)
+
+ # When mixed types are passed and the ints are not level
+ # names, raise
+ self.assertRaises(ValueError, df2.stack, level=['animal', 0])
+
+ # GH #8584: Having 0 in the level names could raise a
+ # strange error about lexsort depth
+ df3 = df.copy()
+ df3.columns.names = ['exp', 'animal', 0]
+ assert_frame_equal(df3.stack(level=['animal', 0]),
+ animal_hair_stacked, check_names=False)
+
+ def test_stack_int_level_names(self):
+ columns = MultiIndex.from_tuples(
+ [('A', 'cat', 'long'), ('B', 'cat', 'long'),
+ ('A', 'dog', 'short'), ('B', 'dog', 'short')],
+ names=['exp', 'animal', 'hair_length']
+ )
+ df = DataFrame(randn(4, 4), columns=columns)
+
+ exp_animal_stacked = df.stack(level=['exp', 'animal'])
+ animal_hair_stacked = df.stack(level=['animal', 'hair_length'])
+ exp_hair_stacked = df.stack(level=['exp', 'hair_length'])
+
+ df2 = df.copy()
+ df2.columns.names = [0, 1, 2]
+ assert_frame_equal(df2.stack(level=[1, 2]), animal_hair_stacked,
+ check_names=False )
+ assert_frame_equal(df2.stack(level=[0, 1]), exp_animal_stacked,
+ check_names=False)
+ assert_frame_equal(df2.stack(level=[0, 2]), exp_hair_stacked,
+ check_names=False)
+
+ # Out-of-order int column names
+ df3 = df.copy()
+ df3.columns.names = [2, 0, 1]
+ assert_frame_equal(df3.stack(level=[0, 1]), animal_hair_stacked,
+ check_names=False)
+ assert_frame_equal(df3.stack(level=[2, 0]), exp_animal_stacked,
+ check_names=False)
+ assert_frame_equal(df3.stack(level=[2, 1]), exp_hair_stacked,
+ check_names=False)
+
+
def test_unstack_bool(self):
df = DataFrame([False, False],
index=MultiIndex.from_arrays([['a', 'b'], ['c', 'l']]),
| closes #8584
Stacking should now work as expected for the cases described in the bug report (#8584). So we can pass a list of both ints and strs to `DataFrame.stack`, provided the ints are level names. Mixed ints and strs when the ints aren't level names raises `ValueError`, but that's unchanged.
I've added a new hidden method `_swaplevel_assume_numbers` since the main cause of the bug was
ambiguity between ints as names and ints as level numbers, and this allows us to make sure the
swap is done correctly once we reach that point.
Another alternative would have been to add an `as_numbers=True` flag to the existing `swaplevel` method. We could keep existing behaviour intact by having the default be `as_numbers=False`. If that
seems like the better option now that you've seen the PR, let me know and it should be pretty easy
to switch.
``` python
import pandas as pd
from pandas import DataFrame, MultiIndex
from numpy.random import randn
columns = MultiIndex.from_tuples([('A', 'cat', 'long'), ('B', 'cat', 'long'), ('A', 'dog', 'short'), ('B', 'dog', 'short')],
names=['exp', 'animal', 'hair_length'])
df = DataFrame(randn(4, 4), columns=columns)
df.columns.names = ['exp', 'animal', 1]
print(df.stack(level=['animal', 1]))
# exp A B
# animal 1
#0 cat long 0.758850 0.327957
# dog short 0.429752 0.236901
#1 cat long 0.572633 1.057861
# dog short 0.802539 1.321191
#2 cat long 0.283352 0.088000
# dog short -0.814458 -1.688529
#3 cat long 2.009912 -1.738292
# dog short 1.272569 -0.133434
df.columns.names = ['exp', 'animal', 0]
print(df.stack(level=['animal', 0]))
# exp A B
# animal 0
#0 cat long 0.758850 0.327957
# dog short 0.429752 0.236901
#1 cat long 0.572633 1.057861
# dog short 0.802539 1.321191
#2 cat long 0.283352 0.088000
# dog short -0.814458 -1.688529
#3 cat long 2.009912 -1.738292
# dog short 1.272569 -0.133434
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8809 | 2014-11-14T00:18:43Z | 2014-11-17T15:24:10Z | 2014-11-17T15:24:10Z | 2014-11-17T15:25:57Z |
GH3508 added basic documentation of google analytics | diff --git a/doc/source/io.rst b/doc/source/io.rst
index 066a9af472c24..58eb5563a0823 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -39,6 +39,7 @@ object.
* :ref:`read_json<io.json_reader>`
* :ref:`read_msgpack<io.msgpack>` (experimental)
* :ref:`read_html<io.read_html>`
+ * :ref:`read_ga<io.analytics>`
* :ref:`read_gbq<io.bigquery>` (experimental)
* :ref:`read_stata<io.stata_reader>`
* :ref:`read_clipboard<io.clipboard>`
@@ -3496,6 +3497,65 @@ And then issue the following queries:
pd.read_sql_query("SELECT * FROM data", con)
+.. _io.analytics:
+
+Google Analytics
+----------------
+
+The :mod:`~pandas.io.ga` module provides a wrapper for
+`Google Analytics API <https://developers.google.com/analytics/devguides>`__
+to simplify retrieving traffic data.
+Result sets are parsed into a pandas DataFrame with a shape and data types
+derived from the source table.
+
+Configuring Access to Google Analytics
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The first thing you need to do is to setup accesses to Google Analytics API. Follow the steps below:
+
+#. In the `Google Developers Console <https://console.developers.google.com>`__
+ #. enable the Analytics API
+ #. create a new project
+ #. create a new Client ID for an "Installed Application" (in the "APIs & auth / Credentials section" of the newly created project)
+ #. download it (JSON file)
+#. On your machine
+ #. rename it to ``client_secrets.json``
+ #. move it to the ``pandas/io`` module directory
+
+The first time you use the :func:`read_ga` funtion, a browser window will open to ask you to authentify to the Google API. Do proceed.
+
+Using the Google Analytics API
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following will fetch users and pageviews (metrics) data per day of the week, for the first semester of 2014, from a particular property.
+
+.. code-block:: python
+
+ import pandas.io.ga as ga
+ ga.read_ga(
+ account_id = "2360420",
+ profile_id = "19462946",
+ property_id = "UA-2360420-5",
+ metrics = ['users', 'pageviews'],
+ dimensions = ['dayOfWeek'],
+ start_date = "2014-01-01",
+ end_date = "2014-08-01",
+ index_col = 0,
+ filters = "pagePath=~aboutus;ga:country==France",
+ )
+
+The only mandatory arguments are ``metrics,`` ``dimensions`` and ``start_date``. We can only strongly recommend you to always specify the ``account_id``, ``profile_id`` and ``property_id`` to avoid accessing the wrong data bucket in Google Analytics.
+
+The ``index_col`` argument indicates which dimension(s) has to be taken as index.
+
+The ``filters`` argument indicates the filtering to apply to the query. In the above example, the page has URL has to contain ``aboutus`` AND the visitors country has to be France.
+
+Detailed informations in the followings:
+
+* `pandas & google analytics, by yhat <http://blog.yhathq.com/posts/pandas-google-analytics.html>`__
+* `Google Analytics integration in pandas, by Chang She <http://quantabee.wordpress.com/2012/12/17/google-analytics-pandas/>`__
+* `Google Analytics Dimensions and Metrics Reference <https://developers.google.com/analytics/devguides/reporting/core/dimsmets>`_
+
.. _io.bigquery:
Google BigQuery (Experimental)
| closes #3508
| https://api.github.com/repos/pandas-dev/pandas/pulls/8807 | 2014-11-13T21:04:40Z | 2014-11-17T00:36:48Z | null | 2014-11-17T00:36:48Z |
[ENH] Add ability to enforce specific filter on Google Analytics query | diff --git a/pandas/io/ga.py b/pandas/io/ga.py
index f002994888932..782ce3932179d 100644
--- a/pandas/io/ga.py
+++ b/pandas/io/ga.py
@@ -20,6 +20,7 @@
from oauth2client.client import AccessTokenRefreshError
from pandas.compat import zip, u
+
TYPE_MAP = {u('INTEGER'): int, u('FLOAT'): float, u('TIME'): int}
NO_CALLBACK = auth.OOB_CALLBACK_URN
@@ -251,7 +252,7 @@ def get_data(self, metrics, start_date, end_date=None,
converters=None, sort=True, dayfirst=False,
account_name=None, account_id=None, property_name=None,
property_id=None, profile_name=None, profile_id=None,
- chunksize=None):
+ chunksize=None, raw_filters=False):
if chunksize is None and max_results > 10000:
raise ValueError('Google API returns maximum of 10,000 rows, '
'please set chunksize')
@@ -274,8 +275,7 @@ def _read(start, result_size):
end_date=end_date, dimensions=dimensions,
segment=segment, filters=filters,
start_index=start,
- max_results=result_size)
-
+ max_results=result_size, raw_filters=raw_filters)
try:
rs = query.execute()
rows = rs.get('rows', [])
@@ -332,6 +332,7 @@ def create_query(self, profile_id, metrics, start_date, end_date=None,
dimensions=dimensions, segment=segment,
filters=filters, start_index=start_index,
max_results=max_results, **kwargs)
+ print("QUERY\n{}".format(qry))
try:
return self.service.data().ga().get(**qry)
except TypeError as error:
@@ -376,8 +377,11 @@ def format_query(ids, metrics, start_date, end_date=None, dimensions=None,
if max_results is not None:
qry['max_results'] = str(max_results)
- return qry
+ if kwargs['raw_filters'] == True:
+ qry['filters'] = filters
+ del qry['raw_filters']
+ return qry
def _maybe_add_arg(query, field, data, prefix='ga'):
if data is not None:
| Fix for #3505
This PR allows to directly write filters to ga.py `read_ga` function.
Currently only **OR** filters are supported. with this PR we can support directly writing the filters as a string, allowing for example **AND** filtering.
**Example here**
| https://api.github.com/repos/pandas-dev/pandas/pulls/8806 | 2014-11-13T20:37:20Z | 2014-11-14T15:36:26Z | null | 2014-11-14T15:36:40Z |
Removing typo as Renaming | diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index 1b5b4746336ad..0a12ed1bf03d1 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -236,7 +236,7 @@ which are removed are replaced by ``np.nan``.:
s = s.cat.remove_categories([4])
s
-Renaming unused categories
+Removing unused categories
~~~~~~~~~~~~~~~~~~~~~~~~~~
Removing unused categories can also be done:
| Should be 'Removing', I think, if I'm not misunderstanding.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8800 | 2014-11-13T05:46:38Z | 2014-11-13T08:48:24Z | 2014-11-13T08:48:24Z | 2014-11-13T08:48:24Z |
ENH: serialization of categorical to HDF5 (GH7621) | diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index 0a12ed1bf03d1..3d7b41a1c4c24 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -541,8 +541,12 @@ The same applies to ``df.append(df_different)``.
Getting Data In/Out
-------------------
-Writing data (`Series`, `Frames`) to a HDF store that contains a ``category`` dtype will currently
-raise ``NotImplementedError``.
+.. versionadded:: 0.15.2
+
+Writing data (`Series`, `Frames`) to a HDF store that contains a ``category`` dtype was implemented
+in 0.15.2. See :ref:`here <io.hdf5-categorical>` for an example and caveats.
+
+Writing data to/from Stata format files was implemented in 0.15.2.
Writing to a CSV file will convert the data, effectively removing any information about the
categorical (categories and ordering). So if you read back the CSV file you have to convert the
@@ -805,4 +809,3 @@ Use ``copy=True`` to prevent such a behaviour or simply don't reuse `Categorical
This also happens in some cases when you supply a `numpy` array instead of a `Categorical`:
using an int array (e.g. ``np.array([1,2,3,4])``) will exhibit the same behaviour, while using
a string array (e.g. ``np.array(["a","b","c","a"])``) will not.
-
diff --git a/doc/source/io.rst b/doc/source/io.rst
index 1d83e06a13567..bbc812e0bf86e 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -3070,6 +3070,50 @@ conversion may not be necessary in future versions of pandas)
df
df.dtypes
+.. _io.hdf5-categorical:
+
+Categorical Data
+~~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.15.2
+
+Writing data (`Series`, `Frames`) to a HDF store that contains a ``category`` dtype was implemented
+in 0.15.2. Queries work the same as if it was an object array (but the ``Categorical`` is stored in a more efficient manner)
+
+.. ipython:: python
+
+ dfcat = DataFrame({ 'A' : Series(list('aabbcdba')).astype('category'),
+ 'B' : np.random.randn(8) })
+ cstore = pd.HDFStore('cats.h5', mode='w')
+ cstore.append('dfcat', dfcat, format='table', data_columns=['A'])
+ result = cstore.select('dfcat', where="A in ['b','c']")
+ result
+ result.dtypes
+
+.. warning::
+
+ The format of the ``Categoricals` is readable by prior versions of pandas (< 0.15.2), but will retrieve
+ the data as an integer based column (e.g. the ``codes``). However, the ``categories`` *can* be retrieved
+ but require the user to select them manually using the explicit meta path.
+
+ The data is stored like so:
+
+ .. ipython:: python
+
+ cstore
+
+ # to get the categories
+ cstore.select('dfcat/meta/A/meta')
+
+.. ipython:: python
+ :suppress:
+ :okexcept:
+
+ cstore.close()
+ import os
+ os.remove('cats.h5')
+
+
String Columns
~~~~~~~~~~~~~~
@@ -3639,6 +3683,8 @@ outside of this range, the data is cast to ``int16``.
data frames containing categorical data will convert non-string categorical values
to strings.
+Writing data to/from Stata format files with a ``category`` dtype was implemented in 0.15.2.
+
.. _io.stata_reader:
Reading from STATA format
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 83e465df7b891..2586d1920b2e6 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -42,6 +42,7 @@ Enhancements
~~~~~~~~~~~~
- Added ability to export Categorical data to Stata (:issue:`8633`).
+- Added ability to export Categorical data to to/from HDF5 (:issue:`7621`). Queries work the same as if it was an object array (but a ``Categorical`` is stored in a much more efficient manner). See :ref:`here <io.hdf5-categorical>` for an example and caveats w.r.t. prior versions of pandas.
.. _whatsnew_0152.performance:
diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py
index 25d6a7f293dac..4290be3e1abba 100644
--- a/pandas/computation/pytables.py
+++ b/pandas/computation/pytables.py
@@ -147,7 +147,17 @@ def is_in_table(self):
@property
def kind(self):
""" the kind of my field """
- return self.queryables.get(self.lhs)
+ return getattr(self.queryables.get(self.lhs),'kind',None)
+
+ @property
+ def meta(self):
+ """ the meta of my field """
+ return getattr(self.queryables.get(self.lhs),'meta',None)
+
+ @property
+ def metadata(self):
+ """ the metadata of my field """
+ return getattr(self.queryables.get(self.lhs),'metadata',None)
def generate(self, v):
""" create and return the op string for this TermValue """
@@ -167,6 +177,7 @@ def stringify(value):
return encoder(value)
kind = _ensure_decoded(self.kind)
+ meta = _ensure_decoded(self.meta)
if kind == u('datetime64') or kind == u('datetime'):
if isinstance(v, (int, float)):
v = stringify(v)
@@ -182,6 +193,10 @@ def stringify(value):
elif kind == u('timedelta64') or kind == u('timedelta'):
v = _coerce_scalar_to_timedelta_type(v, unit='s').value
return TermValue(int(v), v, kind)
+ elif meta == u('category'):
+ metadata = com._values_from_object(self.metadata)
+ result = metadata.searchsorted(v,side='left')
+ return TermValue(result, result, u('integer'))
elif kind == u('integer'):
v = int(float(v))
return TermValue(v, v, kind)
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index 414c4a8315e6d..eb0429ad4a0cd 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -319,6 +319,15 @@ def ndim(self):
"""Number of dimensions of the Categorical """
return self._codes.ndim
+ def reshape(self, new_shape, **kwargs):
+ """ compat with .reshape """
+ return self
+
+ @property
+ def base(self):
+ """ compat, we are always our own object """
+ return None
+
@classmethod
def from_array(cls, data, **kwargs):
"""
@@ -363,10 +372,9 @@ def from_codes(cls, codes, categories, ordered=False, name=None):
categories = cls._validate_categories(categories)
- if codes.max() >= len(categories) or codes.min() < -1:
+ if len(codes) and (codes.max() >= len(categories) or codes.min() < -1):
raise ValueError("codes need to be between -1 and len(categories)-1")
-
return Categorical(codes, categories=categories, ordered=ordered, name=name, fastpath=True)
_codes = None
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 7ab3e4d8d9482..306aebede2476 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -4381,7 +4381,7 @@ def get_reindexed_values(self, empty_dtype, upcasted_na):
else:
fill_value = upcasted_na
- if self.is_null:
+ if self.is_null and not getattr(self.block,'is_categorical',None):
missing_arr = np.empty(self.shape, dtype=empty_dtype)
if np.prod(self.shape):
# NumPy 1.6 workaround: this statement gets strange if all
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 6f8a774356293..56c444095ca51 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -23,7 +23,8 @@
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, _block2d_to_blocknd, _factor_indexer
+from pandas.core.internals import (BlockManager, make_block, _block2d_to_blocknd,
+ _factor_indexer, _block_shape)
from pandas.core.index import _ensure_index
from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type
import pandas.core.common as com
@@ -42,7 +43,7 @@
from distutils.version import LooseVersion
# versioning attribute
-_version = '0.10.1'
+_version = '0.15.2'
# PY3 encoding if we don't specify
_default_encoding = 'UTF-8'
@@ -467,7 +468,7 @@ def __len__(self):
def __unicode__(self):
output = '%s\nFile path: %s\n' % (type(self), pprint_thing(self._path))
if self.is_open:
- lkeys = list(self.keys())
+ lkeys = sorted(list(self.keys()))
if len(lkeys):
keys = []
values = []
@@ -1393,8 +1394,8 @@ class IndexCol(StringMixin):
_info_fields = ['freq', 'tz', 'index_name']
def __init__(self, values=None, kind=None, typ=None, cname=None,
- itemsize=None, name=None, axis=None, kind_attr=None, pos=None,
- freq=None, tz=None, index_name=None, **kwargs):
+ itemsize=None, name=None, axis=None, kind_attr=None,
+ pos=None, freq=None, tz=None, index_name=None, **kwargs):
self.values = values
self.kind = kind
self.typ = typ
@@ -1408,6 +1409,8 @@ def __init__(self, values=None, kind=None, typ=None, cname=None,
self.tz = tz
self.index_name = index_name
self.table = None
+ self.meta = None
+ self.metadata = None
if name is not None:
self.set_name(name, kind_attr)
@@ -1470,11 +1473,13 @@ def copy(self):
new_self = copy.copy(self)
return new_self
- def infer(self, table):
+ def infer(self, handler):
"""infer this column from the table: create and return a new object"""
+ table = handler.table
new_self = self.copy()
new_self.set_table(table)
new_self.get_attr()
+ new_self.read_metadata(handler)
return new_self
def convert(self, values, nan_rep, encoding):
@@ -1544,10 +1549,12 @@ def maybe_set_size(self, min_itemsize=None, **kwargs):
self.typ = _tables(
).StringCol(itemsize=min_itemsize, pos=self.pos)
- def validate_and_set(self, table, append, **kwargs):
- self.set_table(table)
+ def validate_and_set(self, handler, append, **kwargs):
+ self.set_table(handler.table)
self.validate_col()
self.validate_attr(append)
+ self.validate_metadata(handler)
+ self.write_metadata(handler)
self.set_attr()
def validate_col(self, itemsize=None):
@@ -1623,6 +1630,24 @@ def set_attr(self):
""" set the kind for this colummn """
setattr(self.attrs, self.kind_attr, self.kind)
+ def read_metadata(self, handler):
+ """ retrieve the metadata for this columns """
+ self.metadata = handler.read_metadata(self.cname)
+
+ def validate_metadata(self, handler):
+ """ validate that kind=category does not change the categories """
+ if self.meta == 'category':
+ new_metadata = self.metadata
+ cur_metadata = handler.read_metadata(self.cname)
+ if new_metadata is not None and cur_metadata is not None \
+ and not com.array_equivalent(new_metadata, cur_metadata):
+ raise ValueError("cannot append a categorical with different categories"
+ " to the existing")
+
+ def write_metadata(self, handler):
+ """ set the meta data """
+ if self.metadata is not None:
+ handler.write_metadata(self.cname,self.metadata)
class GenericIndexCol(IndexCol):
@@ -1654,11 +1679,13 @@ class DataCol(IndexCol):
data : the actual data
cname : the column name in the table to hold the data (typically
- values)
+ values)
+ meta : a string description of the metadata
+ metadata : the actual metadata
"""
is_an_indexable = False
is_data_indexable = False
- _info_fields = ['tz']
+ _info_fields = ['tz','ordered']
@classmethod
def create_for_block(
@@ -1683,17 +1710,25 @@ def create_for_block(
return cls(name=name, cname=cname, **kwargs)
def __init__(self, values=None, kind=None, typ=None,
- cname=None, data=None, block=None, **kwargs):
+ cname=None, data=None, meta=None, metadata=None, block=None, **kwargs):
super(DataCol, self).__init__(
values=values, kind=kind, typ=typ, cname=cname, **kwargs)
self.dtype = None
self.dtype_attr = u("%s_dtype" % self.name)
+ self.meta = meta
+ self.meta_attr = u("%s_meta" % self.name)
self.set_data(data)
+ self.set_metadata(metadata)
def __unicode__(self):
- return "name->%s,cname->%s,dtype->%s,shape->%s" % (
- self.name, self.cname, self.dtype, self.shape
- )
+ temp = tuple(
+ map(pprint_thing,
+ (self.name,
+ self.cname,
+ self.dtype,
+ self.kind,
+ self.shape)))
+ return "name->%s,cname->%s,dtype->%s,kind->%s,shape->%s" % temp
def __eq__(self, other):
""" compare 2 col items """
@@ -1715,10 +1750,18 @@ def take_data(self):
self.data, data = None, self.data
return data
+ def set_metadata(self, metadata):
+ """ record the metadata """
+ if metadata is not None:
+ metadata = np.array(metadata,copy=False).ravel()
+ self.metadata = metadata
+
def set_kind(self):
# set my kind if we can
+
if self.dtype is not None:
dtype = _ensure_decoded(self.dtype)
+
if dtype.startswith(u('string')) or dtype.startswith(u('bytes')):
self.kind = 'string'
elif dtype.startswith(u('float')):
@@ -1744,15 +1787,20 @@ def set_atom(self, block, block_items, existing_col, min_itemsize,
""" create and setup my atom from the block b """
self.values = list(block_items)
+
+ # short-cut certain block types
+ if block.is_categorical:
+ return self.set_atom_categorical(block, items=block_items, info=info)
+ elif block.is_datetime:
+ return self.set_atom_datetime64(block)
+ elif block.is_timedelta:
+ return self.set_atom_timedelta64(block)
+
dtype = block.dtype.name
rvalues = block.values.ravel()
inferred_type = lib.infer_dtype(rvalues)
- if inferred_type == 'datetime64':
- self.set_atom_datetime64(block)
- elif dtype == 'timedelta64[ns]':
- self.set_atom_timedelta64(block)
- elif inferred_type == 'date':
+ if inferred_type == 'date':
raise TypeError(
"[date] is not implemented as a table column")
elif inferred_type == 'datetime':
@@ -1803,9 +1851,6 @@ def set_atom(self, block, block_items, existing_col, min_itemsize,
raise TypeError(
"[unicode] is not implemented as a table column")
- elif dtype == 'category':
- raise NotImplementedError("cannot store a category dtype")
-
# this is basically a catchall; if say a datetime64 has nans then will
# end up here ###
elif inferred_type == 'string' or dtype == 'object':
@@ -1815,11 +1860,11 @@ def set_atom(self, block, block_items, existing_col, min_itemsize,
min_itemsize,
nan_rep,
encoding)
+
+ # set as a data block
else:
self.set_atom_data(block)
- return self
-
def get_atom_string(self, block, itemsize):
return _tables().StringCol(itemsize=itemsize, shape=block.shape[0])
@@ -1870,23 +1915,50 @@ def set_atom_string(self, block, block_items, existing_col, min_itemsize,
def convert_string_data(self, data, itemsize, encoding):
return _convert_string_array(data, encoding, itemsize)
- def get_atom_coltype(self):
+ def get_atom_coltype(self, kind=None):
""" return the PyTables column class for this column """
+ if kind is None:
+ kind = self.kind
if self.kind.startswith('uint'):
- col_name = "UInt%sCol" % self.kind[4:]
+ col_name = "UInt%sCol" % kind[4:]
else:
- col_name = "%sCol" % self.kind.capitalize()
+ col_name = "%sCol" % kind.capitalize()
return getattr(_tables(), col_name)
- def get_atom_data(self, block):
- return self.get_atom_coltype()(shape=block.shape[0])
+ def get_atom_data(self, block, kind=None):
+ return self.get_atom_coltype(kind=kind)(shape=block.shape[0])
def set_atom_data(self, block):
self.kind = block.dtype.name
self.typ = self.get_atom_data(block)
self.set_data(block.values.astype(self.typ.type))
+ def set_atom_categorical(self, block, items, info=None, values=None):
+ # currently only supports a 1-D categorical
+ # in a 1-D block
+
+ values = block.values
+ codes = values.codes
+ self.kind = 'integer'
+ self.dtype = codes.dtype.name
+ if values.ndim > 1:
+ raise NotImplementedError("only support 1-d categoricals")
+ if len(items) > 1:
+ raise NotImplementedError("only support single block categoricals")
+
+ # write the codes; must be in a block shape
+ self.ordered = values.ordered
+ self.typ = self.get_atom_data(block, kind=codes.dtype.name)
+ self.set_data(_block_shape(codes))
+
+ # write the categories
+ self.meta = 'category'
+ self.set_metadata(block.values.categories)
+
+ # update the info
+ self.update_info(info)
+
def get_atom_datetime64(self, block):
return _tables().Int64Col(shape=block.shape[0])
@@ -1935,12 +2007,16 @@ def convert(self, values, nan_rep, encoding):
"""set the data from this selection (and convert to the correct dtype
if we can)
"""
+
try:
values = values[self.cname]
except:
pass
self.set_data(values)
+ # use the meta if needed
+ meta = _ensure_decoded(self.meta)
+
# convert to the correct dtype
if self.dtype is not None:
dtype = _ensure_decoded(self.dtype)
@@ -1975,6 +2051,15 @@ def convert(self, values, nan_rep, encoding):
self.data = np.array(
[datetime.fromtimestamp(v) for v in self.data],
dtype=object)
+
+ elif meta == u('category'):
+
+ # we have a categorical
+ categories = self.metadata
+ self.data = Categorical.from_codes(self.data.ravel(),
+ categories=categories,
+ ordered=self.ordered)
+
else:
try:
@@ -1993,11 +2078,13 @@ def get_attr(self):
""" get the data for this colummn """
self.values = getattr(self.attrs, self.kind_attr, None)
self.dtype = getattr(self.attrs, self.dtype_attr, None)
+ self.meta = getattr(self.attrs, self.meta_attr, None)
self.set_kind()
def set_attr(self):
""" set the data for this colummn """
setattr(self.attrs, self.kind_attr, self.values)
+ setattr(self.attrs, self.meta_attr, self.meta)
if self.dtype is not None:
setattr(self.attrs, self.dtype_attr, self.dtype)
@@ -2010,8 +2097,8 @@ class DataIndexableCol(DataCol):
def get_atom_string(self, block, itemsize):
return _tables().StringCol(itemsize=itemsize)
- def get_atom_data(self, block):
- return self.get_atom_coltype()()
+ def get_atom_data(self, block, kind=None):
+ return self.get_atom_coltype(kind=kind)()
def get_atom_datetime64(self, block):
return _tables().Int64Col()
@@ -2761,6 +2848,7 @@ class Table(Fixed):
nan_rep : the string to use for nan representations for string
objects
levels : the names of levels
+ metadata : the names of the metadata columns
"""
pandas_kind = u('wide_table')
@@ -2775,6 +2863,7 @@ def __init__(self, *args, **kwargs):
self.non_index_axes = []
self.values_axes = []
self.data_columns = []
+ self.metadata = []
self.info = dict()
self.nan_rep = None
self.selection = None
@@ -2841,6 +2930,10 @@ def is_multi_index(self):
"""the levels attribute is 1 or a list in the case of a multi-index"""
return isinstance(self.levels, list)
+ def validate_metadata(self, existing):
+ """ create / validate metadata """
+ self.metadata = [ c.name for c in self.values_axes if c.metadata is not None ]
+
def validate_multiindex(self, obj):
"""validate that we can store the multi-index; reset and return the
new object
@@ -2904,10 +2997,10 @@ def queryables(self):
# compute the values_axes queryables
return dict(
- [(a.cname, a.kind) for a in self.index_axes] +
+ [(a.cname, a) for a in self.index_axes] +
[(self.storage_obj_type._AXIS_NAMES[axis], None)
for axis, values in self.non_index_axes] +
- [(v.cname, v.kind) for v in self.values_axes
+ [(v.cname, v) for v in self.values_axes
if v.name in set(self.data_columns)]
)
@@ -2919,6 +3012,30 @@ def values_cols(self):
""" return a list of my values cols """
return [i.cname for i in self.values_axes]
+ def _get_metadata_path(self, key):
+ """ return the metadata pathname for this key """
+ return "{group}/meta/{key}/meta".format(group=self.group._v_pathname,
+ key=key)
+
+ def write_metadata(self, key, values):
+ """
+ write out a meta data array to the key as a fixed-format Series
+
+ Parameters
+ ----------
+ key : string
+ values : ndarray
+
+ """
+ values = Series(values)
+ self.parent.put(self._get_metadata_path(key), values, format='table')
+
+ def read_metadata(self, key):
+ """ return the meta data array for this key """
+ if getattr(getattr(self.group,'meta',None),key,None) is not None:
+ return self.parent.select(self._get_metadata_path(key))
+ return None
+
def set_info(self):
""" update our table index info """
self.attrs.info = self.info
@@ -2933,6 +3050,7 @@ def set_attrs(self):
self.attrs.nan_rep = self.nan_rep
self.attrs.encoding = self.encoding
self.attrs.levels = self.levels
+ self.attrs.metadata = self.metadata
self.set_info()
def get_attrs(self):
@@ -2948,13 +3066,14 @@ def get_attrs(self):
getattr(self.attrs, 'encoding', None))
self.levels = getattr(
self.attrs, 'levels', None) or []
- t = self.table
self.index_axes = [
- a.infer(t) for a in self.indexables if a.is_an_indexable
+ a.infer(self) for a in self.indexables if a.is_an_indexable
]
self.values_axes = [
- a.infer(t) for a in self.indexables if not a.is_an_indexable
+ a.infer(self) for a in self.indexables if not a.is_an_indexable
]
+ self.metadata = getattr(
+ self.attrs, 'metadata', None) or []
def validate_version(self, where=None):
""" are we trying to operate on an old version? """
@@ -3338,6 +3457,9 @@ def get_blk_items(mgr, blocks):
# validate our min_itemsize
self.validate_min_itemsize(min_itemsize)
+ # validate our metadata
+ self.validate_metadata(existing_table)
+
# validate the axes if we have an existing table
if validate:
self.validate(existing_table)
@@ -3551,7 +3673,7 @@ def read(self, where=None, columns=None, **kwargs):
# the data need to be sorted
sorted_values = c.take_data().take(sorter, axis=0)
if sorted_values.ndim == 1:
- sorted_values = sorted_values.reshape(sorted_values.shape[0],1)
+ sorted_values = sorted_values.reshape((sorted_values.shape[0],1))
take_labels = [l.take(sorter) for l in labels]
items = Index(c.values)
@@ -3665,7 +3787,7 @@ def write(self, obj, axes=None, append=False, complib=None,
# validate the axes and set the kinds
for a in self.axes:
- a.validate_and_set(table, append)
+ a.validate_and_set(self, append)
# add the rows
self.write_data(chunksize, dropna=dropna)
@@ -3888,7 +4010,7 @@ def read(self, where=None, columns=None, **kwargs):
# if we have a DataIndexableCol, its shape will only be 1 dim
if values.ndim == 1:
- values = values.reshape(1, values.shape[0])
+ values = values.reshape((1, values.shape[0]))
block = make_block(values, placement=np.arange(len(cols_)))
mgr = BlockManager([block], [cols_, index_])
@@ -3983,10 +4105,10 @@ def get_attrs(self):
self.non_index_axes = []
self.nan_rep = None
self.levels = []
- t = self.table
- self.index_axes = [a.infer(t)
+
+ self.index_axes = [a.infer(self)
for a in self.indexables if a.is_an_indexable]
- self.values_axes = [a.infer(t)
+ self.values_axes = [a.infer(self)
for a in self.indexables if not a.is_an_indexable]
self.data_columns = [a.name for a in self.values_axes]
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 8cbad9ab2b3cb..e2a99076e7607 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -428,9 +428,9 @@ def test_versioning(self):
_maybe_remove(store, 'df1')
store.append('df1', df[:10])
store.append('df1', df[10:])
- self.assertEqual(store.root.a._v_attrs.pandas_version, '0.10.1')
- self.assertEqual(store.root.b._v_attrs.pandas_version, '0.10.1')
- self.assertEqual(store.root.df1._v_attrs.pandas_version, '0.10.1')
+ self.assertEqual(store.root.a._v_attrs.pandas_version, '0.15.2')
+ self.assertEqual(store.root.b._v_attrs.pandas_version, '0.15.2')
+ self.assertEqual(store.root.df1._v_attrs.pandas_version, '0.15.2')
# write a file and wipe its versioning
_maybe_remove(store, 'df2')
@@ -4571,29 +4571,83 @@ def test_query_with_nested_special_character(self):
tm.assert_frame_equal(expected, result)
def test_categorical(self):
- # FIXME
with ensure_clean_store(self.path) as store:
s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d']))
- self.assertRaises(NotImplementedError, store.put, 's_fixed', s, format='fixed')
- self.assertRaises(NotImplementedError, store.append, 's_table', s, format='table')
- #store.append('s', s, format='table')
- #result = store.select('s')
- #tm.assert_series_equal(s, result)
+ store.append('s', s, format='table')
+ result = store.select('s')
+ tm.assert_series_equal(s, result)
df = DataFrame({"s":s, "vals":[1,2,3,4,5,6]})
- self.assertRaises(NotImplementedError, store.put, 'df_fixed', df, format='fixed')
- self.assertRaises(NotImplementedError, store.append, 'df_table', df, format='table')
- #store.append('df', df, format='table')
- #result = store.select('df')
- #tm.assert_frame_equal(df, df2)
-
- # Ok, this doesn't work yet
- # FIXME: TypeError: cannot pass a where specification when reading from a Fixed format store. this store must be selected in its entirety
- #result = store.select('df', where = ['index>2'])
- #tm.assert_frame_equal(df[df.index>2],result)
+ store.append('df', df, format='table')
+ result = store.select('df')
+ tm.assert_frame_equal(result, df)
+
+ # dtypes
+ s = Series([1,1,2,2,3,4,5]).astype('category')
+ store.append('si',s)
+ result = store.select('si')
+ tm.assert_series_equal(result, s)
+
+ s = Series([1,1,np.nan,2,3,4,5]).astype('category')
+ store.append('si2',s)
+ result = store.select('si2')
+ tm.assert_series_equal(result, s)
+
+ # multiple
+ df2 = df.copy()
+ df2['s2'] = Series(list('abcdefg')).astype('category')
+ store.append('df2',df2)
+ result = store.select('df2')
+ tm.assert_frame_equal(result, df2)
+
+ # make sure the metadata is ok
+ self.assertTrue('/df2 ' in str(store))
+ self.assertTrue('/df2/meta/values_block_0/meta' in str(store))
+ self.assertTrue('/df2/meta/values_block_1/meta' in str(store))
+
+ # unordered
+ s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'],ordered=False))
+ store.append('s2', s, format='table')
+ result = store.select('s2')
+ tm.assert_series_equal(result, s)
+
+ # query
+ store.append('df3', df, data_columns=['s'])
+ expected = df[df.s.isin(['b','c'])]
+ result = store.select('df3', where = ['s in ["b","c"]'])
+ tm.assert_frame_equal(result, expected)
+
+ expected = df[df.s.isin(['d'])]
+ result = store.select('df3', where = ['s in ["d"]'])
+ tm.assert_frame_equal(result, expected)
+
+ expected = df[df.s.isin(['f'])]
+ result = store.select('df3', where = ['s in ["f"]'])
+ tm.assert_frame_equal(result, expected)
+
+ # appending with same categories is ok
+ store.append('df3', df)
+
+ df = concat([df,df])
+ expected = df[df.s.isin(['b','c'])]
+ result = store.select('df3', where = ['s in ["b","c"]'])
+ tm.assert_frame_equal(result, expected)
+
+ # appending must have the same categories
+ df3 = df.copy()
+ df3['s'].cat.remove_unused_categories(inplace=True)
+
+ self.assertRaises(ValueError, lambda : store.append('df3', df3))
+
+ # remove
+ # make sure meta data is removed (its a recursive removal so should be)
+ result = store.select('df3/meta/s/meta')
+ self.assertIsNotNone(result)
+ store.remove('df3')
+ self.assertRaises(KeyError, lambda : store.select('df3/meta/s/meta'))
def test_duplicate_column_name(self):
df = DataFrame(columns=["a", "a"], data=[[0, 0]])
| This is implemented by storing the codes directly in the table. And a metadata VLArray of the categories.
Query and appending work as expected. The only quirk is that I don't allow you to append to a table unless the new data has exactly the same categories. Otherwise the codes become meaningless.
This has the nice property of drastically shrinking the storage cost compared to regular strings (which are stored as fixed width of the maximum for that particular column).
I bumped the actual HDF5 storage version to current (was 0.10.1). Its not strictly necessary as this is a completely optional feature, but I am adding the sub-group space 'meta' (which FYI we can use for other things, e.g. to store the column labels and avoid the 64KB limit in attrs, their is an issue about this somewhere)
```
In [14]: df = DataFrame({'a' : Series(list('abccdef')).astype('category'), 'b' : np.random.randn(7)})
In [15]: df
Out[15]:
a b
0 a -0.094609
1 b -1.814638
2 c 0.214974
3 c -0.195395
4 d 0.206022
5 e 1.130589
6 f -0.832810
In [19]: store = pd.HDFStore('test.h5',mode='w')
In [20]: store.append('df',df,data_columns=['a'])
In [21]: store.select('df',where=["a in ['b','d']"])
Out[21]:
a b
1 b -1.814638
4 d 0.206022
In [22]: store.select('df',where=["a in ['b','d']"]).dtypes
Out[22]:
a category
b float64
dtype: object
In [25]: store.get_storer('df').group
Out[25]:
/df (Group) u''
children := ['table' (Table), 'meta' (Group)]
In [26]: store.get_storer('df').group.table
Out[26]:
/df/table (Table(7,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
"a": Int8Col(shape=(), dflt=0, pos=2)}
byteorder := 'little'
chunkshape := (3855,)
autoindex := True
colindexes := {
"a": Index(6, medium, shuffle, zlib(1)).is_csi=False,
"index": Index(6, medium, shuffle, zlib(1)).is_csi=False}
In [27]: store.get_storer('df').group.meta
Out[27]:
/df/meta (Group) u''
children := ['a' (VLArray)]
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8793 | 2014-11-12T01:55:30Z | 2014-11-17T00:34:49Z | 2014-11-17T00:34:49Z | 2014-11-17T00:34:50Z |
TST: Add test for get_data_google dtypes | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index fe1454d4d2582..df09168a50449 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -63,3 +63,4 @@ Bug Fixes
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
- ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`).
+- Bug where ``get_data_google``returned object dtypes (:issue:`3995`)
diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py
index dbc439e6c872e..3fca0393339fc 100644
--- a/pandas/io/tests/test_data.py
+++ b/pandas/io/tests/test_data.py
@@ -117,6 +117,14 @@ def test_get_multi2(self):
self.assertEqual((4, 3), result.shape)
assert_n_failed_equals_n_null_columns(w, result)
+ def test_dtypes(self):
+ #GH3995
+ data = web.get_data_google('MSFT', 'JAN-01-12', 'JAN-31-12')
+ assert np.issubdtype(data.Open.dtype, np.number)
+ assert np.issubdtype(data.Close.dtype, np.number)
+ assert np.issubdtype(data.Low.dtype, np.number)
+ assert np.issubdtype(data.High.dtype, np.number)
+ assert np.issubdtype(data.Volume.dtype, np.number)
class TestYahoo(tm.TestCase):
@classmethod
| closes #3995
| https://api.github.com/repos/pandas-dev/pandas/pulls/8792 | 2014-11-12T00:12:57Z | 2014-11-14T12:49:34Z | 2014-11-14T12:49:34Z | 2014-11-14T12:49:41Z |
BUG: pd.Timedelta np.int, np.float. fixes #8757 | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index ee30e0bdadc5d..97cbecd4bb0e7 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -61,3 +61,4 @@ Bug Fixes
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo and when it receives no data from Yahoo (:issue:`8761`), (:issue:`8783`).
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
+- ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`).
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py
index 11bf22a055b8f..c8dd5573370d9 100644
--- a/pandas/tseries/tests/test_timedeltas.py
+++ b/pandas/tseries/tests/test_timedeltas.py
@@ -38,12 +38,24 @@ def test_construction(self):
self.assertEqual(Timedelta(10.0,unit='d').value, expected)
self.assertEqual(Timedelta('10 days').value, expected)
self.assertEqual(Timedelta(days=10).value, expected)
+ self.assertEqual(Timedelta(days=10.0).value, expected)
expected += np.timedelta64(10,'s').astype('m8[ns]').view('i8')
self.assertEqual(Timedelta('10 days 00:00:10').value, expected)
self.assertEqual(Timedelta(days=10,seconds=10).value, expected)
self.assertEqual(Timedelta(days=10,milliseconds=10*1000).value, expected)
self.assertEqual(Timedelta(days=10,microseconds=10*1000*1000).value, expected)
+
+ # test construction with np dtypes
+ # GH 8757
+ timedelta_kwargs = {'days':'D', 'seconds':'s', 'microseconds':'us',
+ 'milliseconds':'ms', 'minutes':'m', 'hours':'h', 'weeks':'W'}
+ npdtypes = [np.int64, np.int32, np.int16,
+ np.float64, np.float32, np.float16]
+ for npdtype in npdtypes:
+ for pykwarg, npkwarg in timedelta_kwargs.items():
+ expected = np.timedelta64(1, npkwarg).astype('m8[ns]').view('i8')
+ self.assertEqual(Timedelta(**{pykwarg:npdtype(1)}).value, expected)
# rounding cases
self.assertEqual(Timedelta(82739999850000).value, 82739999850000)
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index ffe94a94b15b5..30c18e2d5d594 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -37,7 +37,7 @@ from datetime import time as datetime_time
from dateutil.tz import (tzoffset, tzlocal as _dateutil_tzlocal, tzfile as _dateutil_tzfile,
tzutc as _dateutil_tzutc, gettz as _dateutil_gettz)
from pytz.tzinfo import BaseTzInfo as _pytz_BaseTzInfo
-from pandas.compat import parse_date, string_types, PY3
+from pandas.compat import parse_date, string_types, PY3, iteritems
from sys import version_info
import operator
@@ -1619,6 +1619,7 @@ class Timedelta(_Timedelta):
Denote the unit of the input, if input is an integer. Default 'ns'.
days, seconds, microseconds, milliseconds, minutes, hours, weeks : numeric, optional
Values for construction in compat with datetime.timedelta.
+ np ints and floats will be coereced to python ints and floats.
Notes
-----
@@ -1632,9 +1633,19 @@ class Timedelta(_Timedelta):
if value is None:
if not len(kwargs):
raise ValueError("cannot construct a TimeDelta without a value/unit or descriptive keywords (days,seconds....)")
+
+ def _to_py_int_float(v):
+ if is_integer_object(v):
+ return int(v)
+ elif is_float_object(v):
+ return float(v)
+ raise TypeError("Invalid type {0}. Must be int or float.".format(type(v)))
+
+ kwargs = dict([ (k, _to_py_int_float(v)) for k, v in iteritems(kwargs) ])
+
try:
value = timedelta(**kwargs)
- except (TypeError):
+ except TypeError as e:
raise ValueError("cannot construct a TimeDelta from the passed arguments, allowed keywords are "
"[days, seconds, microseconds, milliseconds, minutes, hours, weeks]")
| closes #8757
| https://api.github.com/repos/pandas-dev/pandas/pulls/8787 | 2014-11-11T17:46:19Z | 2014-11-11T23:37:43Z | 2014-11-11T23:37:43Z | 2014-11-11T23:37:48Z |
Fix unrecognized 'Z' UTC designator | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 83e465df7b891..4503e94c1d4c0 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -93,3 +93,4 @@ Bug Fixes
- Bug in `pd.infer_freq`/`DataFrame.inferred_freq` that prevented proper sub-daily frequency inference
when the index contained DST days (:issue:`8772`).
+- Regression in ``Timestamp`` does not parse 'Z' zone designator for UTC (:issue:`8771`)
diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c
index 3f09de851e231..c94e759e0a472 100644
--- a/pandas/src/datetime/np_datetime_strings.c
+++ b/pandas/src/datetime/np_datetime_strings.c
@@ -363,7 +363,8 @@ convert_datetimestruct_local_to_utc(pandas_datetimestruct *out_dts_utc,
* to be cast to the 'unit' parameter.
*
* 'out' gets filled with the parsed date-time.
- * 'out_local' gets whether returned value contains timezone. 0 for UTC, 1 for local time.
+ * 'out_local' gets set to 1 if the parsed time contains timezone,
+ * to 0 otherwise.
* 'out_tzoffset' gets set to timezone offset by minutes
* if the parsed time was in local time,
* to 0 otherwise. The values 'now' and 'today' don't get counted
@@ -785,9 +786,13 @@ parse_iso_8601_datetime(char *str, int len,
/* UTC specifier */
if (*substr == 'Z') {
- /* "Z" means not local */
+ /* "Z" should be equivalent to tz offset "+00:00" */
if (out_local != NULL) {
- *out_local = 0;
+ *out_local = 1;
+ }
+
+ if (out_tzoffset != NULL) {
+ *out_tzoffset = 0;
}
if (sublen == 1) {
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index 9adcbb4ea4a41..89473b20659bc 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -6,7 +6,7 @@
import datetime
from pandas.core.api import Timestamp, Series
-from pandas.tslib import period_asfreq, period_ordinal
+from pandas.tslib import period_asfreq, period_ordinal, get_timezone
from pandas.tseries.index import date_range
from pandas.tseries.frequencies import get_freq
import pandas.tseries.offsets as offsets
@@ -298,6 +298,11 @@ def test_barely_oob_dts(self):
# One us more than the maximum is an error
self.assertRaises(ValueError, Timestamp, max_ts_us + one_us)
+ def test_utc_z_designator(self):
+ self.assertEqual(get_timezone(Timestamp('2014-11-02 01:00Z').tzinfo), 'UTC')
+ self.assertEqual(get_timezone(Timestamp('2014-11-02 01:00Z00').tzinfo), 'UTC')
+ self.assertRaises(ValueError, Timestamp, '2014-11-02 01:00Z0')
+
class TestDatetimeParsingWrappers(tm.TestCase):
def test_does_not_convert_mixed_integer(self):
| closes [#8771](https://github.com/pydata/pandas/issues/8771)
| https://api.github.com/repos/pandas-dev/pandas/pulls/8786 | 2014-11-11T17:18:26Z | 2014-11-15T02:33:45Z | null | 2014-11-16T20:34:40Z |
COMPAT: remove Index warnings from io/ga.py | diff --git a/pandas/io/ga.py b/pandas/io/ga.py
index f002994888932..57de5c6f5abb4 100644
--- a/pandas/io/ga.py
+++ b/pandas/io/ga.py
@@ -430,7 +430,7 @@ def _clean_index(index_dims, parse_dates):
to_remove = pd.Index(set(to_remove))
to_add = pd.Index(set(to_add))
- return index_dims - to_remove + to_add
+ return index_dims.difference(to_remove).union(to_add)
def _get_col_names(header_info):
| https://api.github.com/repos/pandas-dev/pandas/pulls/8785 | 2014-11-11T14:40:19Z | 2014-11-17T15:12:38Z | 2014-11-17T15:12:38Z | 2014-11-17T15:12:38Z | |
BUG: Issue with to_latex and MultiIndex column format. GH8336 | diff --git a/doc/source/10min.rst b/doc/source/10min.rst
index c98f41973e1ee..1f59c38d75f93 100644
--- a/doc/source/10min.rst
+++ b/doc/source/10min.rst
@@ -12,7 +12,11 @@
from pandas import options
import pandas as pd
np.set_printoptions(precision=4, suppress=True)
- options.display.mpl_style='default'
+ import matplotlib
+ try:
+ matplotlib.style.use('ggplot')
+ except AttributeError:
+ options.display.mpl_style = 'default'
options.display.max_rows=15
#### portions of this were borrowed from the
@@ -695,8 +699,6 @@ Plotting
import matplotlib.pyplot as plt
plt.close('all')
- from pandas import options
- options.display.mpl_style='default'
.. ipython:: python
diff --git a/doc/source/api.rst b/doc/source/api.rst
index 58ea517d055a0..b617009fe2f13 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -517,8 +517,6 @@ String handling
strings and apply several methods to it. These can be acccessed like
``Series.str.<function/property>``.
-.. currentmodule:: pandas.core.strings
-
.. autosummary::
:toctree: generated/
:template: autosummary/accessor_method.rst
@@ -863,6 +861,7 @@ Combining / joining / merging
:toctree: generated/
DataFrame.append
+ DataFrame.assign
DataFrame.join
DataFrame.merge
DataFrame.update
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index 7fe04af716cec..d03e0fb117c5c 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -13,7 +13,6 @@
from pandas import *
import pandas as pd
np.set_printoptions(precision=4, suppress=True)
- options.display.mpl_style='default'
options.display.max_rows=15
diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index 759675c51b960..4b0fe39d929a9 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -10,9 +10,13 @@
import pandas.util.testing as tm
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
+ import matplotlib
+ try:
+ matplotlib.style.use('ggplot')
+ except AttributeError:
+ options.display.mpl_style = 'default'
import matplotlib.pyplot as plt
plt.close('all')
- options.display.mpl_style='default'
options.display.max_rows=15
Computational tools
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index 6a3751cf7a0b8..0e6386955a653 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -17,7 +17,12 @@
np.random.seed(123456)
pd.options.display.max_rows=15
- pd.options.display.mpl_style='default'
+
+ import matplotlib
+ try:
+ matplotlib.style.use('ggplot')
+ except AttributeError:
+ pd.options.display.mpl_style = 'default'
np.set_printoptions(precision=4, suppress=True)
diff --git a/doc/source/faq.rst b/doc/source/faq.rst
index de88b436198dd..467ec02b55f20 100644
--- a/doc/source/faq.rst
+++ b/doc/source/faq.rst
@@ -21,7 +21,11 @@ Frequently Asked Questions (FAQ)
from pandas.tseries.offsets import *
import matplotlib.pyplot as plt
plt.close('all')
- options.display.mpl_style='default'
+ import matplotlib
+ try:
+ matplotlib.style.use('ggplot')
+ except AttributeError:
+ options.display.mpl_style = 'default'
from pandas.compat import lrange
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index db19e0de3d788..7ad2641dec52a 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -12,7 +12,11 @@
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.close('all')
- options.display.mpl_style='default'
+ import matplotlib
+ try:
+ matplotlib.style.use('ggplot')
+ except AttributeError:
+ options.display.mpl_style = 'default'
from pandas.compat import zip
*****************************
@@ -346,7 +350,7 @@ A single group can be selected using ``GroupBy.get_group()``:
.. ipython:: python
grouped.get_group('bar')
-
+
Or for an object grouped on multiple columns:
.. ipython:: python
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 5079b4fa8ad6f..fc074802353ee 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -1137,6 +1137,17 @@ should be taken instead.
df2.drop_duplicates(['a','b'])
df2.drop_duplicates(['a','b'], take_last=True)
+An alternative way to drop duplicates on the index is ``.groupby(level=0)`` combined with ``first()`` or ``last()``.
+
+.. ipython:: python
+
+ df3 = df2.set_index('b')
+ df3
+ df3.groupby(level=0).first()
+
+ # a bit more verbose
+ df3.reset_index().drop_duplicates(subset='b', take_last=False).set_index('b')
+
.. _indexing.dictionarylike:
Dictionary-like :meth:`~pandas.DataFrame.get` method
diff --git a/doc/source/sparse.rst b/doc/source/sparse.rst
index e72ee6b709282..79def066f0710 100644
--- a/doc/source/sparse.rst
+++ b/doc/source/sparse.rst
@@ -10,9 +10,6 @@
import pandas.util.testing as tm
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
- import matplotlib.pyplot as plt
- plt.close('all')
- options.display.mpl_style='default'
options.display.max_rows = 15
**********************
@@ -222,4 +219,3 @@ row and columns coordinates of the matrix. Note that this will consume a signifi
ss_dense = SparseSeries.from_coo(A, dense_index=True)
ss_dense
-
diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index 852397c355361..9d4cba2e5ee8c 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -31,10 +31,14 @@ We use the standard convention for referencing the matplotlib API:
import matplotlib.pyplot as plt
-.. versionadded:: 0.11.0
+The plots in this document are made using matplotlib's ``ggplot`` style (new in version 1.4):
-The plots in this document are made using matplotlib's ``ggplot`` style (new in version 1.4).
-If your version of matplotlib is 1.3 or lower, setting the ``display.mpl_style`` to ``'default'``
+.. code-block:: python
+
+ import matplotlib
+ matplotlib.style.use('ggplot')
+
+If your version of matplotlib is 1.3 or lower, you can set ``display.mpl_style`` to ``'default'``
with ``pd.options.display.mpl_style = 'default'``
to produce more appealing plots.
When set, matplotlib's ``rcParams`` are changed (globally!) to nicer-looking settings.
diff --git a/doc/source/whatsnew.rst b/doc/source/whatsnew.rst
index 4f5991bc20ed9..d05c19a5e4bea 100644
--- a/doc/source/whatsnew.rst
+++ b/doc/source/whatsnew.rst
@@ -18,6 +18,8 @@ What's New
These are new features and improvements of note in each release.
+.. include:: whatsnew/v0.16.1.txt
+
.. include:: whatsnew/v0.16.0.txt
.. include:: whatsnew/v0.15.2.txt
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt
index 9c88815887674..3c3742c968642 100644
--- a/doc/source/whatsnew/v0.16.1.txt
+++ b/doc/source/whatsnew/v0.16.1.txt
@@ -3,24 +3,34 @@
v0.16.1 (April ??, 2015)
------------------------
-This is a minor bug-fix release from 0.16.0 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.
+This is a minor bug-fix release from 0.16.0 and includes a a large number of
+bug fixes along several new features, enhancements, and performance improvements.
+We recommend that all users upgrade to this version.
+
+.. contents:: What's new in v0.16.1
+ :local:
+ :backlinks: none
+
+
+.. _whatsnew_0161.enhancements:
+
+Enhancements
+~~~~~~~~~~~~
+
+
+
+
-- :ref:`Enhancements <whatsnew_0161.enhancements>`
-- :ref:`API Changes <whatsnew_0161.api>`
-- :ref:`Performance Improvements <whatsnew_0161.performance>`
-- :ref:`Bug Fixes <whatsnew_0161.bug_fixes>`
.. _whatsnew_0161.api:
API changes
~~~~~~~~~~~
-.. _whatsnew_0161.enhancements:
-Enhancements
-~~~~~~~~~~~~
+
+
+
- Add support for separating years and quarters using dashes, for
example 2014-Q1. (:issue:`9688`)
@@ -30,7 +40,27 @@ Enhancements
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+
+
.. _whatsnew_0161.bug_fixes:
Bug Fixes
~~~~~~~~~
+
+
+
+
+
+- Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`)
+
+
+
+
+
+
+
+
+- Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`)
diff --git a/pandas/core/format.py b/pandas/core/format.py
index b21ca9050ffd0..78f06b04dc7fd 100644
--- a/pandas/core/format.py
+++ b/pandas/core/format.py
@@ -608,14 +608,20 @@ def get_col_type(dtype):
strcols = self._to_str_columns()
if self.index and isinstance(self.frame.index, MultiIndex):
- clevels = self.frame.columns.nlevels
strcols.pop(0)
- name = any(self.frame.columns.names)
- for i, lev in enumerate(self.frame.index.levels):
- lev2 = lev.format(name=name)
- width = len(lev2[0])
- lev3 = [' ' * width] * clevels + lev2
- strcols.insert(i, lev3)
+
+
+ fmt = self._get_formatter('__index__')
+ fmt_index = self.frame.index.format(sparsify=self.sparsify,
+ adjoin=False,
+ names=True,
+ formatter=fmt)
+
+ for i, lev in enumerate(fmt_index):
+ width = len(lev[0])
+ lev2 = [width * ' ' if l == '' else l for l in lev]
+ lev2.insert(0, width * ' ')
+ strcols.insert(i, lev2)
if column_format is None:
dtypes = self.frame.dtypes.values
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 73439fb1e535d..6d98b3b99021b 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -2453,7 +2453,7 @@ def _transform_fast(self, func):
if isinstance(func, compat.string_types):
func = getattr(self,func)
values = func().values
- counts = self.count().values
+ counts = self.size().values
values = np.repeat(values, com._ensure_platform_int(counts))
return self._set_result_index_ordered(Series(values))
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 7e3b21be13525..68f3a6032402f 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2095,7 +2095,7 @@ def _maybe_box(self, func, dropna=False):
boxer = com.i8_boxer(self)
if len(values) == 0:
- return boxer(iNaT)
+ return boxer(tslib.iNaT)
values = values.view('i8')
result = func(values)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 79ebb80fc9ebb..e7001eb09f20c 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -1058,6 +1058,19 @@ def test_transform_function_aliases(self):
expected = self.df.groupby('A')['C'].transform(np.mean)
assert_series_equal(result, expected)
+ def test_transform_length(self):
+ # GH 9697
+ df = pd.DataFrame({'col1':[1,1,2,2], 'col2':[1,2,3,np.nan]})
+ expected = pd.Series([3.0]*4)
+ def nsum(x):
+ return np.nansum(x)
+ results = [df.groupby('col1').transform(sum)['col2'],
+ df.groupby('col1')['col2'].transform(sum),
+ df.groupby('col1').transform(nsum)['col2'],
+ df.groupby('col1')['col2'].transform(nsum)]
+ for result in results:
+ assert_series_equal(result, expected)
+
def test_with_na(self):
index = Index(np.arange(10))
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index ae2ed4eaca2f4..9b5e36974553b 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -6837,6 +6837,11 @@ def test_repeat(self):
def test_unique_data_ownership(self):
# it works! #1807
Series(Series(["a", "c", "b"]).unique()).sort()
+
+ def test_datetime_timedelta_quantiles(self):
+ # covers #9694
+ self.assertTrue(pd.isnull(Series([],dtype='M8[ns]').quantile(.5)))
+ self.assertTrue(pd.isnull(Series([],dtype='m8[ns]').quantile(.5)))
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index 79adabafb7044..e452ddee9d8db 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -167,7 +167,7 @@ def test_repr(self):
# dateutil zone change (only matters for repr)
import dateutil
- if dateutil.__version__ >= LooseVersion('2.3') and dateutil.__version__ <= LooseVersion('2.4'):
+ if dateutil.__version__ >= LooseVersion('2.3') and dateutil.__version__ <= LooseVersion('2.4.0'):
timezones = ['UTC', 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Pacific']
else:
timezones = ['UTC', 'Asia/Tokyo', 'US/Eastern', 'dateutil/America/Los_Angeles']
| closes #8336
This is a potential resolution to GH8336 It borrows the same code flow from _get_formatted_index in
pandas.core.format
``` python
import pandas as pd
pd.show_versions()
df = pd.DataFrame({
"A": [191, 191, 193, 193, 193],
"B": [1, 1, 2, 2, 3],
"C": ["foo", "foo", "foo", "foo", "foo"],
"D": ["bar", "bar", "bar", "bar", "bar"]})
print "\nOutput:"
print df.set_index(["A", "B"]).to_latex()
```
``` latex
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.17.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: 2.1.1
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
Output:
\begin{tabular}{llll}
\toprule
& & C & D \\
\midrule
191 & 1 & & \\
193 & 2 & foo & bar \\
\bottomrule
\end{tabular}
```
After PR
``` latex
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.17.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.15.1-18-g81c8a5f
nose: None
Cython: None
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: 2.3.0
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
Output:
\begin{tabular}{llll}
\toprule
& & C & D \\
\midrule
191 & 1 & & \\
& 1 & foo & bar \\
193 & 2 & foo & bar \\
& 2 & foo & bar \\
& 3 & foo & bar \\
\bottomrule
\end{tabular}
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8784 | 2014-11-11T13:52:55Z | 2015-07-28T21:50:15Z | null | 2023-05-11T01:12:43Z |
TST: Add additional RemoteDataError check to io.data.Options | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index b1fa6dbed442d..fb0e4ecbf3119 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -44,5 +44,5 @@ 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`).
+- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo and when it receives no data from Yahoo (:issue:`8761`), (:issue:`8783`).
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
diff --git a/pandas/io/data.py b/pandas/io/data.py
index d69343817a63f..0827d74191842 100644
--- a/pandas/io/data.py
+++ b/pandas/io/data.py
@@ -685,8 +685,14 @@ def _option_frames_from_url(self, url):
except IndexError:
self.underlying_price, self.quote_time = np.nan, np.nan
- calls = self._process_data(frames[self._TABLE_LOC['calls']], 'call')
- puts = self._process_data(frames[self._TABLE_LOC['puts']], 'put')
+ calls = frames[self._TABLE_LOC['calls']]
+ puts = frames[self._TABLE_LOC['puts']]
+
+ if len(calls) == 0 or len(puts) == 0:
+ raise RemoteDataError('Received no data from Yahoo at url: %s' % url)
+
+ calls = self._process_data(calls, 'call')
+ puts = self._process_data(puts, 'put')
return {'calls': calls, 'puts': puts}
| Per comment in #8761
| https://api.github.com/repos/pandas-dev/pandas/pulls/8783 | 2014-11-11T05:07:23Z | 2014-11-11T14:09:56Z | 2014-11-11T14:09:56Z | 2014-11-12T12:59:26Z |
Do not convert series when looking at sub-daily frequencies. | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 97cbecd4bb0e7..1cd53cd575650 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -62,3 +62,5 @@ Bug Fixes
- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
- ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`).
+- Bug in `pd.infer_freq`/`DataFrame.inferred_freq` that prevented proper sub-daily frequency inference
+ when the index contained DST days (:issue:`8772`).
diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py
index 7cd286129e936..54b29b1641309 100644
--- a/pandas/tseries/frequencies.py
+++ b/pandas/tseries/frequencies.py
@@ -698,6 +698,8 @@ def __init__(self, index, warn=True):
self.index = index
self.values = np.asarray(index).view('i8')
+ # This moves the values, which are implicitly in UTC, to the
+ # the timezone so they are in local time
if hasattr(index,'tz'):
if index.tz is not None:
self.values = tslib.tz_convert(self.values, 'UTC', index.tz)
@@ -712,10 +714,18 @@ def __init__(self, index, warn=True):
@cache_readonly
def deltas(self):
return tslib.unique_deltas(self.values)
+
+ @cache_readonly
+ def deltas_asi8(self):
+ return tslib.unique_deltas(self.index.asi8)
@cache_readonly
def is_unique(self):
return len(self.deltas) == 1
+
+ @cache_readonly
+ def is_unique_asi8(self):
+ return len(self.deltas_asi8) == 1
def get_freq(self):
if not self.is_monotonic or not self.index.is_unique:
@@ -725,9 +735,12 @@ def get_freq(self):
if _is_multiple(delta, _ONE_DAY):
return self._infer_daily_rule()
else:
- # Possibly intraday frequency
- if not self.is_unique:
+ # Possibly intraday frequency. Here we use the
+ # original .asi8 values as the modified values
+ # will not work around DST transitions. See #8772
+ if not self.is_unique_asi8:
return None
+ delta = self.deltas_asi8[0]
if _is_multiple(delta, _ONE_HOUR):
# Hours
return _maybe_add_count('H', delta / _ONE_HOUR)
diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py
index b251ae50e22d6..b84cdefe7009f 100644
--- a/pandas/tseries/tests/test_frequencies.py
+++ b/pandas/tseries/tests/test_frequencies.py
@@ -268,6 +268,24 @@ def test_infer_freq_tz(self):
idx = DatetimeIndex(dates, tz=tz)
self.assertEqual(idx.inferred_freq, expected)
+ def test_infer_freq_tz_transition(self):
+ # Tests for #8772
+ date_pairs = [['2013-11-02', '2013-11-5'], #Fall DST
+ ['2014-03-08', '2014-03-11'], #Spring DST
+ ['2014-01-01', '2014-01-03']] #Regular Time
+ freqs = ['3H', '10T', '3601S', '3600001L', '3600000001U', '3600000000001N']
+
+ for tz in [None, 'Australia/Sydney', 'Asia/Tokyo', 'Europe/Paris',
+ 'US/Pacific', 'US/Eastern']:
+ for date_pair in date_pairs:
+ for freq in freqs:
+ idx = date_range(date_pair[0], date_pair[1], freq=freq, tz=tz)
+ print(idx)
+ self.assertEqual(idx.inferred_freq, freq)
+
+ index = date_range("2013-11-03", periods=5, freq="3H").tz_localize("America/Chicago")
+ self.assertIsNone(index.inferred_freq)
+
def test_not_monotonic(self):
rng = _dti(['1/31/2000', '1/31/2001', '1/31/2002'])
rng = rng[::-1]
| Fixes #8772 by not converting values from UTC to local timezone when trying to infer sub-daily frequencies. Across a DST transition this will result in a non-unique deltas and an inferred frequency of None.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8782 | 2014-11-11T02:06:09Z | 2014-11-14T19:31:48Z | 2014-11-14T19:31:48Z | 2014-11-14T19:31:56Z |
Allowing empty slice in multi-indices | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 4bcbcb82e7c83..b1fa6dbed442d 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -44,4 +44,5 @@ 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
+- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`).
+- Bug in slicing a multi-index with an empty list and at least one boolean indexer (:issue:`8781`)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index a6907c3f8b5f2..40c0fdaf91fab 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -4148,9 +4148,9 @@ def _convert_indexer(r):
# ignore not founds
continue
if len(k):
- ranges.append(reduce(np.logical_or,indexers))
+ ranges.append(reduce(np.logical_or, indexers))
else:
- ranges.append(tuple([]))
+ ranges.append(np.zeros(self.labels[i].shape, dtype=bool))
elif _is_null_slice(k):
# empty slice
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index f41e1ac03f993..66307d58b2f27 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -2139,9 +2139,11 @@ def f():
df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index)
df = df.sortlevel(0, axis=1)
- result = df.loc[:, ([], slice(None))]
+ result1 = df.loc[:, ([], slice(None))]
+ result2 = df.loc[:, (['foo'], [])]
expected = DataFrame(index=range(5),columns=multi_index.reindex([])[0])
- assert_frame_equal(result, expected)
+ assert_frame_equal(result1, expected)
+ assert_frame_equal(result2, expected)
# regression from < 0.14.0
# GH 7914
| This solves #8737 in the case where there is at least one boolean indexer. The patch of #8739 does not allow the following:
``` python
import pandas as pd
import numpy as np
multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
df = pd.DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index)
df = df.sortlevel(0, axis=1)
df.loc[:, (['foo'], [])]
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8781 | 2014-11-10T21:14:40Z | 2014-11-11T00:11:42Z | 2014-11-11T00:11:42Z | 2014-11-11T00:12:45Z |
TST: Change a failing wb test #8768 | diff --git a/pandas/io/tests/test_wb.py b/pandas/io/tests/test_wb.py
index 19fee9361ae40..7cdfc85b84e90 100644
--- a/pandas/io/tests/test_wb.py
+++ b/pandas/io/tests/test_wb.py
@@ -15,17 +15,13 @@ class TestWB(tm.TestCase):
@network
def test_wdi_search(self):
- expected = {u('id'): {6716: u('NY.GDP.PCAP.KD'),
- 6718: u('NY.GDP.PCAP.KN'),
- 6720: u('NY.GDP.PCAP.PP.KD')},
- u('name'): {6716: u('GDP per capita (constant 2005 US$)'),
- 6718: u('GDP per capita (constant LCU)'),
- 6720: u('GDP per capita, PPP (constant 2011 '
- 'international $)')}}
- result = search('gdp.*capita.*constant').loc[6716:,['id','name']]
- expected = pandas.DataFrame(expected)
- expected.index = result.index
- assert_frame_equal(result, expected)
+ # Test that a name column exists, and that some results were returned
+ # ...without being too strict about what the actual contents of the
+ # results actually are. The fact that there are some, is good enough.
+
+ result = search('gdp.*capita.*constant')
+ GDP_labels = [l for l in result.name if 'GDP' in l.upper()]
+ self.assertTrue(len(GDP_labels) > 1)
@slow
@network
| Fixes a previously poorly designed test in io.wb.
Closes #8768
| https://api.github.com/repos/pandas-dev/pandas/pulls/8769 | 2014-11-10T03:32:07Z | 2014-11-10T12:43:19Z | null | 2015-02-09T00:44:01Z |
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)
```
| 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 |
BUG: Fix plots showing multiple sets of axis labels if index is a timeseries. | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 58dc1da214c05..874eb9155e2a3 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -222,3 +222,5 @@ Bug Fixes
- Fixed ValueError raised by cummin/cummax when datetime64 Series contains NaT. (:issue:`8965`)
+- Bug in plotting if sharex was enabled and index was a timeseries, would show labels on multiple axes (:issue:`3964`).
+
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index b06342e8ce3c3..20f4b13867188 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -1329,6 +1329,32 @@ def test_subplots_multiple_axes(self):
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
self.assertEqual(axes.shape, (1, ))
+ def test_subplots_ts_share_axes(self):
+ # GH 3964
+ fig, axes = self.plt.subplots(3, 3, sharex=True, sharey=True)
+ self.plt.subplots_adjust(left=0.05, right=0.95, hspace=0.3, wspace=0.3)
+ df = DataFrame(np.random.randn(10, 9), index=date_range(start='2014-07-01', freq='M', periods=10))
+ for i, ax in enumerate(axes.ravel()):
+ df[i].plot(ax=ax, fontsize=5)
+
+ #Rows other than bottom should not be visible
+ for ax in axes[0:-1].ravel():
+ self._check_visible(ax.get_xticklabels(), visible=False)
+
+ #Bottom row should be visible
+ for ax in axes[-1].ravel():
+ self._check_visible(ax.get_xticklabels(), visible=True)
+
+ #First column should be visible
+ for ax in axes[[0, 1, 2], [0]].ravel():
+ self._check_visible(ax.get_yticklabels(), visible=True)
+
+ #Other columns should not be visible
+ for ax in axes[[0, 1, 2], [1]].ravel():
+ self._check_visible(ax.get_yticklabels(), visible=False)
+ for ax in axes[[0, 1, 2], [2]].ravel():
+ self._check_visible(ax.get_yticklabels(), visible=False)
+
def test_negative_log(self):
df = - DataFrame(rand(6, 4),
index=list(string.ascii_letters[:6]),
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index b9a96ee262101..9fa747f28069b 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -1022,7 +1022,10 @@ def _post_plot_logic(self):
def _adorn_subplots(self):
to_adorn = self.axes
- # todo: sharex, sharey handling?
+ if len(self.axes) > 0:
+ all_axes = self._get_axes()
+ nrows, ncols = self._get_axes_layout()
+ _handle_shared_axes(all_axes, len(all_axes), len(all_axes), nrows, ncols, self.sharex, self.sharey)
for ax in to_adorn:
if self.yticks is not None:
@@ -1375,6 +1378,19 @@ def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True):
errors[kw] = err
return errors
+ def _get_axes(self):
+ return self.axes[0].get_figure().get_axes()
+
+ def _get_axes_layout(self):
+ axes = self._get_axes()
+ x_set = set()
+ y_set = set()
+ for ax in axes:
+ # check axes coordinates to estimate layout
+ points = ax.get_position().get_points()
+ x_set.add(points[0][0])
+ y_set.add(points[0][1])
+ return (len(y_set), len(x_set))
class ScatterPlot(MPLPlot):
_layout_type = 'single'
@@ -3231,6 +3247,28 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
ax = fig.add_subplot(nrows, ncols, i + 1, **kwds)
axarr[i] = ax
+ _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
+
+ if naxes != nplots:
+ for ax in axarr[naxes:]:
+ ax.set_visible(False)
+
+ if squeeze:
+ # Reshape the array to have the final desired dimension (nrow,ncol),
+ # though discarding unneeded dimensions that equal 1. If we only have
+ # one subplot, just return it instead of a 1-element array.
+ if nplots == 1:
+ axes = axarr[0]
+ else:
+ axes = axarr.reshape(nrows, ncols).squeeze()
+ else:
+ # returned axis array will be always 2-d, even if nrows=ncols=1
+ axes = axarr.reshape(nrows, ncols)
+
+ return fig, axes
+
+
+def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey):
if nplots > 1:
if sharex and nrows > 1:
@@ -3241,8 +3279,11 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
# set_visible will not be effective if
# minor axis has NullLocator and NullFormattor (default)
import matplotlib.ticker as ticker
- ax.xaxis.set_minor_locator(ticker.AutoLocator())
- ax.xaxis.set_minor_formatter(ticker.FormatStrFormatter(''))
+
+ if isinstance(ax.xaxis.get_minor_locator(), ticker.NullLocator):
+ ax.xaxis.set_minor_locator(ticker.AutoLocator())
+ if isinstance(ax.xaxis.get_minor_formatter(), ticker.NullFormatter):
+ ax.xaxis.set_minor_formatter(ticker.FormatStrFormatter(''))
for label in ax.get_xticklabels(minor=True):
label.set_visible(False)
except Exception: # pragma no cover
@@ -3255,32 +3296,16 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
label.set_visible(False)
try:
import matplotlib.ticker as ticker
- ax.yaxis.set_minor_locator(ticker.AutoLocator())
- ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter(''))
+ if isinstance(ax.yaxis.get_minor_locator(), ticker.NullLocator):
+ ax.yaxis.set_minor_locator(ticker.AutoLocator())
+ if isinstance(ax.yaxis.get_minor_formatter(), ticker.NullFormatter):
+ ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter(''))
for label in ax.get_yticklabels(minor=True):
label.set_visible(False)
except Exception: # pragma no cover
pass
ax.yaxis.get_label().set_visible(False)
- if naxes != nplots:
- for ax in axarr[naxes:]:
- ax.set_visible(False)
-
- if squeeze:
- # Reshape the array to have the final desired dimension (nrow,ncol),
- # though discarding unneeded dimensions that equal 1. If we only have
- # one subplot, just return it instead of a 1-element array.
- if nplots == 1:
- axes = axarr[0]
- else:
- axes = axarr.reshape(nrows, ncols).squeeze()
- else:
- # returned axis array will be always 2-d, even if nrows=ncols=1
- axes = axarr.reshape(nrows, ncols)
-
- return fig, axes
-
def _flatten(axes):
if not com.is_list_like(axes):
| Fixes #3964
| https://api.github.com/repos/pandas-dev/pandas/pulls/8764 | 2014-11-09T22:48:58Z | 2014-12-07T15:42:01Z | null | 2014-12-07T15:44:01Z |
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 = c;})(document.documentElement);</script>
@@ -233,7 +233,7 @@
#yfi-portfolios-multi-quotes #y-nav, #yfi-portfolios-multi-quotes #navigation, #yfi-portfolios-multi-quotes .y-nav-legobg,
#yfi-portfolios-my-portfolios #y-nav, #yfi-portfolios-my-portfolios #navigation, #yfi-portfolios-my-portfolios .y-nav-legobg {
width : 100%;
- }</style> <div id="yucsHead" class="yucs-finance yucs-en-us yucs-standard"><!-- meta --><div id="yucs-meta" data-authstate="signedout" data-cobrand="standard" data-crumb="zKkBZFoq0zW" data-mc-crumb="mwEkqeuUSA4" data-gta="pBwxu4noueU" data-device="desktop" data-experience="GS" data-firstname="" data-flight="1414419271" data-forcecobrand="standard" data-guid="" data-host="finance.yahoo.com" data-https="1" data-languagetag="en-us" data-property="finance" data-protocol="https" data-shortfirstname="" data-shortuserid="" data-status="active" data-spaceid="2022773886" data-test_id="" data-userid="" data-stickyheader = "true" ></div><!-- /meta --><div id="yucs-comet" style="display:none;"></div><div id="yucs-disclaimer" class="yucs-disclaimer yucs-activate yucs-hide yucs-property-finance yucs-fcb- " data-dsstext="Want a better search experience? {dssLink}Set your Search to Yahoo{linkEnd}" data-dsstext-mobile="Search Less, Find More" data-dsstext-mobile-ok="OK" data-dsstext-mobile-set-search="Set Search to Yahoo" data-ylt-link="https://search.yahoo.com/searchset;_ylt=Al2iNk84ZFe3vsmpMi0sLON.FJF4?pn=" data-ylt-dssbarclose="/;_ylt=ArI_5xVHyNPUdf4.fLphQx1.FJF4" data-ylt-dssbaropen="/;_ylt=Ap.BO.eIp.f8qfRcSlzJKZ9.FJF4" data-linktarget="_top" data-lang="en-us" data-property="finance" data-device="Desktop" data-close-txt="Close this window" data-maybelater-txt = "Maybe Later" data-killswitch = "0" data-host="finance.yahoo.com" data-spaceid="2022773886" data-pn="/GWssfDqkuG" data-pn-tablet="JseRpea9uoa" data-news-search-yahoo-com="wXGsx68W/D." data-answers-search-yahoo-com="ovdLZbRBs8k" data-finance-search-yahoo-com="wV0JNbYUgo8" data-images-search-yahoo-com="Pga0ZzOXrK9" data-video-search-yahoo-com="HDwkSwGTHgJ" data-sports-search-yahoo-com="nA.GKwOLGMk" data-shopping-search-yahoo-com="ypH3fzAKGMA" data-shopping-yahoo-com="ypH3fzAKGMA" data-us-qa-trunk-news-search-yahoo-com ="wXGsx68W/D." data-dss="1"></div> <div id="yucs-top-bar" class='yucs-ps' ><div id='yucs-top-inner'><ul id="yucs-top-list"><li id="yucs-top-home"><a href="https://us.lrd.yahoo.com/_ylt=Am8dqMy96x60X9HHBhDQzqJ.FJF4/SIG=11a4f7jo5/EXP=1414448071/**https%3a//www.yahoo.com/" ><span class="sp yucs-top-ico"></span>Home</a></li><li id="yucs-top-mail"><a href="https://mail.yahoo.com/;_ylt=AqLwjiy5Hwzx45TJ0uVm0UJ.FJF4" >Mail</a></li><li id="yucs-top-news"><a href="http://news.yahoo.com/;_ylt=AjL_fQCS3P1MUS1QNXEob2t.FJF4" >News</a></li><li id="yucs-top-sports"><a href="http://sports.yahoo.com/;_ylt=Ar1LPaA1l9Hh032b3TmzeRl.FJF4" >Sports</a></li><li id="yucs-top-finance"><a href="http://finance.yahoo.com/;_ylt=AvElT3ChO3J0x45gc0n3jPN.FJF4" >Finance</a></li><li id="yucs-top-weather"><a href="https://weather.yahoo.com/;_ylt=Aj3LWBGzRBgXLYY98EuE.L9.FJF4" >Weather</a></li><li id="yucs-top-games"><a href="https://games.yahoo.com/;_ylt=Ar3USsmQ5mkSzm027xEOPPh.FJF4" >Games</a></li><li id="yucs-top-groups"><a href="https://us.lrd.yahoo.com/_ylt=AnafrxtCgneSniG44AXxijd.FJF4/SIG=11d1oojmd/EXP=1414448071/**https%3a//groups.yahoo.com/" >Groups</a></li><li id="yucs-top-answers"><a href="https://answers.yahoo.com/;_ylt=Am0HX64fgfWi0fAgzxkG9RR.FJF4" >Answers</a></li><li id="yucs-top-screen"><a href="https://us.lrd.yahoo.com/_ylt=AtaK_OyxcJAU7CT1W24k7t9.FJF4/SIG=11dop20u0/EXP=1414448071/**https%3a//screen.yahoo.com/" >Screen</a></li><li id="yucs-top-flickr"><a href="https://us.lrd.yahoo.com/_ylt=AhU5xIacF9XKw3IwGX5fDXl.FJF4/SIG=11bsdn4um/EXP=1414448071/**https%3a//www.flickr.com/" >Flickr</a></li><li id="yucs-top-mobile"><a href="https://mobile.yahoo.com/;_ylt=Ag1xuM02KB5z8RyXN.5iHfx.FJF4" >Mobile</a></li><li id='yucs-more' class='yucs-menu yucs-more-activate' data-ylt="/;_ylt=AiSMBpItLuD7SyIkfiuFyop.FJF4"><a href="http://everything.yahoo.com/" id='yucs-more-link'>More<span class="sp yucs-top-ico"></span></a><div id='yucs-top-menu'><div class="yui3-menu-content"><ul class="yucs-hide yucs-leavable"><li id='yucs-top-celebrity'><a href="https://celebrity.yahoo.com/;_ylt=Al2xaAdnqHSMUyAHGv2xsPp.FJF4" >Celebrity</a></li><li id='yucs-top-movies'><a href="https://us.lrd.yahoo.com/_ylt=AjehhuB92yKYpq0TjmYZ.PV.FJF4/SIG=11gv63816/EXP=1414448071/**https%3a//www.yahoo.com/movies" >Movies</a></li><li id='yucs-top-music'><a href="https://music.yahoo.com/;_ylt=AlndKPrX9jW26Eali79vHK5.FJF4" >Music</a></li><li id='yucs-top-tv'><a href="https://tv.yahoo.com/;_ylt=Au3FxGqryuWCm377nSecqax.FJF4" >TV</a></li><li id='yucs-top-health'><a href="https://us.lrd.yahoo.com/_ylt=AoJ9.s3zI0_WnRRRtH304HN.FJF4/SIG=11gu2n09t/EXP=1414448071/**https%3a//www.yahoo.com/health" >Health</a></li><li id='yucs-top-style'><a href="https://us.lrd.yahoo.com/_ylt=AitGWa2ARsSPJvv82IuR0Xt.FJF4/SIG=11fgbb9k0/EXP=1414448071/**https%3a//www.yahoo.com/style" >Style</a></li><li id='yucs-top-beauty'><a href="https://us.lrd.yahoo.com/_ylt=AjnzqmM451CsF3H2amAir1t.FJF4/SIG=11gfsph7k/EXP=1414448071/**https%3a//www.yahoo.com/beauty" >Beauty</a></li><li id='yucs-top-food'><a href="https://us.lrd.yahoo.com/_ylt=AhwK7B46uAgWMBDtfYJhR.t.FJF4/SIG=11ej7qij2/EXP=1414448071/**https%3a//www.yahoo.com/food" >Food</a></li><li id='yucs-top-parenting'><a href="https://us.lrd.yahoo.com/_ylt=An.6FK34dEC5xgLkqXLZW5t.FJF4/SIG=11jove7q7/EXP=1414448071/**https%3a//www.yahoo.com/parenting" >Parenting</a></li><li id='yucs-top-diy'><a href="https://us.lrd.yahoo.com/_ylt=Ao8HEvGRgFHZFk4GRHWtWgd.FJF4/SIG=11dmmjrid/EXP=1414448071/**https%3a//www.yahoo.com/diy" >DIY</a></li><li id='yucs-top-tech'><a href="https://us.lrd.yahoo.com/_ylt=ArqwcASML6I1y1T2Bj553Kl.FJF4/SIG=11esaq4jj/EXP=1414448071/**https%3a//www.yahoo.com/tech" >Tech</a></li><li id='yucs-top-shopping'><a href="http://shopping.yahoo.com/;_ylt=AnCNX8qG_BfYREkAEdvjCk5.FJF4" >Shopping</a></li><li id='yucs-top-travel'><a href="https://us.lrd.yahoo.com/_ylt=AvM8qGXJE97gH4jLGwJXTCx.FJF4/SIG=11gk017pq/EXP=1414448071/**https%3a//www.yahoo.com/travel" >Travel</a></li><li id='yucs-top-autos'><a href="https://autos.yahoo.com/;_ylt=AttKdUI62XHRJ2Wy20Xheml.FJF4" >Autos</a></li><li id='yucs-top-homes'><a href="https://us.lrd.yahoo.com/_ylt=Au0WL33_PHLp1RxtVG9jo41.FJF4/SIG=11liut9r7/EXP=1414448071/**https%3a//homes.yahoo.com/own-rent/" >Homes</a></li></ul></div></div></li></ul></div></div><div id="yucs" class="yucs yucs-mc yog-grid" data-lang="en-us" data-property="finance" data-flight="1414419271" data-linktarget="_top" data-uhvc="/;_ylt=Ak1_N49Q7BHOCDypMNnqnxh.FJF4"> <div class="yucs-fl-left yog-cp"> <div id="yucs-logo"> <style> #yucs #yucs-logo-ani { width:120px ; height:34px; background-image:url(https://s.yimg.com/rz/l/yahoo_finance_en-US_f_pw_119x34.png) ; _background-image:url(https://s.yimg.com/rz/l/yahoo_finance_en-US_f_pw_119x34.gif) ; *left: 0px; display:block ; visibility: visible; position: relative; 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action="https://finance.yahoo.com/q;_ylt=AvQZGc96_yHu76g8yuJy8jR.FJF4" method="get"> <table role="presentation"> <tbody role="presentation"> <tr role="presentation"> <td class="yucs-form-input" role="presentation"> <input autocomplete="off" class="yucs-search-input" name="s" type="search" aria-describedby="mnp-search_box" data-yltvsearch="https://finance.yahoo.com/q;_ylt=AjmPUzYatcChhFCVTZdg0Wh.FJF4" data-yltvsearchsugg="/;_ylt=AknKBVGXSjv1neFQ2zy6ccR.FJF4" data-satype="mini" data-gosurl="https://s.yimg.com/aq/autoc" data-pubid="666" data-enter-ylt="https://finance.yahoo.com/q;_ylt=ApXeLdbOnkuZce1QRT_iP7B.FJF4" data-enter-fr="" data-maxresults="" id="mnp-search_box" data-rapidbucket=""/> </td><td NOWRAP class="yucs-form-btn" role="presentation"><div id="yucs-prop_search_button_wrapper" class="yucs-search-buttons"><div class="yucs-shadow"><div class="yucs-gradient"></div></div><button id="yucs-sprop_button" class="yucs-action_btn yucs-button_theme yucs-vsearch-button" type="submit" data-vfr="uh3_finance_vert_gs" onclick="var vfr = this.getAttribute('data-vfr'); if(vfr){document.getElementById('fr').value = vfr}" data-vsearch="https://finance.yahoo.com/q">Search Finance</button></div><div id="yucs-web_search_button_wrapper" class="yucs-search-buttons"><div class="yucs-shadow"><div class="yucs-gradient"></div></div><button id="yucs-search_button" class="yucs-action_btn yucs-wsearch-button" onclick="var form=document.getElementById('yucs-search').children[0];var wa=form.getAttribute('data-webaction');form.setAttribute('action',wa);var searchbox=document.getElementById('mnp-search_box');searchbox.setAttribute('name','p');" type="submit">Search Web</button></div></td></tr> </tbody> </table> <input type="hidden" id="uhb" name="uhb" value="uhb2" /> <input type="hidden" name="type" value="2button" data-ylk="slk:yhstype-hddn;itc:1;"/> <input type="hidden" id="fr" name="fr" value="uh3_finance_web_gs" /> </form><div id="yucs-satray" class="sa-tray sa-hidden" data-wstext="Search Web for: " data-wsearch="https://search.yahoo.com/search;_ylt=Av30GxTqRj_iF47j07GMbMd.FJF4" data-vfr="uh3_finance_vert_gs" data-vsearchAll="/;_ylt=AtTdhJitRpAGYXo0EKPS3cZ.FJF4" data-vsearch="https://finance.yahoo.com/q;_ylt=AknKBVGXSjv1neFQ2zy6ccR.FJF4" data-vstext= "Search news for: " data-vert_fin_search="https://finance.search.yahoo.com/search/;_ylt=Armtbi_fyne9e6KT4fGbzu5.FJF4"></div> </div></div><div class="yucs-fl-right"> <div id="yucs-profile" class="yucs-profile yucs-signedout"> <a id="yucs-menu_link_profile_signed_out" href="https://login.yahoo.com/config/login;_ylt=AmGq5CTE_HrIpafarS46uEl.FJF4?.src=quote&.intl=us&.lang=en-US&.done=https://finance.yahoo.com/q/op%3fs=AAPL" target="_top" rel="nofollow" class="sp yucs-fc" aria-label="Profile"> </a> <div id="yucs-profile_text" class="yucs-fc"> <a id="yucs-login_signIn" 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data-popup-login-url="https://login.yahoo.com/config/login_verify2?.pd=c%3DOIVaOGq62e5hAP8Tv..nr5E3&.src=sc" data-middleauthtext="You have {count} new messages." data-yltmessage-link="https://us.lrd.yahoo.com/_ylt=Apjs_7Bw4AQ5COKvTbrMDKx.FJF4/SIG=13du204tl/EXP=1415275234/**http%3a//mrd.mail.yahoo.com/msg%3fmid=%7bmsgID%7d%26fid=Inbox%26src=uh%26.crumb=aZBofaj31.n" data-yltviewall-link="https://mail.yahoo.com/;_ylt=AkZXUf33MxPfw4XkZY1bqdF.FJF4" data-yltpanelshown="/;_ylt=AvV9fdBkZu0DRjrOJ1yaV2R.FJF4" data-ylterror="/;_ylt=Aqh5OGjhTlmzeb.Y463yxtN.FJF4" data-ylttimeout="/;_ylt=AjxO6d0TeAd9Jf0SBG2mmUt.FJF4" data-generic-error="We're unable to preview your mail.<br>Go to Mail." data-nosubject="[No Subject]" data-timestamp='short'></div></div> <div id="yucs-help" class="yucs-activate yucs-help yucs-menu_nav"> <a id="yucs-help_button" class="sp yltasis" href="javascript:void(0);" aria-label="Help" rel="nofollow"> <em class="yucs-hide yucs-menu_anchor">Help</em> </a> <div id="yucs-help_inner" class="yucs-hide yucs-menu yucs-hm-activate" data-yltmenushown="/;_ylt=Ah9fa5WrZdRgDLUzOiLLpVl.FJF4"> <span class="sp yucs-dock"></span> <ul id="yuhead-help-panel"> <li><a class="yucs-acct-link" href="https://us.lrd.yahoo.com/_ylt=AlsuZ_6yHv0NDKyfJNNvHG1.FJF4/SIG=15sbagtrs/EXP=1415275234/**https%3a//edit.yahoo.com/mc2.0/eval_profile%3f.intl=us%26.lang=en-US%26.done=https%3a//finance.yahoo.com/q/op%253fs=AAPL%26amp;.src=quote%26amp;.intl=us%26amp;.lang=en-US" target="_top">Account Info</a></li> <li><a href="https://help.yahoo.com/l/us/yahoo/finance/;_ylt=An6G3Wp7BbpG._Y9LLd3VG9.FJF4" rel="nofollow" >Help</a></li> <span class="yucs-separator" role="presentation" style="display: block;"></span><li><a href="https://us.lrd.yahoo.com/_ylt=AuUffeNOhmC6O.nl5UerF0F.FJF4/SIG=11rqao4mv/EXP=1415275234/**http%3a//feedback.yahoo.com/forums/207809" rel="nofollow" >Suggestions</a></li> </ul> </div></div> <div id="yucs-network_link"><a id="yucs-home_link" href="https://us.lrd.yahoo.com/_ylt=AjxTXiLYDPm3IinSDeGVmE9.FJF4/SIG=11a8uhr0m/EXP=1415275234/**https%3a//www.yahoo.com/" rel="nofollow" target="_top"><em class="sp">Yahoo</em><span class="yucs-fc">Home</span></a></div> </div> </div> <!-- contextual_shortcuts --><!-- /contextual_shortcuts --><!-- property: finance | languagetag: en-us | status: active | spaceid: 2022773886 | cobrand: standard | markup: empty --><div id="yucs-location-js" class="yucs-hide yucs-offscreen yucs-location-activate" data-appid="yahoo.locdrop.ucs.desktop" 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'></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'></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. (AAPL)</h2>
}
mod.fullpath = mod.fullpath.replace('{lang}', lang);
return true;
- 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<script>
(function (root) {
@@ -5476,9 +5555,9 @@ <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;
}
@@ -5573,7 +5652,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2>
}
callback();
});
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+}],"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"}};
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var getRequires = loader.getRequires;
loader.getRequires = function (mod) {
@@ -5624,21 +5703,21 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2>
}
mod.fullpath = mod.fullpath.replace('{lang}', lang);
return true;
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@@ -5757,7 +5836,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2>
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diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py
index 549a60a85e25e..0047bc855e446 100644
--- a/pandas/io/tests/test_data.py
+++ b/pandas/io/tests/test_data.py
@@ -354,7 +354,8 @@ def test_chop_out_of_strike_range(self):
@network
def test_sample_page_price_quote_time2(self):
- #Tests the weekday quote time format
+ #Tests the EDT page format
+ #regression test for #8741
price, quote_time = self.aapl._get_underlying_price(self.html2)
self.assertIsInstance(price, (int, float, complex))
self.assertIsInstance(quote_time, (datetime, Timestamp))
| 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 |
BUG: Bug in slicing a multi-index level with an empty-list (GH8737) | diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt
index ab1be57934ce7..5ba41b3bd62d4 100644
--- a/doc/source/whatsnew/v0.15.1.txt
+++ b/doc/source/whatsnew/v0.15.1.txt
@@ -246,7 +246,7 @@ Bug Fixes
- 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`)
-
+- Bug in slicing a multi-index level with an empty-list (:issue:`8737`)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 154410bbebf01..a6907c3f8b5f2 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -4147,8 +4147,10 @@ def _convert_indexer(r):
# ignore not founds
continue
-
- ranges.append(reduce(np.logical_or,indexers))
+ if len(k):
+ ranges.append(reduce(np.logical_or,indexers))
+ else:
+ ranges.append(tuple([]))
elif _is_null_slice(k):
# empty slice
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index 32b27e139cc21..f41e1ac03f993 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -2133,6 +2133,16 @@ def f():
result = s.loc[idx[:,['foo','bah']]]
assert_series_equal(result,expected)
+ # GH 8737
+ # empty indexer
+ multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
+ df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index)
+ df = df.sortlevel(0, axis=1)
+
+ result = df.loc[:, ([], slice(None))]
+ expected = DataFrame(index=range(5),columns=multi_index.reindex([])[0])
+ assert_frame_equal(result, expected)
+
# regression from < 0.14.0
# GH 7914
df = DataFrame([[np.mean, np.median],['mean','median']],
| closes #8737
```
In [5]: multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
In [6]: df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index).sortlevel(0,axis=1)
In [7]: df
Out[7]:
bar baz foo
alpha beta alpha beta alpha beta
0 -0.725086 -0.715498 1.141087 -0.586018 0.906581 -0.260218
1 -1.111794 -0.393149 2.101567 0.648850 0.366892 0.554206
2 1.258239 1.472659 -0.174970 -0.355639 -0.843127 1.101716
3 -0.775790 0.419361 -0.521444 1.245519 0.759791 0.183877
4 -0.001001 -1.526741 -1.721501 0.527531 0.407099 -0.415245
In [8]: df.loc[:, ([], slice(None))]
Out[8]:
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4]
In [9]: df.loc[:, ([], slice(None))].columns
Out[9]:
MultiIndex(levels=[[u'bar', u'baz', u'foo'], [u'alpha', u'beta']],
labels=[[], []])
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/8739 | 2014-11-05T20:39:06Z | 2014-11-05T21:21:15Z | null | 2014-11-05T21:21:15Z |
BUG: The 'jobComplete' key may be present but False in the BigQuery query results | diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt
index 18c55e38ab7af..be785d8cdb97e 100644
--- a/doc/source/whatsnew/v0.16.0.txt
+++ b/doc/source/whatsnew/v0.16.0.txt
@@ -197,3 +197,5 @@ Bug Fixes
- Fixed issue in the ``xlsxwriter`` engine where it added a default 'General' format to cells if no other format wass applied. This prevented other row or column formatting being applied. (:issue:`9167`)
- Fixes issue with ``index_col=False`` when ``usecols`` is also specified in ``read_csv``. (:issue:`9082`)
- Bug where ``wide_to_long`` would modify the input stubnames list (:issue:`9204`)
+
+- Bug in the Google BigQuery reader where the 'jobComplete' key may be present but False in the query results (:issue:`8728`)
diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py
index 572a8be5c65e8..5b4f37098a8bf 100644
--- a/pandas/io/gbq.py
+++ b/pandas/io/gbq.py
@@ -185,7 +185,7 @@ def run_query(self, query):
job_reference = query_reply['jobReference']
- while(not 'jobComplete' in query_reply):
+ while(not query_reply.get('jobComplete', False)):
print('Job not yet complete...')
query_reply = job_collection.getQueryResults(
projectId=job_reference['projectId'],
| xref #9141
Fixes an issue when looking for 'totalRows' on a large dataset that is not finished in between two subsequent checks.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8728 | 2014-11-04T16:35:56Z | 2015-03-06T22:59:19Z | null | 2015-03-06T22:59:19Z |
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 | |
ENH: read_csv dialect parameter can take a string (GH8703) | diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt
index e4fc085d4b16a..e628829196e95 100644
--- a/doc/source/whatsnew/v0.15.1.txt
+++ b/doc/source/whatsnew/v0.15.1.txt
@@ -213,6 +213,7 @@ Bug Fixes
- 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`)
+- Bug in ``read_csv``, ``dialect`` parameter would not take a string (:issue: `8703`)
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 2034d4fa899ce..8f8e3151d56e6 100644
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -526,6 +526,8 @@ def __init__(self, f, engine=None, **kwds):
if kwds.get('dialect') is not None:
dialect = kwds['dialect']
+ if dialect in csv.list_dialects():
+ dialect = csv.get_dialect(dialect)
kwds['delimiter'] = dialect.delimiter
kwds['doublequote'] = dialect.doublequote
kwds['escapechar'] = dialect.escapechar
diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py
index 44daeaca4ed32..8440d45ffb4be 100644
--- a/pandas/io/tests/test_parsers.py
+++ b/pandas/io/tests/test_parsers.py
@@ -191,6 +191,21 @@ def test_dialect(self):
exp.replace('a', '"a', inplace=True)
tm.assert_frame_equal(df, exp)
+ def test_dialect_str(self):
+ data = """\
+fruit:vegetable
+apple:brocolli
+pear:tomato
+"""
+ exp = DataFrame({
+ 'fruit': ['apple', 'pear'],
+ 'vegetable': ['brocolli', 'tomato']
+ })
+ dia = csv.register_dialect('mydialect', delimiter=':')
+ df = self.read_csv(StringIO(data), dialect='mydialect')
+ tm.assert_frame_equal(df, exp)
+ csv.unregister_dialect('mydialect')
+
def test_1000_sep(self):
data = """A|B|C
1|2,334|5
| Closes #8703
I wasn't sure where to put the test, hopefully this is right.
Follow-up of #8718
| https://api.github.com/repos/pandas-dev/pandas/pulls/8719 | 2014-11-03T03:26:28Z | 2014-11-05T21:18:37Z | null | 2014-11-05T21:18:37Z |
ENH: read_csv dialect parameter can take a string (GH8703) | diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 2034d4fa899ce..8f8e3151d56e6 100644
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -526,6 +526,8 @@ def __init__(self, f, engine=None, **kwds):
if kwds.get('dialect') is not None:
dialect = kwds['dialect']
+ if dialect in csv.list_dialects():
+ dialect = csv.get_dialect(dialect)
kwds['delimiter'] = dialect.delimiter
kwds['doublequote'] = dialect.doublequote
kwds['escapechar'] = dialect.escapechar
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 1aa734c4834de..6cb80bf03172e 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -5982,6 +5982,15 @@ def _check_df(df,cols=None):
cols = ['b','a']
_check_df(df,cols)
+ def test_read_csv_dialect(self):
+ df = pd.DataFrame({'fruit':['apple'], 'vegetable':['broccoli']})
+ csv.register_dialect("mydialect", delimiter="\t")
+ with ensure_clean() as path:
+ df.to_csv(path, sep='\t')
+ rc = pd.read_csv(path, dialect='mydialect', index_col=0)
+ assert_frame_equal(df, rc)
+ csv.unregister_dialect("mydialect")
+
@slow
def test_to_csv_moar(self):
path = '__tmp_to_csv_moar__'
| closes #8703
| https://api.github.com/repos/pandas-dev/pandas/pulls/8718 | 2014-11-03T00:11:19Z | 2014-11-03T00:14:55Z | null | 2014-11-03T08:21:13Z |
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 |
WIP/API/ENH: IntervalIndex | diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index e5347f03b5462..5ff780b5e5593 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -262,10 +262,9 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
if bins is not None:
try:
- cat, bins = cut(values, bins, retbins=True)
+ values, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
- values = cat.codes
if com.is_categorical_dtype(values.dtype):
result = values.value_counts(dropna)
@@ -320,11 +319,6 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
keys = Index(keys)
result = Series(counts, index=keys, name=name)
- if bins is not None:
- # TODO: This next line should be more efficient
- result = result.reindex(np.arange(len(cat.categories)), fill_value=0)
- result.index = bins[:-1]
-
if sort:
result = result.sort_values(ascending=ascending)
diff --git a/pandas/core/api.py b/pandas/core/api.py
index e2ac57e37cba6..8c446b0922e72 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -9,6 +9,7 @@
from pandas.core.groupby import Grouper
from pandas.core.format import set_eng_float_format
from pandas.core.index import Index, CategoricalIndex, Int64Index, Float64Index, MultiIndex
+from pandas.core.interval import Interval, IntervalIndex
from pandas.core.series import Series, TimeSeries
from pandas.core.frame import DataFrame
diff --git a/pandas/core/common.py b/pandas/core/common.py
index e81b58a3f7eef..ddc74b7b34899 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -83,6 +83,7 @@ def _check(cls, inst):
ABCTimedeltaIndex = create_pandas_abc_type("ABCTimedeltaIndex", "_typ", ("timedeltaindex",))
ABCPeriodIndex = create_pandas_abc_type("ABCPeriodIndex", "_typ", ("periodindex",))
ABCCategoricalIndex = create_pandas_abc_type("ABCCategoricalIndex", "_typ", ("categoricalindex",))
+ABCIntervalIndex = create_pandas_abc_type("ABCIntervalIndex", "_typ", ("intervalindex",))
ABCIndexClass = create_pandas_abc_type("ABCIndexClass", "_typ", ("index",
"int64index",
"float64index",
@@ -90,8 +91,9 @@ def _check(cls, inst):
"datetimeindex",
"timedeltaindex",
"periodindex",
- "categoricalindex"))
-
+ "categoricalindex",
+ "intervalindex"))
+ABCInterval = create_pandas_abc_type("ABCInterval", "_typ", ("interval",))
ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series",))
ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe",))
ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel",))
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 584b946d47618..0aa490275c0c3 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -17,6 +17,7 @@
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.index import Index, MultiIndex, CategoricalIndex, _ensure_index
+from pandas.core.interval import IntervalIndex
from pandas.core.internals import BlockManager, make_block
from pandas.core.series import Series
from pandas.core.panel import Panel
@@ -2735,12 +2736,20 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
if bins is None:
lab, lev = algos.factorize(val, sort=True)
else:
- cat, bins = cut(val, bins, retbins=True)
+ raise NotImplementedError('this is broken')
+ lab, bins = cut(val, bins, retbins=True)
# bins[:-1] for backward compat;
# o.w. cat.categories could be better
- lab, lev, dropna = cat.codes, bins[:-1], False
-
- sorter = np.lexsort((lab, ids))
+ # cat = Categorical(cat)
+ # lab, lev, dropna = cat.codes, bins[:-1], False
+
+ if (lab.dtype == object
+ and lib.is_interval_array_fixed_closed(lab[notnull(lab)])):
+ lab_index = Index(lab)
+ assert isinstance(lab, IntervalIndex)
+ sorter = np.lexsort((lab_index.left, lab_index.right, ids))
+ else:
+ sorter = np.lexsort((lab, ids))
ids, lab = ids[sorter], lab[sorter]
# group boundaries are where group ids change
@@ -2771,12 +2780,13 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
acc = rep(np.diff(np.r_[idx, len(ids)]))
out /= acc[mask] if dropna else acc
- if sort and bins is None:
+ if sort: # and bins is None:
cat = ids[inc][mask] if dropna else ids[inc]
sorter = np.lexsort((out if ascending else -out, cat))
out, labels[-1] = out[sorter], labels[-1][sorter]
- if bins is None:
+ # if bins is None:
+ if True:
mi = MultiIndex(levels=levels, labels=labels, names=names,
verify_integrity=False)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 1433d755d294d..26a0cd8e4f0b5 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -173,6 +173,9 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False,
return Int64Index(subarr.astype('i8'), copy=copy, name=name)
elif inferred in ['floating', 'mixed-integer-float']:
return Float64Index(subarr, copy=copy, name=name)
+ elif inferred == 'interval':
+ from pandas.core.interval import IntervalIndex
+ return IntervalIndex.from_intervals(subarr, name=name)
elif inferred == 'boolean':
# don't support boolean explicity ATM
pass
@@ -829,7 +832,7 @@ def _mpl_repr(self):
@property
def is_monotonic(self):
""" alias for is_monotonic_increasing (deprecated) """
- return self._engine.is_monotonic_increasing
+ return self.is_monotonic_increasing
@property
def is_monotonic_increasing(self):
@@ -1633,7 +1636,7 @@ def union(self, other):
def _wrap_union_result(self, other, result):
name = self.name if self.name == other.name else None
- return self.__class__(data=result, name=name)
+ return self._constructor(data=result, name=name)
def intersection(self, other):
"""
@@ -2671,6 +2674,13 @@ def _searchsorted_monotonic(self, label, side='left'):
raise ValueError('index must be monotonic increasing or decreasing')
+ def _get_loc_only_exact_matches(self, key):
+ """
+ This is overriden on subclasses (namely, IntervalIndex) to control
+ get_slice_bound.
+ """
+ return self.get_loc(key)
+
def get_slice_bound(self, label, side, kind):
"""
Calculate slice bound that corresponds to given label.
@@ -2698,7 +2708,7 @@ def get_slice_bound(self, label, side, kind):
# we need to look up the label
try:
- slc = self.get_loc(label)
+ slc = self._get_loc_only_exact_matches(label)
except KeyError as err:
try:
return self._searchsorted_monotonic(label, side)
diff --git a/pandas/core/interval.py b/pandas/core/interval.py
new file mode 100644
index 0000000000000..68e07f21367a0
--- /dev/null
+++ b/pandas/core/interval.py
@@ -0,0 +1,521 @@
+import operator
+
+import numpy as np
+import pandas as pd
+
+from pandas.core.base import PandasObject, IndexOpsMixin
+from pandas.core.common import (_values_from_object, _ensure_platform_int,
+ notnull, is_datetime_or_timedelta_dtype,
+ is_integer_dtype, is_float_dtype)
+from pandas.core.index import (Index, _ensure_index, default_pprint,
+ InvalidIndexError, MultiIndex)
+from pandas.lib import (Interval, IntervalMixin, IntervalTree,
+ interval_bounds_to_intervals,
+ intervals_to_interval_bounds)
+from pandas.util.decorators import cache_readonly
+import pandas.core.common as com
+
+
+_VALID_CLOSED = set(['left', 'right', 'both', 'neither'])
+
+
+def _get_next_label(label):
+ dtype = getattr(label, 'dtype', type(label))
+ if isinstance(label, (pd.Timestamp, pd.Timedelta)):
+ dtype = 'datetime64'
+ if is_datetime_or_timedelta_dtype(dtype):
+ return label + np.timedelta64(1, 'ns')
+ elif is_integer_dtype(dtype):
+ return label + 1
+ elif is_float_dtype(dtype):
+ return np.nextafter(label, np.infty)
+ else:
+ raise TypeError('cannot determine next label for type %r'
+ % type(label))
+
+
+def _get_prev_label(label):
+ dtype = getattr(label, 'dtype', type(label))
+ if isinstance(label, (pd.Timestamp, pd.Timedelta)):
+ dtype = 'datetime64'
+ if is_datetime_or_timedelta_dtype(dtype):
+ return label - np.timedelta64(1, 'ns')
+ elif is_integer_dtype(dtype):
+ return label - 1
+ elif is_float_dtype(dtype):
+ return np.nextafter(label, -np.infty)
+ else:
+ raise TypeError('cannot determine next label for type %r'
+ % type(label))
+
+
+def _get_interval_closed_bounds(interval):
+ """
+ Given an Interval or IntervalIndex, return the corresponding interval with
+ closed bounds.
+ """
+ left, right = interval.left, interval.right
+ if interval.open_left:
+ left = _get_next_label(left)
+ if interval.open_right:
+ right = _get_prev_label(right)
+ return left, right
+
+
+class IntervalIndex(IntervalMixin, Index):
+ """
+ Immutable Index implementing an ordered, sliceable set. IntervalIndex
+ represents an Index of intervals that are all closed on the same side.
+
+ .. versionadded:: 0.18
+
+ Properties
+ ----------
+ left, right : array-like (1-dimensional)
+ Left and right bounds for each interval.
+ closed : {'left', 'right', 'both', 'neither'}, optional
+ Whether the intervals are closed on the left-side, right-side, both or
+ neither. Defaults to 'right'.
+ name : object, optional
+ Name to be stored in the index.
+ """
+ _typ = 'intervalindex'
+ _comparables = ['name']
+ _attributes = ['name', 'closed']
+ _allow_index_ops = True
+ _engine = None # disable it
+
+ def __new__(cls, left, right, closed='right', name=None, fastpath=False):
+ # TODO: validation
+ result = IntervalMixin.__new__(cls)
+ result._left = _ensure_index(left)
+ result._right = _ensure_index(right)
+ result._closed = closed
+ result.name = name
+ if not fastpath:
+ result._validate()
+ result._reset_identity()
+ return result
+
+ def _validate(self):
+ """Verify that the IntervalIndex is valid.
+ """
+ # TODO: exclude periods?
+ if self.closed not in _VALID_CLOSED:
+ raise ValueError("invalid options for 'closed': %s" % self.closed)
+ if len(self.left) != len(self.right):
+ raise ValueError('left and right must have the same length')
+ left_valid = notnull(self.left)
+ right_valid = notnull(self.right)
+ if not (left_valid == right_valid).all():
+ raise ValueError('missing values must be missing in the same '
+ 'location both left and right sides')
+ if not (self.left[left_valid] <= self.right[left_valid]).all():
+ raise ValueError('left side of interval must be <= right side')
+
+ def _simple_new(cls, values, name=None, **kwargs):
+ # ensure we don't end up here (this is a superclass method)
+ raise NotImplementedError
+
+ def _cleanup(self):
+ pass
+
+ @property
+ def _engine(self):
+ raise NotImplementedError
+
+ @cache_readonly
+ def _tree(self):
+ return IntervalTree(self.left, self.right, closed=self.closed)
+
+ @property
+ def _constructor(self):
+ return type(self).from_intervals
+
+ @classmethod
+ def from_breaks(cls, breaks, closed='right', name=None):
+ """
+ Construct an IntervalIndex from an array of splits
+
+ Parameters
+ ----------
+ breaks : array-like (1-dimensional)
+ Left and right bounds for each interval.
+ closed : {'left', 'right', 'both', 'neither'}, optional
+ Whether the intervals are closed on the left-side, right-side, both
+ or neither. Defaults to 'right'.
+ name : object, optional
+ Name to be stored in the index.
+
+ Examples
+ --------
+
+ >>> IntervalIndex.from_breaks([0, 1, 2, 3])
+ IntervalIndex(left=[0, 1, 2],
+ right=[1, 2, 3],
+ closed='right')
+ """
+ return cls(breaks[:-1], breaks[1:], closed, name)
+
+ @classmethod
+ def from_intervals(cls, data, name=None):
+ """
+ Construct an IntervalIndex from a 1d array of Interval objects
+
+ Parameters
+ ----------
+ data : array-like (1-dimensional)
+ Array of Interval objects. All intervals must be closed on the same
+ sides.
+ name : object, optional
+ Name to be stored in the index.
+
+ Examples
+ --------
+
+ >>> IntervalIndex.from_intervals([Interval(0, 1), Interval(1, 2)])
+ IntervalIndex(left=[0, 1],
+ right=[1, 2],
+ closed='right')
+
+ The generic Index constructor work identically when it infers an array
+ of all intervals:
+
+ >>> Index([Interval(0, 1), Interval(1, 2)])
+ IntervalIndex(left=[0, 1],
+ right=[1, 2],
+ closed='right')
+ """
+ data = np.asarray(data)
+ left, right, closed = intervals_to_interval_bounds(data)
+ return cls(left, right, closed, name)
+
+ @classmethod
+ def from_tuples(cls, data, closed='right', name=None):
+ left = []
+ right = []
+ for l, r in data:
+ left.append(l)
+ right.append(r)
+ return cls(np.array(left), np.array(right), closed, name)
+
+ def to_tuples(self):
+ return Index(com._asarray_tuplesafe(zip(self.left, self.right)))
+
+ @cache_readonly
+ def _multiindex(self):
+ return MultiIndex.from_arrays([self.left, self.right],
+ names=['left', 'right'])
+
+ @property
+ def left(self):
+ return self._left
+
+ @property
+ def right(self):
+ return self._right
+
+ @property
+ def closed(self):
+ return self._closed
+
+ def __len__(self):
+ return len(self.left)
+
+ @cache_readonly
+ def values(self):
+ """Returns the IntervalIndex's data as a numpy array of Interval
+ objects (with dtype='object')
+ """
+ left = np.asarray(self.left)
+ right = np.asarray(self.right)
+ return interval_bounds_to_intervals(left, right, self.closed)
+
+ def __array__(self, result=None):
+ """ the array interface, return my values """
+ return self.values
+
+ def __array_wrap__(self, result, context=None):
+ # we don't want the superclass implementation
+ return result
+
+ def _array_values(self):
+ return self.values
+
+ def __reduce__(self):
+ return self.__class__, (self.left, self.right, self.closed, self.name)
+
+ def _shallow_copy(self, values=None, name=None):
+ name = name if name is not None else self.name
+ if values is not None:
+ return type(self).from_intervals(values, name=name)
+ else:
+ return self.copy(name=name)
+
+ def copy(self, deep=False, name=None):
+ left = self.left.copy(deep=True) if deep else self.left
+ right = self.right.copy(deep=True) if deep else self.right
+ name = name if name is not None else self.name
+ return type(self)(left, right, closed=self.closed, name=name,
+ fastpath=True)
+
+ @cache_readonly
+ def dtype(self):
+ return np.dtype('O')
+
+ @cache_readonly
+ def mid(self):
+ """Returns the mid-point of each interval in the index as an array
+ """
+ try:
+ return Index(0.5 * (self.left.values + self.right.values))
+ except TypeError:
+ # datetime safe version
+ delta = self.right.values - self.left.values
+ return Index(self.left.values + 0.5 * delta)
+
+ @cache_readonly
+ def is_monotonic_increasing(self):
+ return self._multiindex.is_monotonic_increasing
+
+ @cache_readonly
+ def is_monotonic_decreasing(self):
+ return self._multiindex.is_monotonic_decreasing
+
+ @cache_readonly
+ def is_unique(self):
+ return self._multiindex.is_unique
+
+ @cache_readonly
+ def is_non_overlapping_monotonic(self):
+ # must be increasing (e.g., [0, 1), [1, 2), [2, 3), ... )
+ # or decreasing (e.g., [-1, 0), [-2, -1), [-3, -2), ...)
+ # we already require left <= right
+ return ((self.right[:-1] <= self.left[1:]).all() or
+ (self.left[:-1] >= self.right[1:]).all())
+
+ def _convert_scalar_indexer(self, key, kind=None):
+ return key
+
+ def _maybe_cast_slice_bound(self, label, side, kind):
+ return getattr(self, side)._maybe_cast_slice_bound(label, side, kind)
+
+ def _convert_list_indexer(self, keyarr, kind=None):
+ """
+ we are passed a list indexer.
+ Return our indexer or raise if all of the values are not included in the categories
+ """
+ locs = self.get_indexer(keyarr)
+ # TODO: handle keyarr if it includes intervals
+ if (locs == -1).any():
+ raise KeyError("a list-indexer must only include existing intervals")
+
+ return locs
+
+ def _check_method(self, method):
+ if method is not None:
+ raise NotImplementedError(
+ 'method %r not yet implemented for IntervalIndex' % method)
+
+ def _searchsorted_monotonic(self, label, side, exclude_label=False):
+ if not self.is_non_overlapping_monotonic:
+ raise KeyError('can only get slices from an IntervalIndex if '
+ 'bounds are non-overlapping and all monotonic '
+ 'increasing or decreasing')
+
+ if isinstance(label, IntervalMixin):
+ raise NotImplementedError
+
+ if ((side == 'left' and self.left.is_monotonic_increasing) or
+ (side == 'right' and self.left.is_monotonic_decreasing)):
+ sub_idx = self.right
+ if self.open_right or exclude_label:
+ label = _get_next_label(label)
+ else:
+ sub_idx = self.left
+ if self.open_left or exclude_label:
+ label = _get_prev_label(label)
+
+ return sub_idx._searchsorted_monotonic(label, side)
+
+ def _get_loc_only_exact_matches(self, key):
+ return self._multiindex._tuple_index.get_loc(key)
+
+ def _find_non_overlapping_monotonic_bounds(self, key):
+ if isinstance(key, IntervalMixin):
+ start = self._searchsorted_monotonic(
+ key.left, 'left', exclude_label=key.open_left)
+ stop = self._searchsorted_monotonic(
+ key.right, 'right', exclude_label=key.open_right)
+ else:
+ # scalar
+ start = self._searchsorted_monotonic(key, 'left')
+ stop = self._searchsorted_monotonic(key, 'right')
+ return start, stop
+
+ def get_loc(self, key, method=None):
+ self._check_method(method)
+
+ original_key = key
+
+ if self.is_non_overlapping_monotonic:
+ if isinstance(key, Interval):
+ left = self._maybe_cast_slice_bound(key.left, 'left', None)
+ right = self._maybe_cast_slice_bound(key.right, 'right', None)
+ key = Interval(left, right, key.closed)
+ else:
+ key = self._maybe_cast_slice_bound(key, 'left', None)
+
+ start, stop = self._find_non_overlapping_monotonic_bounds(key)
+
+ if start + 1 == stop:
+ return start
+ elif start < stop:
+ return slice(start, stop)
+ else:
+ raise KeyError(original_key)
+
+ else:
+ # use the interval tree
+ if isinstance(key, Interval):
+ left, right = _get_interval_closed_bounds(key)
+ return self._tree.get_loc_interval(left, right)
+ else:
+ return self._tree.get_loc(key)
+
+ def get_value(self, series, key):
+ # this method seems necessary for Series.__getitem__ but I have no idea
+ # what it should actually do here...
+ loc = self.get_loc(key) # nb. this can't handle slice objects
+ return series.iloc[loc]
+
+ def get_indexer(self, target, method=None, limit=None, tolerance=None):
+ self._check_method(method)
+ target = _ensure_index(target)
+
+ if self.is_non_overlapping_monotonic:
+ start, stop = self._find_non_overlapping_monotonic_bounds(target)
+
+ start_plus_one = start + 1
+ if (start_plus_one < stop).any():
+ raise ValueError('indexer corresponds to non-unique elements')
+ return np.where(start_plus_one == stop, start, -1)
+
+ else:
+ if isinstance(target, IntervalIndex):
+ raise NotImplementedError(
+ 'have not yet implemented get_indexer '
+ 'for IntervalIndex indexers')
+ else:
+ return self._tree.get_indexer(target)
+
+ def delete(self, loc):
+ new_left = self.left.delete(loc)
+ new_right = self.right.delete(loc)
+ return type(self)(new_left, new_right, self.closed, self.name,
+ fastpath=True)
+
+ def insert(self, loc, item):
+ if not isinstance(item, Interval):
+ raise ValueError('can only insert Interval objects into an '
+ 'IntervalIndex')
+ if not item.closed == self.closed:
+ raise ValueError('inserted item must be closed on the same side '
+ 'as the index')
+ new_left = self.left.insert(loc, item.left)
+ new_right = self.right.insert(loc, item.right)
+ return type(self)(new_left, new_right, self.closed, self.name,
+ fastpath=True)
+
+ def _as_like_interval_index(self, other, error_msg):
+ self._assert_can_do_setop(other)
+ other = _ensure_index(other)
+ if (not isinstance(other, IntervalIndex) or
+ self.closed != other.closed):
+ raise ValueError(error_msg)
+ return other
+
+ def append(self, other):
+ msg = ('can only append two IntervalIndex objects that are closed on '
+ 'the same side')
+ other = self._as_like_interval_index(other, msg)
+ new_left = self.left.append(other.left)
+ new_right = self.right.append(other.right)
+ if other.name is not None and other.name != self.name:
+ name = None
+ else:
+ name = self.name
+ return type(self)(new_left, new_right, self.closed, name,
+ fastpath=True)
+
+ def take(self, indexer, axis=0):
+ indexer = com._ensure_platform_int(indexer)
+ new_left = self.left.take(indexer)
+ new_right = self.right.take(indexer)
+ return type(self)(new_left, new_right, self.closed, self.name,
+ fastpath=True)
+
+ def __contains__(self, key):
+ try:
+ self.get_loc(key)
+ return True
+ except KeyError:
+ return False
+
+ def __getitem__(self, value):
+ left = self.left[value]
+ right = self.right[value]
+ if not isinstance(left, Index):
+ return Interval(left, right, self.closed)
+ else:
+ return type(self)(left, right, self.closed, self.name)
+
+ # __repr__ associated methods are based on MultiIndex
+
+ def _format_attrs(self):
+ attrs = [('left', default_pprint(self.left)),
+ ('right', default_pprint(self.right)),
+ ('closed', repr(self.closed))]
+ if self.name is not None:
+ attrs.append(('name', default_pprint(self.name)))
+ return attrs
+
+ def _format_space(self):
+ return "\n%s" % (' ' * (len(self.__class__.__name__) + 1))
+
+ def _format_data(self):
+ return None
+
+ def argsort(self, *args, **kwargs):
+ return np.lexsort((self.right, self.left))
+
+ def equals(self, other):
+ if self.is_(other):
+ return True
+ try:
+ return (self.left.equals(other.left)
+ and self.right.equals(other.right)
+ and self.closed == other.closed)
+ except AttributeError:
+ return False
+
+ def _setop(op_name):
+ def func(self, other):
+ msg = ('can only do set operations between two IntervalIndex '
+ 'objects that are closed on the same side')
+ other = self._as_like_interval_index(other, msg)
+ result = getattr(self._multiindex, op_name)(other._multiindex)
+ result_name = self.name if self.name == other.name else None
+ return type(self).from_tuples(result.values, closed=self.closed,
+ name=result_name)
+ return func
+
+ union = _setop('union')
+ intersection = _setop('intersection')
+ difference = _setop('difference')
+ sym_diff = _setop('sym_diff')
+
+ # TODO: arithmetic operations
+
+
+IntervalIndex._add_logical_methods_disabled()
diff --git a/pandas/hashtable.pxd b/pandas/hashtable.pxd
index 97b6687d061e9..593d8b8a1833a 100644
--- a/pandas/hashtable.pxd
+++ b/pandas/hashtable.pxd
@@ -1,4 +1,5 @@
from khash cimport kh_int64_t, kh_float64_t, kh_pymap_t, int64_t, float64_t
+from numpy cimport ndarray
# prototypes for sharing
@@ -22,3 +23,16 @@ cdef class PyObjectHashTable(HashTable):
cpdef get_item(self, object val)
cpdef set_item(self, object key, Py_ssize_t val)
+
+cdef struct Int64VectorData:
+ int64_t *data
+ size_t n, m
+
+cdef class Int64Vector:
+ cdef Int64VectorData *data
+ cdef ndarray ao
+
+ cdef resize(self)
+ cpdef to_array(self)
+ cdef inline void append(self, int64_t x)
+ cdef extend(self, int64_t[:] x)
diff --git a/pandas/hashtable.pyx b/pandas/hashtable.pyx
index dfa7930ada62f..c5fb65c1acee7 100644
--- a/pandas/hashtable.pyx
+++ b/pandas/hashtable.pyx
@@ -96,11 +96,8 @@ cdef void append_data(vector_data *data, sixty_four_bit_scalar x) nogil:
data.data[data.n] = x
data.n += 1
-cdef class Int64Vector:
- cdef:
- Int64VectorData *data
- ndarray ao
+cdef class Int64Vector:
def __cinit__(self):
self.data = <Int64VectorData *>PyMem_Malloc(sizeof(Int64VectorData))
@@ -122,7 +119,7 @@ cdef class Int64Vector:
def __len__(self):
return self.data.n
- def to_array(self):
+ cpdef to_array(self):
self.ao.resize(self.data.n)
self.data.m = self.data.n
return self.ao
@@ -134,6 +131,11 @@ cdef class Int64Vector:
append_data(self.data, x)
+ cdef extend(self, int64_t[:] x):
+ for i in range(len(x)):
+ self.append(x[i])
+
+
cdef class Float64Vector:
cdef:
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index f7978c4791538..34eb954615803 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -306,6 +306,7 @@ def isscalar(object val):
- instances of datetime.datetime
- instances of datetime.timedelta
- Period
+ - Interval
"""
@@ -316,7 +317,8 @@ def isscalar(object val):
or PyDate_Check(val)
or PyDelta_Check(val)
or PyTime_Check(val)
- or util.is_period_object(val))
+ or util.is_period_object(val)
+ or is_interval(val))
def item_from_zerodim(object val):
@@ -1896,4 +1898,6 @@ cdef class BlockPlacement:
include "reduce.pyx"
include "properties.pyx"
+include "interval.pyx"
+include "intervaltree.pyx"
include "inference.pyx"
diff --git a/pandas/src/generate_intervaltree.py b/pandas/src/generate_intervaltree.py
new file mode 100644
index 0000000000000..275a0d40e2433
--- /dev/null
+++ b/pandas/src/generate_intervaltree.py
@@ -0,0 +1,395 @@
+"""
+This file generates `intervaltree.pyx` which is then included in `../lib.pyx`
+during building. To regenerate `intervaltree.pyx`, just run:
+
+ `python generate_intervaltree.py`.
+"""
+from __future__ import print_function
+import os
+from pandas.compat import StringIO
+import numpy as np
+
+
+warning_to_new_contributors = """
+# DO NOT EDIT THIS FILE: This file was autogenerated from
+# generate_intervaltree.py, so please edit that file and then run
+# `python2 generate_intervaltree.py` to re-generate this file.
+"""
+
+header = r'''
+from numpy cimport int64_t, float64_t
+from numpy cimport ndarray, PyArray_ArgSort, NPY_QUICKSORT, PyArray_Take
+import numpy as np
+
+cimport cython
+cimport numpy as cnp
+cnp.import_array()
+
+from hashtable cimport Int64Vector, Int64VectorData
+
+
+ctypedef fused scalar64_t:
+ float64_t
+ int64_t
+
+
+NODE_CLASSES = {}
+
+
+cdef class IntervalTree(IntervalMixin):
+ """A centered interval tree
+
+ Based off the algorithm described on Wikipedia:
+ http://en.wikipedia.org/wiki/Interval_tree
+ """
+ cdef:
+ readonly object left, right, root
+ readonly str closed
+ object _left_sorter, _right_sorter
+
+ def __init__(self, left, right, closed='right', leaf_size=100):
+ """
+ Parameters
+ ----------
+ left, right : np.ndarray[ndim=1]
+ Left and right bounds for each interval. Assumed to contain no
+ NaNs.
+ closed : {'left', 'right', 'both', 'neither'}, optional
+ Whether the intervals are closed on the left-side, right-side, both
+ or neither. Defaults to 'right'.
+ leaf_size : int, optional
+ Parameter that controls when the tree switches from creating nodes
+ to brute-force search. Tune this parameter to optimize query
+ performance.
+ """
+ if closed not in ['left', 'right', 'both', 'neither']:
+ raise ValueError("invalid option for 'closed': %s" % closed)
+
+ left = np.asarray(left)
+ right = np.asarray(right)
+ dtype = np.result_type(left, right)
+ self.left = np.asarray(left, dtype=dtype)
+ self.right = np.asarray(right, dtype=dtype)
+
+ indices = np.arange(len(left), dtype='int64')
+
+ self.closed = closed
+
+ node_cls = NODE_CLASSES[str(dtype), closed]
+ self.root = node_cls(self.left, self.right, indices, leaf_size)
+
+ @property
+ def left_sorter(self):
+ """How to sort the left labels; this is used for binary search
+ """
+ if self._left_sorter is None:
+ self._left_sorter = np.argsort(self.left)
+ return self._left_sorter
+
+ @property
+ def right_sorter(self):
+ """How to sort the right labels
+ """
+ if self._right_sorter is None:
+ self._right_sorter = np.argsort(self.right)
+ return self._right_sorter
+
+ def get_loc(self, scalar64_t key):
+ """Return all positions corresponding to intervals that overlap with
+ the given scalar key
+ """
+ result = Int64Vector()
+ self.root.query(result, key)
+ if not result.data.n:
+ raise KeyError(key)
+ return result.to_array()
+
+ def _get_partial_overlap(self, key_left, key_right, side):
+ """Return all positions corresponding to intervals with the given side
+ falling between the left and right bounds of an interval query
+ """
+ if side == 'left':
+ values = self.left
+ sorter = self.left_sorter
+ else:
+ values = self.right
+ sorter = self.right_sorter
+ key = [key_left, key_right]
+ i, j = values.searchsorted(key, sorter=sorter)
+ return sorter[i:j]
+
+ def get_loc_interval(self, key_left, key_right):
+ """Lookup the intervals enclosed in the given interval bounds
+
+ The given interval is presumed to have closed bounds.
+ """
+ import pandas as pd
+ left_overlap = self._get_partial_overlap(key_left, key_right, 'left')
+ right_overlap = self._get_partial_overlap(key_left, key_right, 'right')
+ enclosing = self.get_loc(0.5 * (key_left + key_right))
+ combined = np.concatenate([left_overlap, right_overlap, enclosing])
+ uniques = pd.unique(combined)
+ return uniques
+
+ def get_indexer(self, scalar64_t[:] target):
+ """Return the positions corresponding to unique intervals that overlap
+ with the given array of scalar targets.
+ """
+ # TODO: write get_indexer_intervals
+ cdef:
+ int64_t old_len, i
+ Int64Vector result
+
+ result = Int64Vector()
+ old_len = 0
+ for i in range(len(target)):
+ self.root.query(result, target[i])
+ if result.data.n == old_len:
+ result.append(-1)
+ elif result.data.n > old_len + 1:
+ raise KeyError(
+ 'indexer does not intersect a unique set of intervals')
+ old_len = result.data.n
+ return result.to_array()
+
+ def get_indexer_non_unique(self, scalar64_t[:] target):
+ """Return the positions corresponding to intervals that overlap with
+ the given array of scalar targets. Non-unique positions are repeated.
+ """
+ cdef:
+ int64_t old_len, i
+ Int64Vector result, missing
+
+ result = Int64Vector()
+ missing = Int64Vector()
+ old_len = 0
+ for i in range(len(target)):
+ self.root.query(result, target[i])
+ if result.data.n == old_len:
+ result.append(-1)
+ missing.append(i)
+ old_len = result.data.n
+ return result.to_array(), missing.to_array()
+
+ def __repr__(self):
+ return ('<IntervalTree: %s elements>'
+ % self.root.n_elements)
+
+
+cdef take(ndarray source, ndarray indices):
+ """Take the given positions from a 1D ndarray
+ """
+ return PyArray_Take(source, indices, 0)
+
+
+cdef sort_values_and_indices(all_values, all_indices, subset):
+ indices = take(all_indices, subset)
+ values = take(all_values, subset)
+ sorter = PyArray_ArgSort(values, 0, NPY_QUICKSORT)
+ sorted_values = take(values, sorter)
+ sorted_indices = take(indices, sorter)
+ return sorted_values, sorted_indices
+'''
+
+# we need specialized nodes and leaves to optimize for different dtype and
+# closed values
+# unfortunately, fused dtypes can't parameterize attributes on extension types,
+# so we're stuck using template generation.
+
+node_template = r'''
+cdef class {dtype_title}Closed{closed_title}IntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ {dtype_title}Closed{closed_title}IntervalNode left_node, right_node
+ {dtype}_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ {dtype}_t min_left, max_right
+ readonly {dtype}_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[{dtype}_t, ndim=1] left,
+ ndarray[{dtype}_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, {dtype}_t[:] left, {dtype}_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] {cmp_right_converse} self.pivot:
+ left_ind.append(i)
+ elif self.pivot {cmp_left_converse} left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[{dtype}_t, ndim=1] left,
+ ndarray[{dtype}_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return {dtype_title}Closed{closed_title}IntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ {dtype}_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] {cmp_left} point {cmp_right} self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] {cmp_left} point:
+ break
+ result.append(indices[i])
+ if point {cmp_right} self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point {cmp_right} values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left {cmp_left} point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<{dtype_title}Closed{closed_title}IntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<{dtype_title}Closed{closed_title}IntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['{dtype}', '{closed}'] = {dtype_title}Closed{closed_title}IntervalNode
+'''
+
+
+def generate_node_template():
+ output = StringIO()
+ for dtype in ['float64', 'int64']:
+ for closed, cmp_left, cmp_right in [
+ ('left', '<=', '<'),
+ ('right', '<', '<='),
+ ('both', '<=', '<='),
+ ('neither', '<', '<')]:
+ cmp_left_converse = '<' if cmp_left == '<=' else '<='
+ cmp_right_converse = '<' if cmp_right == '<=' else '<='
+ classes = node_template.format(dtype=dtype,
+ dtype_title=dtype.title(),
+ closed=closed,
+ closed_title=closed.title(),
+ cmp_left=cmp_left,
+ cmp_right=cmp_right,
+ cmp_left_converse=cmp_left_converse,
+ cmp_right_converse=cmp_right_converse)
+ output.write(classes)
+ output.write("\n")
+ return output.getvalue()
+
+
+def generate_cython_file():
+ # Put `intervaltree.pyx` in the same directory as this file
+ directory = os.path.dirname(os.path.realpath(__file__))
+ filename = 'intervaltree.pyx'
+ path = os.path.join(directory, filename)
+
+ with open(path, 'w') as f:
+ print(warning_to_new_contributors, file=f)
+ print(header, file=f)
+ print(generate_node_template(), file=f)
+
+
+if __name__ == '__main__':
+ generate_cython_file()
diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx
index 1a5703eb91053..e0cba8d73538c 100644
--- a/pandas/src/inference.pyx
+++ b/pandas/src/inference.pyx
@@ -194,6 +194,10 @@ def infer_dtype(object _values):
if is_period_array(values):
return 'period'
+ elif is_interval(val):
+ if is_interval_array_fixed_closed(values):
+ return 'interval'
+
for i in range(n):
val = util.get_value_1d(values, i)
if util.is_integer_object(val):
@@ -539,6 +543,23 @@ def is_period_array(ndarray[object] values):
return False
return True
+cdef inline bint is_interval(object o):
+ return isinstance(o, Interval)
+
+def is_interval_array_fixed_closed(ndarray[object] values):
+ cdef Py_ssize_t i, n = len(values)
+ cdef str closed
+ if n == 0:
+ return False
+ for i in range(n):
+ if not is_interval(values[i]):
+ return False
+ if i == 0:
+ closed = values[0].closed
+ elif closed != values[i].closed:
+ return False
+ return True
+
cdef extern from "parse_helper.h":
inline int floatify(object, double *result, int *maybe_int) except -1
diff --git a/pandas/src/interval.pyx b/pandas/src/interval.pyx
new file mode 100644
index 0000000000000..495730e0fd6a1
--- /dev/null
+++ b/pandas/src/interval.pyx
@@ -0,0 +1,171 @@
+cimport numpy as np
+import numpy as np
+import pandas as pd
+
+cimport cython
+import cython
+
+from cpython.object cimport (Py_EQ, Py_NE, Py_GT, Py_LT, Py_GE, Py_LE,
+ PyObject_RichCompare)
+
+import numbers
+_VALID_CLOSED = frozenset(['left', 'right', 'both', 'neither'])
+
+cdef class IntervalMixin:
+ property closed_left:
+ def __get__(self):
+ return self.closed == 'left' or self.closed == 'both'
+
+ property closed_right:
+ def __get__(self):
+ return self.closed == 'right' or self.closed == 'both'
+
+ property open_left:
+ def __get__(self):
+ return not self.closed_left
+
+ property open_right:
+ def __get__(self):
+ return not self.closed_right
+
+ property mid:
+ def __get__(self):
+ try:
+ return 0.5 * (self.left + self.right)
+ except TypeError:
+ # datetime safe version
+ return self.left + 0.5 * (self.right - self.left)
+
+
+cdef _interval_like(other):
+ return (hasattr(other, 'left')
+ and hasattr(other, 'right')
+ and hasattr(other, 'closed'))
+
+
+cdef class Interval(IntervalMixin):
+ cdef readonly object left, right
+ cdef readonly str closed
+
+ def __init__(self, left, right, str closed='right'):
+ # note: it is faster to just do these checks than to use a special
+ # constructor (__cinit__/__new__) to avoid them
+ if closed not in _VALID_CLOSED:
+ raise ValueError("invalid option for 'closed': %s" % closed)
+ if not left <= right:
+ raise ValueError('left side of interval must be <= right side')
+ self.left = left
+ self.right = right
+ self.closed = closed
+
+ def __hash__(self):
+ return hash((self.left, self.right, self.closed))
+
+ def __contains__(self, key):
+ if _interval_like(key):
+ raise TypeError('__contains__ not defined for two intervals')
+ return ((self.left < key if self.open_left else self.left <= key) and
+ (key < self.right if self.open_right else key <= self.right))
+
+ def __richcmp__(self, other, int op):
+ if hasattr(other, 'ndim'):
+ # let numpy (or IntervalIndex) handle vectorization
+ return NotImplemented
+
+ if _interval_like(other):
+ self_tuple = (self.left, self.right, self.closed)
+ other_tuple = (other.left, other.right, other.closed)
+ return PyObject_RichCompare(self_tuple, other_tuple, op)
+
+ # nb. could just return NotImplemented now, but handling this
+ # explicitly allows us to opt into the Python 3 behavior, even on
+ # Python 2.
+ if op == Py_EQ or op == Py_NE:
+ return NotImplemented
+ else:
+ op_str = {Py_LT: '<', Py_LE: '<=', Py_GT: '>', Py_GE: '>='}[op]
+ raise TypeError('unorderable types: %s() %s %s()' %
+ (type(self).__name__, op_str, type(other).__name__))
+
+ def __reduce__(self):
+ args = (self.left, self.right, self.closed)
+ return (type(self), args)
+
+ def __repr__(self):
+ return ('%s(%r, %r, closed=%r)' %
+ (type(self).__name__, self.left, self.right, self.closed))
+
+ def __str__(self):
+ start_symbol = '[' if self.closed_left else '('
+ end_symbol = ']' if self.closed_right else ')'
+ return '%s%s, %s%s' % (start_symbol, self.left, self.right, end_symbol)
+
+ def __add__(self, y):
+ if isinstance(y, numbers.Number):
+ return Interval(self.left + y, self.right + y)
+ elif isinstance(y, Interval) and isinstance(self, numbers.Number):
+ return Interval(y.left + self, y.right + self)
+ else:
+ raise NotImplemented
+
+ def __sub__(self, y):
+ if isinstance(y, numbers.Number):
+ return Interval(self.left - y, self.right - y)
+ else:
+ raise NotImplemented
+
+ def __mul__(self, y):
+ if isinstance(y, numbers.Number):
+ return Interval(self.left * y, self.right * y)
+ elif isinstance(y, Interval) and isinstance(self, numbers.Number):
+ return Interval(y.left * self, y.right * self)
+ else:
+ return NotImplemented
+
+ def __div__(self, y):
+ if isinstance(y, numbers.Number):
+ return Interval(self.left / y, self.right / y)
+ else:
+ return NotImplemented
+
+ def __truediv__(self, y):
+ if isinstance(y, numbers.Number):
+ return Interval(self.left / y, self.right / y)
+ else:
+ return NotImplemented
+
+ def __floordiv__(self, y):
+ if isinstance(y, numbers.Number):
+ return Interval(self.left // y, self.right // y)
+ else:
+ return NotImplemented
+
+
+@cython.wraparound(False)
+@cython.boundscheck(False)
+cpdef interval_bounds_to_intervals(np.ndarray left, np.ndarray right,
+ str closed):
+ result = np.empty(len(left), dtype=object)
+ nulls = pd.isnull(left) | pd.isnull(right)
+ result[nulls] = np.nan
+ for i in np.flatnonzero(~nulls):
+ result[i] = Interval(left[i], right[i], closed)
+ return result
+
+
+@cython.wraparound(False)
+@cython.boundscheck(False)
+cpdef intervals_to_interval_bounds(np.ndarray intervals):
+ left = np.empty(len(intervals), dtype=object)
+ right = np.empty(len(intervals), dtype=object)
+ cdef str closed = None
+ for i in range(len(intervals)):
+ interval = intervals[i]
+ left[i] = interval.left
+ right[i] = interval.right
+ if closed is None:
+ closed = interval.closed
+ elif closed != interval.closed:
+ raise ValueError('intervals must all be closed on the same side')
+ return left, right, closed
+
diff --git a/pandas/src/intervaltree.pyx b/pandas/src/intervaltree.pyx
new file mode 100644
index 0000000000000..55782c930d4f8
--- /dev/null
+++ b/pandas/src/intervaltree.pyx
@@ -0,0 +1,1444 @@
+
+# DO NOT EDIT THIS FILE: This file was autogenerated from
+# generate_intervaltree.py, so please edit that file and then run
+# `python2 generate_intervaltree.py` to re-generate this file.
+
+
+from numpy cimport int64_t, float64_t
+from numpy cimport ndarray, PyArray_ArgSort, NPY_QUICKSORT, PyArray_Take
+import numpy as np
+
+cimport cython
+cimport numpy as cnp
+cnp.import_array()
+
+from hashtable cimport Int64Vector, Int64VectorData
+
+
+ctypedef fused scalar64_t:
+ float64_t
+ int64_t
+
+
+NODE_CLASSES = {}
+
+
+cdef class IntervalTree(IntervalMixin):
+ """A centered interval tree
+
+ Based off the algorithm described on Wikipedia:
+ http://en.wikipedia.org/wiki/Interval_tree
+ """
+ cdef:
+ readonly object left, right, root
+ readonly str closed
+ object _left_sorter, _right_sorter
+
+ def __init__(self, left, right, closed='right', leaf_size=100):
+ """
+ Parameters
+ ----------
+ left, right : np.ndarray[ndim=1]
+ Left and right bounds for each interval. Assumed to contain no
+ NaNs.
+ closed : {'left', 'right', 'both', 'neither'}, optional
+ Whether the intervals are closed on the left-side, right-side, both
+ or neither. Defaults to 'right'.
+ leaf_size : int, optional
+ Parameter that controls when the tree switches from creating nodes
+ to brute-force search. Tune this parameter to optimize query
+ performance.
+ """
+ if closed not in ['left', 'right', 'both', 'neither']:
+ raise ValueError("invalid option for 'closed': %s" % closed)
+
+ left = np.asarray(left)
+ right = np.asarray(right)
+ dtype = np.result_type(left, right)
+ self.left = np.asarray(left, dtype=dtype)
+ self.right = np.asarray(right, dtype=dtype)
+
+ indices = np.arange(len(left), dtype='int64')
+
+ self.closed = closed
+
+ node_cls = NODE_CLASSES[str(dtype), closed]
+ self.root = node_cls(self.left, self.right, indices, leaf_size)
+
+ @property
+ def left_sorter(self):
+ """How to sort the left labels; this is used for binary search
+ """
+ if self._left_sorter is None:
+ self._left_sorter = np.argsort(self.left)
+ return self._left_sorter
+
+ @property
+ def right_sorter(self):
+ """How to sort the right labels
+ """
+ if self._right_sorter is None:
+ self._right_sorter = np.argsort(self.right)
+ return self._right_sorter
+
+ def get_loc(self, scalar64_t key):
+ """Return all positions corresponding to intervals that overlap with
+ the given scalar key
+ """
+ result = Int64Vector()
+ self.root.query(result, key)
+ if not result.data.n:
+ raise KeyError(key)
+ return result.to_array()
+
+ def _get_partial_overlap(self, key_left, key_right, side):
+ """Return all positions corresponding to intervals with the given side
+ falling between the left and right bounds of an interval query
+ """
+ if side == 'left':
+ values = self.left
+ sorter = self.left_sorter
+ else:
+ values = self.right
+ sorter = self.right_sorter
+ key = [key_left, key_right]
+ i, j = values.searchsorted(key, sorter=sorter)
+ return sorter[i:j]
+
+ def get_loc_interval(self, key_left, key_right):
+ """Lookup the intervals enclosed in the given interval bounds
+
+ The given interval is presumed to have closed bounds.
+ """
+ import pandas as pd
+ left_overlap = self._get_partial_overlap(key_left, key_right, 'left')
+ right_overlap = self._get_partial_overlap(key_left, key_right, 'right')
+ enclosing = self.get_loc(0.5 * (key_left + key_right))
+ combined = np.concatenate([left_overlap, right_overlap, enclosing])
+ uniques = pd.unique(combined)
+ return uniques
+
+ def get_indexer(self, scalar64_t[:] target):
+ """Return the positions corresponding to unique intervals that overlap
+ with the given array of scalar targets.
+ """
+ # TODO: write get_indexer_intervals
+ cdef:
+ int64_t old_len, i
+ Int64Vector result
+
+ result = Int64Vector()
+ old_len = 0
+ for i in range(len(target)):
+ self.root.query(result, target[i])
+ if result.data.n == old_len:
+ result.append(-1)
+ elif result.data.n > old_len + 1:
+ raise KeyError(
+ 'indexer does not intersect a unique set of intervals')
+ old_len = result.data.n
+ return result.to_array()
+
+ def get_indexer_non_unique(self, scalar64_t[:] target):
+ """Return the positions corresponding to intervals that overlap with
+ the given array of scalar targets. Non-unique positions are repeated.
+ """
+ cdef:
+ int64_t old_len, i
+ Int64Vector result, missing
+
+ result = Int64Vector()
+ missing = Int64Vector()
+ old_len = 0
+ for i in range(len(target)):
+ self.root.query(result, target[i])
+ if result.data.n == old_len:
+ result.append(-1)
+ missing.append(i)
+ old_len = result.data.n
+ return result.to_array(), missing.to_array()
+
+ def __repr__(self):
+ return ('<IntervalTree: %s elements>'
+ % self.root.n_elements)
+
+
+cdef take(ndarray source, ndarray indices):
+ """Take the given positions from a 1D ndarray
+ """
+ return PyArray_Take(source, indices, 0)
+
+
+cdef sort_values_and_indices(all_values, all_indices, subset):
+ indices = take(all_indices, subset)
+ values = take(all_values, subset)
+ sorter = PyArray_ArgSort(values, 0, NPY_QUICKSORT)
+ sorted_values = take(values, sorter)
+ sorted_indices = take(indices, sorter)
+ return sorted_values, sorted_indices
+
+
+cdef class Float64ClosedLeftIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Float64ClosedLeftIntervalNode left_node, right_node
+ float64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ float64_t min_left, max_right
+ readonly float64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, float64_t[:] left, float64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] <= self.pivot:
+ left_ind.append(i)
+ elif self.pivot < left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Float64ClosedLeftIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ float64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] <= point < self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] <= point:
+ break
+ result.append(indices[i])
+ if point < self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point < values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left <= point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Float64ClosedLeftIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Float64ClosedLeftIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['float64', 'left'] = Float64ClosedLeftIntervalNode
+
+
+cdef class Float64ClosedRightIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Float64ClosedRightIntervalNode left_node, right_node
+ float64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ float64_t min_left, max_right
+ readonly float64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, float64_t[:] left, float64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] < self.pivot:
+ left_ind.append(i)
+ elif self.pivot <= left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Float64ClosedRightIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ float64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] < point <= self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] < point:
+ break
+ result.append(indices[i])
+ if point <= self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point <= values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left < point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Float64ClosedRightIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Float64ClosedRightIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['float64', 'right'] = Float64ClosedRightIntervalNode
+
+
+cdef class Float64ClosedBothIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Float64ClosedBothIntervalNode left_node, right_node
+ float64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ float64_t min_left, max_right
+ readonly float64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, float64_t[:] left, float64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] < self.pivot:
+ left_ind.append(i)
+ elif self.pivot < left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Float64ClosedBothIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ float64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] <= point <= self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] <= point:
+ break
+ result.append(indices[i])
+ if point <= self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point <= values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left <= point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Float64ClosedBothIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Float64ClosedBothIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['float64', 'both'] = Float64ClosedBothIntervalNode
+
+
+cdef class Float64ClosedNeitherIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Float64ClosedNeitherIntervalNode left_node, right_node
+ float64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ float64_t min_left, max_right
+ readonly float64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, float64_t[:] left, float64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] <= self.pivot:
+ left_ind.append(i)
+ elif self.pivot <= left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[float64_t, ndim=1] left,
+ ndarray[float64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Float64ClosedNeitherIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ float64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] < point < self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] < point:
+ break
+ result.append(indices[i])
+ if point < self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point < values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left < point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Float64ClosedNeitherIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Float64ClosedNeitherIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['float64', 'neither'] = Float64ClosedNeitherIntervalNode
+
+
+cdef class Int64ClosedLeftIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Int64ClosedLeftIntervalNode left_node, right_node
+ int64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ int64_t min_left, max_right
+ readonly int64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, int64_t[:] left, int64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] <= self.pivot:
+ left_ind.append(i)
+ elif self.pivot < left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Int64ClosedLeftIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ int64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] <= point < self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] <= point:
+ break
+ result.append(indices[i])
+ if point < self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point < values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left <= point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Int64ClosedLeftIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Int64ClosedLeftIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['int64', 'left'] = Int64ClosedLeftIntervalNode
+
+
+cdef class Int64ClosedRightIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Int64ClosedRightIntervalNode left_node, right_node
+ int64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ int64_t min_left, max_right
+ readonly int64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, int64_t[:] left, int64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] < self.pivot:
+ left_ind.append(i)
+ elif self.pivot <= left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Int64ClosedRightIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ int64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] < point <= self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] < point:
+ break
+ result.append(indices[i])
+ if point <= self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point <= values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left < point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Int64ClosedRightIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Int64ClosedRightIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['int64', 'right'] = Int64ClosedRightIntervalNode
+
+
+cdef class Int64ClosedBothIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Int64ClosedBothIntervalNode left_node, right_node
+ int64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ int64_t min_left, max_right
+ readonly int64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, int64_t[:] left, int64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] < self.pivot:
+ left_ind.append(i)
+ elif self.pivot < left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Int64ClosedBothIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ int64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] <= point <= self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] <= point:
+ break
+ result.append(indices[i])
+ if point <= self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point <= values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left <= point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Int64ClosedBothIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Int64ClosedBothIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['int64', 'both'] = Int64ClosedBothIntervalNode
+
+
+cdef class Int64ClosedNeitherIntervalNode:
+ """Non-terminal node for an IntervalTree
+
+ Categorizes intervals by those that fall to the left, those that fall to
+ the right, and those that overlap with the pivot.
+ """
+ cdef:
+ Int64ClosedNeitherIntervalNode left_node, right_node
+ int64_t[:] center_left_values, center_right_values, left, right
+ int64_t[:] center_left_indices, center_right_indices, indices
+ int64_t min_left, max_right
+ readonly int64_t pivot
+ readonly int64_t n_elements, n_center, leaf_size
+ readonly bint is_leaf_node
+
+ def __init__(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ int64_t leaf_size):
+
+ self.n_elements = len(left)
+ self.leaf_size = leaf_size
+ if left.size > 0:
+ self.min_left = left.min()
+ self.max_right = right.max()
+ else:
+ self.min_left = 0
+ self.max_right = 0
+
+ if self.n_elements <= leaf_size:
+ # make this a terminal (leaf) node
+ self.is_leaf_node = True
+ self.left = left
+ self.right = right
+ self.indices = indices
+ self.n_center
+ else:
+ # calculate a pivot so we can create child nodes
+ self.is_leaf_node = False
+ self.pivot = np.median(left + right) / 2
+ left_set, right_set, center_set = self.classify_intervals(left, right)
+
+ self.left_node = self.new_child_node(left, right, indices, left_set)
+ self.right_node = self.new_child_node(left, right, indices, right_set)
+
+ self.center_left_values, self.center_left_indices = \
+ sort_values_and_indices(left, indices, center_set)
+ self.center_right_values, self.center_right_indices = \
+ sort_values_and_indices(right, indices, center_set)
+ self.n_center = len(self.center_left_indices)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ cdef classify_intervals(self, int64_t[:] left, int64_t[:] right):
+ """Classify the given intervals based upon whether they fall to the
+ left, right, or overlap with this node's pivot.
+ """
+ cdef:
+ Int64Vector left_ind, right_ind, overlapping_ind
+ Py_ssize_t i
+
+ left_ind = Int64Vector()
+ right_ind = Int64Vector()
+ overlapping_ind = Int64Vector()
+
+ for i in range(self.n_elements):
+ if right[i] <= self.pivot:
+ left_ind.append(i)
+ elif self.pivot <= left[i]:
+ right_ind.append(i)
+ else:
+ overlapping_ind.append(i)
+
+ return (left_ind.to_array(),
+ right_ind.to_array(),
+ overlapping_ind.to_array())
+
+ cdef new_child_node(self,
+ ndarray[int64_t, ndim=1] left,
+ ndarray[int64_t, ndim=1] right,
+ ndarray[int64_t, ndim=1] indices,
+ ndarray[int64_t, ndim=1] subset):
+ """Create a new child node.
+ """
+ left = take(left, subset)
+ right = take(right, subset)
+ indices = take(indices, subset)
+ return Int64ClosedNeitherIntervalNode(
+ left, right, indices, self.leaf_size)
+
+ @cython.wraparound(False)
+ @cython.boundscheck(False)
+ @cython.initializedcheck(False)
+ cpdef query(self, Int64Vector result, scalar64_t point):
+ """Recursively query this node and its sub-nodes for intervals that
+ overlap with the query point.
+ """
+ cdef:
+ int64_t[:] indices
+ int64_t[:] values
+ Py_ssize_t i
+
+ if self.is_leaf_node:
+ # Once we get down to a certain size, it doesn't make sense to
+ # continue the binary tree structure. Instead, we use linear
+ # search.
+ for i in range(self.n_elements):
+ if self.left[i] < point < self.right[i]:
+ result.append(self.indices[i])
+ else:
+ # There are child nodes. Based on comparing our query to the pivot,
+ # look at the center values, then go to the relevant child.
+ if point < self.pivot:
+ values = self.center_left_values
+ indices = self.center_left_indices
+ for i in range(self.n_center):
+ if not values[i] < point:
+ break
+ result.append(indices[i])
+ if point < self.left_node.max_right:
+ self.left_node.query(result, point)
+ elif point > self.pivot:
+ values = self.center_right_values
+ indices = self.center_right_indices
+ for i in range(self.n_center - 1, -1, -1):
+ if not point < values[i]:
+ break
+ result.append(indices[i])
+ if self.right_node.min_left < point:
+ self.right_node.query(result, point)
+ else:
+ result.extend(self.center_left_indices)
+
+ def __repr__(self):
+ if self.is_leaf_node:
+ return ('<Int64ClosedNeitherIntervalNode: '
+ '%s elements (terminal)>' % self.n_elements)
+ else:
+ n_left = self.left_node.n_elements
+ n_right = self.right_node.n_elements
+ n_center = self.n_elements - n_left - n_right
+ return ('<Int64ClosedNeitherIntervalNode: pivot %s, '
+ '%s elements (%s left, %s right, %s overlapping)>' %
+ (self.pivot, self.n_elements, n_left, n_right, n_center))
+
+ def counts(self):
+ if self.is_leaf_node:
+ return self.n_elements
+ else:
+ m = len(self.center_left_values)
+ l = self.left_node.counts()
+ r = self.right_node.counts()
+ return (m, (l, r))
+
+NODE_CLASSES['int64', 'neither'] = Int64ClosedNeitherIntervalNode
+
+
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index b18bd7b2b3978..eb0964392d20c 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -352,14 +352,10 @@ def test_value_counts(self):
arr = np.random.randn(4)
factor = cut(arr, 4)
- tm.assertIsInstance(factor, Categorical)
+ # tm.assertIsInstance(factor, n)
result = algos.value_counts(factor)
- cats = ['(-1.194, -0.535]',
- '(-0.535, 0.121]',
- '(0.121, 0.777]',
- '(0.777, 1.433]'
- ]
- expected_index = CategoricalIndex(cats, cats, ordered=True)
+ breaks = [-1.192, -0.535, 0.121, 0.777, 1.433]
+ expected_index = pd.IntervalIndex.from_breaks(breaks)
expected = Series([1, 1, 1, 1],
index=expected_index)
tm.assert_series_equal(result.sort_index(), expected.sort_index())
@@ -368,12 +364,12 @@ def test_value_counts_bins(self):
s = [1, 2, 3, 4]
result = algos.value_counts(s, bins=1)
self.assertEqual(result.tolist(), [4])
- self.assertEqual(result.index[0], 0.997)
+ self.assertEqual(result.index[0], pd.Interval(0.999, 4.0))
result = algos.value_counts(s, bins=2, sort=False)
self.assertEqual(result.tolist(), [2, 2])
- self.assertEqual(result.index[0], 0.997)
- self.assertEqual(result.index[1], 2.5)
+ self.assertEqual(result.index.min(), pd.Interval(0.999, 2.5))
+ self.assertEqual(result.index.max(), pd.Interval(2.5, 4.0))
def test_value_counts_dtypes(self):
result = algos.value_counts([1, 1.])
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index 8eb5bd3a202a4..ca60c357b73c5 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -11,7 +11,8 @@
from pandas.tseries.base import DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assertIsInstance
from pandas.tseries.common import is_datetimelike
-from pandas import Series, Index, Int64Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta
+from pandas import (Series, Index, Int64Index, DatetimeIndex, TimedeltaIndex,
+ PeriodIndex, IntervalIndex, Timedelta, Interval)
import pandas.tslib as tslib
from pandas import _np_version_under1p9
import nose
@@ -553,20 +554,21 @@ def test_value_counts_inferred(self):
s1 = Series([1, 1, 2, 3])
res1 = s1.value_counts(bins=1)
- exp1 = Series({0.998: 4})
+ exp1 = Series({Interval(0.999, 3.0): 4})
tm.assert_series_equal(res1, exp1)
res1n = s1.value_counts(bins=1, normalize=True)
- exp1n = Series({0.998: 1.0})
+ exp1n = Series({Interval(0.999, 3.0): 1.0})
tm.assert_series_equal(res1n, exp1n)
self.assert_numpy_array_equal(s1.unique(), np.array([1, 2, 3]))
self.assertEqual(s1.nunique(), 3)
res4 = s1.value_counts(bins=4)
- exp4 = Series({0.998: 2, 1.5: 1, 2.0: 0, 2.5: 1}, index=[0.998, 2.5, 1.5, 2.0])
+ intervals = IntervalIndex.from_breaks([0.999, 1.5, 2.0, 2.5, 3.0])
+ exp4 = Series([2, 1, 1], index=intervals.take([0, 3, 1]))
tm.assert_series_equal(res4, exp4)
res4n = s1.value_counts(bins=4, normalize=True)
- exp4n = Series({0.998: 0.5, 1.5: 0.25, 2.0: 0.0, 2.5: 0.25}, index=[0.998, 2.5, 1.5, 2.0])
+ exp4n = Series([0.5, 0.25, 0.25], index=intervals.take([0, 3, 1]))
tm.assert_series_equal(res4n, exp4n)
# handle NA's properly
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 64908f96bfdd8..492f3eb79947e 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -11,7 +11,8 @@
import numpy as np
import pandas as pd
-from pandas import Categorical, Index, Series, DataFrame, PeriodIndex, Timestamp, CategoricalIndex
+from pandas import (Categorical, Index, Series, DataFrame, PeriodIndex,
+ Timestamp, CategoricalIndex, Interval)
from pandas.core.config import option_context
import pandas.core.common as com
@@ -1328,9 +1329,10 @@ def setUp(self):
df = DataFrame({'value': np.random.randint(0, 10000, 100)})
labels = [ "{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500) ]
+ cat_labels = Categorical(labels, labels)
df = df.sort_values(by=['value'], ascending=True)
- df['value_group'] = pd.cut(df.value, range(0, 10500, 500), right=False, labels=labels)
+ df['value_group'] = pd.cut(df.value, range(0, 10500, 500), right=False, labels=cat_labels)
self.cat = df
def test_dtypes(self):
@@ -1727,7 +1729,7 @@ def test_series_functions_no_warnings(self):
def test_assignment_to_dataframe(self):
# assignment
df = DataFrame({'value': np.array(np.random.randint(0, 10000, 100),dtype='int32')})
- labels = [ "{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500) ]
+ labels = Categorical(["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)])
df = df.sort_values(by=['value'], ascending=True)
s = pd.cut(df.value, range(0, 10500, 500), right=False, labels=labels)
@@ -2590,7 +2592,7 @@ def f(x):
# GH 9603
df = pd.DataFrame({'a': [1, 0, 0, 0]})
- c = pd.cut(df.a, [0, 1, 2, 3, 4])
+ c = pd.cut(df.a, [0, 1, 2, 3, 4], labels=pd.Categorical(list('abcd')))
result = df.groupby(c).apply(len)
expected = pd.Series([1, 0, 0, 0], index=pd.CategoricalIndex(c.values.categories))
expected.index.name = 'a'
@@ -2725,7 +2727,7 @@ def test_slicing(self):
df = DataFrame({'value': (np.arange(100)+1).astype('int64')})
df['D'] = pd.cut(df.value, bins=[0,25,50,75,100])
- expected = Series([11,'(0, 25]'], index=['value','D'], name=10)
+ expected = Series([11, Interval(0, 25)], index=['value','D'], name=10)
result = df.iloc[10]
tm.assert_series_equal(result, expected)
@@ -2735,7 +2737,7 @@ def test_slicing(self):
result = df.iloc[10:20]
tm.assert_frame_equal(result, expected)
- expected = Series([9,'(0, 25]'],index=['value', 'D'], name=8)
+ expected = Series([9, Interval(0, 25)],index=['value', 'D'], name=8)
result = df.loc[8]
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 09de3bf4a8046..f3af12b68cb47 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -14465,6 +14465,17 @@ def test_reset_index_with_datetimeindex_cols(self):
datetime(2013, 1, 2)])
assert_frame_equal(result, expected)
+ def test_reset_index_with_intervals(self):
+ idx = pd.IntervalIndex.from_breaks(np.arange(11), name='x')
+ original = pd.DataFrame({'x': idx, 'y': np.arange(10)})[['x', 'y']]
+
+ result = original.set_index('x')
+ expected = pd.DataFrame({'y': np.arange(10)}, index=idx)
+ assert_frame_equal(result, expected)
+
+ result2 = result.reset_index()
+ assert_frame_equal(result2, original)
+
#----------------------------------------------------------------------
# Tests to cope with refactored internals
def test_as_matrix_numeric_cols(self):
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index bd21053f37568..c1960f915e981 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -9,6 +9,7 @@
from pandas import date_range,bdate_range, Timestamp
from pandas.core.index import Index, MultiIndex, Int64Index, CategoricalIndex
+from pandas.core.interval import IntervalIndex
from pandas.core.api import Categorical, DataFrame
from pandas.core.groupby import (SpecificationError, DataError,
_nargsort, _lexsort_indexer)
@@ -4036,7 +4037,7 @@ def test_groupby_categorical_unequal_len(self):
#GH3011
series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4])
# The raises only happens with categorical, not with series of types category
- bins = pd.cut(series.dropna().values, 4)
+ bins = pd.cut(series.dropna().values, 4, labels=pd.Categorical(list('abcd')))
# len(bins) != len(series) here
self.assertRaises(ValueError,lambda : series.groupby(bins).mean())
@@ -5677,13 +5678,13 @@ def test_groupby_categorical_two_columns(self):
d = {'C1': [3, 3, 4, 5], 'C2': [1, 2, 3, 4], 'C3': [10, 100, 200, 34]}
test = pd.DataFrame(d)
- values = pd.cut(test['C1'], [1, 2, 3, 6])
+ values = pd.cut(test['C1'], [1, 2, 3, 6], labels=pd.Categorical(['a', 'b', 'c']))
values.name = "cat"
groups_double_key = test.groupby([values,'C2'])
res = groups_double_key.agg('mean')
nan = np.nan
- idx = MultiIndex.from_product([["(1, 2]", "(2, 3]", "(3, 6]"],[1,2,3,4]],
+ idx = MultiIndex.from_product([['a', 'b', 'c'], [1, 2, 3, 4]],
names=["cat", "C2"])
exp = DataFrame({"C1":[nan,nan,nan,nan, 3, 3,nan,nan, nan,nan, 4, 5],
"C3":[nan,nan,nan,nan, 10,100,nan,nan, nan,nan,200,34]}, index=idx)
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index e2fa6a90429dc..2a7138c9bfdec 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -13,7 +13,8 @@
from pandas import (period_range, date_range, Categorical, Series,
Index, Float64Index, Int64Index, MultiIndex,
- CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex)
+ CategoricalIndex, IntervalIndex, DatetimeIndex,
+ TimedeltaIndex, PeriodIndex)
from pandas.core.index import InvalidIndexError, NumericIndex
from pandas.util.testing import (assert_almost_equal, assertRaisesRegexp,
assert_copy)
@@ -109,9 +110,6 @@ def test_reindex_base(self):
actual = idx.get_indexer(idx)
tm.assert_numpy_array_equal(expected, actual)
- with tm.assertRaisesRegexp(ValueError, 'Invalid fill method'):
- idx.get_indexer(idx, method='invalid')
-
def test_ndarray_compat_properties(self):
idx = self.create_index()
@@ -222,7 +220,7 @@ def test_duplicates(self):
if not len(ind):
continue
- if isinstance(ind, MultiIndex):
+ if isinstance(ind, (MultiIndex, IntervalIndex)):
continue
idx = self._holder([ind[0]]*5)
self.assertFalse(idx.is_unique)
@@ -1410,6 +1408,9 @@ def test_get_indexer_invalid(self):
with tm.assertRaisesRegexp(ValueError, 'limit argument'):
idx.get_indexer([1, 0], limit=1)
+ with tm.assertRaisesRegexp(ValueError, 'Invalid fill method'):
+ idx.get_indexer(idx, method='invalid')
+
def test_get_indexer_nearest(self):
idx = Index(np.arange(10))
@@ -2615,6 +2616,18 @@ def test_fillna_categorical(self):
idx.fillna(2.0)
+class TestIntervalIndex(Base, tm.TestCase):
+ # see test_interval for more extensive tests
+ _holder = IntervalIndex
+
+ def setUp(self):
+ self.indices = dict(intvIndex = tm.makeIntervalIndex(100))
+ self.setup_indices()
+
+ def create_index(self):
+ return IntervalIndex.from_breaks(np.arange(0, 100, 10))
+
+
class Numeric(Base):
def test_numeric_compat(self):
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index c6d80a08ad61a..a5e54d58b2559 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -17,7 +17,8 @@
from pandas import option_context
from pandas.core.indexing import _non_reducing_slice, _maybe_numeric_slice
from pandas.core.api import (DataFrame, Index, Series, Panel, isnull,
- MultiIndex, Float64Index, Timestamp, Timedelta)
+ MultiIndex, Float64Index, IntervalIndex,
+ Timestamp, Timedelta)
from pandas.util.testing import (assert_almost_equal, assert_series_equal,
assert_frame_equal, assert_panel_equal,
assert_attr_equal)
@@ -4345,6 +4346,31 @@ def test_floating_index(self):
assert_series_equal(result1, result3)
assert_series_equal(result1, Series([1],index=[2.5]))
+ def test_interval_index(self):
+ s = Series(np.arange(5), IntervalIndex.from_breaks(np.arange(6)))
+
+ expected = 0
+ self.assertEqual(expected, s.loc[0.5])
+ self.assertEqual(expected, s.loc[1])
+ self.assertEqual(expected, s.loc[pd.Interval(0, 1)])
+ self.assertRaises(KeyError, s.loc.__getitem__, 0)
+
+ expected = s.iloc[:3]
+ assert_series_equal(expected, s.loc[:3])
+ assert_series_equal(expected, s.loc[:2.5])
+ assert_series_equal(expected, s.loc[0.1:2.5])
+ assert_series_equal(expected, s.loc[-1:3])
+
+ def _assert_expected_loc_array_indexer(expected, original, indexer):
+ expected = pd.Series(expected, indexer)
+ actual = original.loc[indexer]
+ assert_series_equal(expected, actual)
+
+ expected = s.iloc[1:4]
+ assert_series_equal(expected, s.loc[[1.5, 2.5, 3.5]])
+ assert_series_equal(expected, s.loc[[2, 3, 4]])
+ assert_series_equal(expected, s.loc[[1.5, 3, 4]])
+
def test_scalar_indexer(self):
# float indexing checked above
diff --git a/pandas/tests/test_interval.py b/pandas/tests/test_interval.py
new file mode 100644
index 0000000000000..1b52e2629b38c
--- /dev/null
+++ b/pandas/tests/test_interval.py
@@ -0,0 +1,591 @@
+from __future__ import division
+import numpy as np
+
+from pandas.core.interval import Interval, IntervalIndex
+from pandas.core.index import Index
+from pandas.lib import IntervalTree
+
+import pandas.util.testing as tm
+import pandas as pd
+
+
+class TestInterval(tm.TestCase):
+ def setUp(self):
+ self.interval = Interval(0, 1)
+
+ def test_properties(self):
+ self.assertEqual(self.interval.closed, 'right')
+ self.assertEqual(self.interval.left, 0)
+ self.assertEqual(self.interval.right, 1)
+ self.assertEqual(self.interval.mid, 0.5)
+
+ def test_repr(self):
+ self.assertEqual(repr(self.interval),
+ "Interval(0, 1, closed='right')")
+ self.assertEqual(str(self.interval), "(0, 1]")
+
+ interval_left = Interval(0, 1, closed='left')
+ self.assertEqual(repr(interval_left),
+ "Interval(0, 1, closed='left')")
+ self.assertEqual(str(interval_left), "[0, 1)")
+
+ def test_contains(self):
+ self.assertIn(0.5, self.interval)
+ self.assertIn(1, self.interval)
+ self.assertNotIn(0, self.interval)
+ self.assertRaises(TypeError, lambda: self.interval in self.interval)
+
+ interval = Interval(0, 1, closed='both')
+ self.assertIn(0, interval)
+ self.assertIn(1, interval)
+
+ interval = Interval(0, 1, closed='neither')
+ self.assertNotIn(0, interval)
+ self.assertIn(0.5, interval)
+ self.assertNotIn(1, interval)
+
+ def test_equal(self):
+ self.assertEqual(Interval(0, 1), Interval(0, 1, closed='right'))
+ self.assertNotEqual(Interval(0, 1), Interval(0, 1, closed='left'))
+ self.assertNotEqual(Interval(0, 1), 0)
+
+ def test_comparison(self):
+ with self.assertRaisesRegexp(TypeError, 'unorderable types'):
+ Interval(0, 1) < 2
+
+ self.assertTrue(Interval(0, 1) < Interval(1, 2))
+ self.assertTrue(Interval(0, 1) < Interval(0, 2))
+ self.assertTrue(Interval(0, 1) < Interval(0.5, 1.5))
+ self.assertTrue(Interval(0, 1) <= Interval(0, 1))
+ self.assertTrue(Interval(0, 1) > Interval(-1, 2))
+ self.assertTrue(Interval(0, 1) >= Interval(0, 1))
+
+ def test_hash(self):
+ # should not raise
+ hash(self.interval)
+
+ def test_math_add(self):
+ expected = Interval(1, 2)
+ actual = self.interval + 1
+ self.assertEqual(expected, actual)
+
+ expected = Interval(1, 2)
+ actual = 1 + self.interval
+ self.assertEqual(expected, actual)
+
+ actual = self.interval
+ actual += 1
+ self.assertEqual(expected, actual)
+
+ with self.assertRaises(TypeError):
+ self.interval + Interval(1, 2)
+
+ def test_math_sub(self):
+ expected = Interval(-1, 0)
+ actual = self.interval - 1
+ self.assertEqual(expected, actual)
+
+ actual = self.interval
+ actual -= 1
+ self.assertEqual(expected, actual)
+
+ with self.assertRaises(TypeError):
+ self.interval - Interval(1, 2)
+
+ def test_math_mult(self):
+ expected = Interval(0, 2)
+ actual = self.interval * 2
+ self.assertEqual(expected, actual)
+
+ expected = Interval(0, 2)
+ actual = 2 * self.interval
+ self.assertEqual(expected, actual)
+
+ actual = self.interval
+ actual *= 2
+ self.assertEqual(expected, actual)
+
+ with self.assertRaises(TypeError):
+ self.interval * Interval(1, 2)
+
+ def test_math_div(self):
+ expected = Interval(0, 0.5)
+ actual = self.interval / 2.0
+ self.assertEqual(expected, actual)
+
+ actual = self.interval
+ actual /= 2.0
+ self.assertEqual(expected, actual)
+
+ with self.assertRaises(TypeError):
+ self.interval / Interval(1, 2)
+
+
+class TestIntervalTree(tm.TestCase):
+ def setUp(self):
+ self.tree = IntervalTree(np.arange(5), np.arange(5) + 2)
+
+ def test_get_loc(self):
+ self.assert_numpy_array_equal(self.tree.get_loc(1), [0])
+ self.assert_numpy_array_equal(np.sort(self.tree.get_loc(2)), [0, 1])
+ with self.assertRaises(KeyError):
+ self.tree.get_loc(-1)
+
+ def test_get_indexer(self):
+ self.assert_numpy_array_equal(
+ self.tree.get_indexer(np.array([1.0, 5.5, 6.5])), [0, 4, -1])
+ with self.assertRaises(KeyError):
+ self.tree.get_indexer(np.array([3.0]))
+
+ def test_get_indexer_non_unique(self):
+ indexer, missing = self.tree.get_indexer_non_unique(
+ np.array([1.0, 2.0, 6.5]))
+ self.assert_numpy_array_equal(indexer[:1], [0])
+ self.assert_numpy_array_equal(np.sort(indexer[1:3]), [0, 1])
+ self.assert_numpy_array_equal(np.sort(indexer[3:]), [-1])
+ self.assert_numpy_array_equal(missing, [2])
+
+ def test_duplicates(self):
+ tree = IntervalTree([0, 0, 0], [1, 1, 1])
+ self.assert_numpy_array_equal(np.sort(tree.get_loc(0.5)), [0, 1, 2])
+
+ with self.assertRaises(KeyError):
+ tree.get_indexer(np.array([0.5]))
+
+ indexer, missing = tree.get_indexer_non_unique(np.array([0.5]))
+ self.assert_numpy_array_equal(np.sort(indexer), [0, 1, 2])
+ self.assert_numpy_array_equal(missing, [])
+
+ def test_get_loc_closed(self):
+ for closed in ['left', 'right', 'both', 'neither']:
+ tree = IntervalTree([0], [1], closed=closed)
+ for p, errors in [(0, tree.open_left),
+ (1, tree.open_right)]:
+ if errors:
+ with self.assertRaises(KeyError):
+ tree.get_loc(p)
+ else:
+ self.assert_numpy_array_equal(tree.get_loc(p),
+ np.array([0]))
+
+ def test_get_indexer_closed(self):
+ x = np.arange(1000)
+ found = x
+ not_found = -np.ones(1000)
+ for leaf_size in [1, 10, 100, 10000]:
+ for closed in ['left', 'right', 'both', 'neither']:
+ tree = IntervalTree(x, x + 0.5, closed=closed,
+ leaf_size=leaf_size)
+ self.assert_numpy_array_equal(found, tree.get_indexer(x + 0.25))
+
+ expected = found if tree.closed_left else not_found
+ self.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.0))
+
+ expected = found if tree.closed_right else not_found
+ self.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.5))
+
+
+class TestIntervalIndex(tm.TestCase):
+ def setUp(self):
+ self.index = IntervalIndex([0, 1], [1, 2])
+
+ def test_constructors(self):
+ expected = self.index
+ actual = IntervalIndex.from_breaks(np.arange(3), closed='right')
+ self.assertTrue(expected.equals(actual))
+
+ alternate = IntervalIndex.from_breaks(np.arange(3), closed='left')
+ self.assertFalse(expected.equals(alternate))
+
+ actual = IntervalIndex.from_intervals([Interval(0, 1), Interval(1, 2)])
+ self.assertTrue(expected.equals(actual))
+
+ self.assertRaises(ValueError, IntervalIndex, [0], [1], closed='invalid')
+
+ # TODO: fix all these commented out tests (here and below)
+
+ intervals = [Interval(0, 1), Interval(1, 2, closed='left')]
+ with self.assertRaises(ValueError):
+ IntervalIndex.from_intervals(intervals)
+
+ with self.assertRaises(ValueError):
+ IntervalIndex([0, 10], [3, 5])
+
+ actual = Index([Interval(0, 1), Interval(1, 2)])
+ self.assertIsInstance(actual, IntervalIndex)
+ self.assertTrue(expected.equals(actual))
+
+ actual = Index(expected)
+ self.assertIsInstance(actual, IntervalIndex)
+ self.assertTrue(expected.equals(actual))
+
+ # no point in nesting periods in an IntervalIndex
+ # self.assertRaises(ValueError, IntervalIndex.from_breaks,
+ # pd.period_range('2000-01-01', periods=3))
+
+ def test_properties(self):
+ self.assertEqual(len(self.index), 2)
+ self.assertEqual(self.index.size, 2)
+
+ self.assert_numpy_array_equal(self.index.left, [0, 1])
+ self.assertIsInstance(self.index.left, Index)
+
+ self.assert_numpy_array_equal(self.index.right, [1, 2])
+ self.assertIsInstance(self.index.right, Index)
+
+ self.assert_numpy_array_equal(self.index.mid, [0.5, 1.5])
+ self.assertIsInstance(self.index.mid, Index)
+
+ self.assertEqual(self.index.closed, 'right')
+
+ expected = np.array([Interval(0, 1), Interval(1, 2)], dtype=object)
+ self.assert_numpy_array_equal(np.asarray(self.index), expected)
+ self.assert_numpy_array_equal(self.index.values, expected)
+
+ def test_copy(self):
+ actual = self.index.copy()
+ self.assertTrue(actual.equals(self.index))
+
+ actual = self.index.copy(deep=True)
+ self.assertTrue(actual.equals(self.index))
+ self.assertIsNot(actual.left, self.index.left)
+
+ def test_delete(self):
+ expected = IntervalIndex.from_breaks([1, 2])
+ actual = self.index.delete(0)
+ self.assertTrue(expected.equals(actual))
+
+ def test_insert(self):
+ expected = IntervalIndex.from_breaks(range(4))
+ actual = self.index.insert(2, Interval(2, 3))
+ self.assertTrue(expected.equals(actual))
+
+ self.assertRaises(ValueError, self.index.insert, 0, 1)
+ self.assertRaises(ValueError, self.index.insert, 0,
+ Interval(2, 3, closed='left'))
+
+ def test_take(self):
+ actual = self.index.take([0, 1])
+ self.assertTrue(self.index.equals(actual))
+
+ expected = IntervalIndex([0, 0, 1], [1, 1, 2])
+ actual = self.index.take([0, 0, 1])
+ self.assertTrue(expected.equals(actual))
+
+ def test_monotonic_and_unique(self):
+ self.assertTrue(self.index.is_monotonic)
+ self.assertTrue(self.index.is_unique)
+
+ idx = IntervalIndex.from_tuples([(0, 1), (0.5, 1.5)])
+ self.assertTrue(idx.is_monotonic)
+ self.assertTrue(idx.is_unique)
+
+ idx = IntervalIndex.from_tuples([(0, 1), (2, 3), (1, 2)])
+ self.assertFalse(idx.is_monotonic)
+ self.assertTrue(idx.is_unique)
+
+ idx = IntervalIndex.from_tuples([(0, 2), (0, 2)])
+ self.assertFalse(idx.is_unique)
+ self.assertTrue(idx.is_monotonic)
+
+ def test_repr(self):
+ expected = ("IntervalIndex(left=[0, 1],\n right=[1, 2],"
+ "\n closed='right')")
+ IntervalIndex((0, 1), (1, 2), closed='right')
+ self.assertEqual(repr(self.index), expected)
+
+ def test_get_loc_value(self):
+ self.assertRaises(KeyError, self.index.get_loc, 0)
+ self.assertEqual(self.index.get_loc(0.5), 0)
+ self.assertEqual(self.index.get_loc(1), 0)
+ self.assertEqual(self.index.get_loc(1.5), 1)
+ self.assertEqual(self.index.get_loc(2), 1)
+ self.assertRaises(KeyError, self.index.get_loc, -1)
+ self.assertRaises(KeyError, self.index.get_loc, 3)
+
+ idx = IntervalIndex.from_tuples([(0, 2), (1, 3)])
+ self.assertEqual(idx.get_loc(0.5), 0)
+ self.assertEqual(idx.get_loc(1), 0)
+ self.assert_numpy_array_equal(idx.get_loc(1.5), [0, 1])
+ self.assert_numpy_array_equal(np.sort(idx.get_loc(2)), [0, 1])
+ self.assertEqual(idx.get_loc(3), 1)
+ self.assertRaises(KeyError, idx.get_loc, 3.5)
+
+ idx = IntervalIndex([0, 2], [1, 3])
+ self.assertRaises(KeyError, idx.get_loc, 1.5)
+
+ def slice_locs_cases(self, breaks):
+ # TODO: same tests for more index types
+ index = IntervalIndex.from_breaks([0, 1, 2], closed='right')
+ self.assertEqual(index.slice_locs(), (0, 2))
+ self.assertEqual(index.slice_locs(0, 1), (0, 1))
+ self.assertEqual(index.slice_locs(1, 1), (0, 1))
+ self.assertEqual(index.slice_locs(0, 2), (0, 2))
+ self.assertEqual(index.slice_locs(0.5, 1.5), (0, 2))
+ self.assertEqual(index.slice_locs(0, 0.5), (0, 1))
+ self.assertEqual(index.slice_locs(start=1), (0, 2))
+ self.assertEqual(index.slice_locs(start=1.2), (1, 2))
+ self.assertEqual(index.slice_locs(end=1), (0, 1))
+ self.assertEqual(index.slice_locs(end=1.1), (0, 2))
+ self.assertEqual(index.slice_locs(end=1.0), (0, 1))
+ self.assertEqual(*index.slice_locs(-1, -1))
+
+ index = IntervalIndex.from_breaks([0, 1, 2], closed='neither')
+ self.assertEqual(index.slice_locs(0, 1), (0, 1))
+ self.assertEqual(index.slice_locs(0, 2), (0, 2))
+ self.assertEqual(index.slice_locs(0.5, 1.5), (0, 2))
+ self.assertEqual(index.slice_locs(1, 1), (1, 1))
+ self.assertEqual(index.slice_locs(1, 2), (1, 2))
+
+ index = IntervalIndex.from_breaks([0, 1, 2], closed='both')
+ self.assertEqual(index.slice_locs(1, 1), (0, 2))
+ self.assertEqual(index.slice_locs(1, 2), (0, 2))
+
+ def test_slice_locs_int64(self):
+ self.slice_locs_cases([0, 1, 2])
+
+ def test_slice_locs_float64(self):
+ self.slice_locs_cases([0.0, 1.0, 2.0])
+
+ def slice_locs_decreasing_cases(self, tuples):
+ index = IntervalIndex.from_tuples(tuples)
+ self.assertEqual(index.slice_locs(1.5, 0.5), (1, 3))
+ self.assertEqual(index.slice_locs(2, 0), (1, 3))
+ self.assertEqual(index.slice_locs(2, 1), (1, 3))
+ self.assertEqual(index.slice_locs(3, 1.1), (0, 3))
+ self.assertEqual(index.slice_locs(3, 3), (0, 2))
+ self.assertEqual(index.slice_locs(3.5, 3.3), (0, 1))
+ self.assertEqual(index.slice_locs(1, -3), (2, 3))
+ self.assertEqual(*index.slice_locs(-1, -1))
+
+ def test_slice_locs_decreasing_int64(self):
+ self.slice_locs_cases([(2, 4), (1, 3), (0, 2)])
+
+ def test_slice_locs_decreasing_float64(self):
+ self.slice_locs_cases([(2., 4.), (1., 3.), (0., 2.)])
+
+ def test_slice_locs_fails(self):
+ index = IntervalIndex.from_tuples([(1, 2), (0, 1), (2, 3)])
+ with self.assertRaises(KeyError):
+ index.slice_locs(1, 2)
+
+ def test_get_loc_interval(self):
+ self.assertEqual(self.index.get_loc(Interval(0, 1)), 0)
+ self.assertEqual(self.index.get_loc(Interval(0, 0.5)), 0)
+ self.assertEqual(self.index.get_loc(Interval(0, 1, 'left')), 0)
+ self.assertRaises(KeyError, self.index.get_loc, Interval(2, 3))
+ self.assertRaises(KeyError, self.index.get_loc, Interval(-1, 0, 'left'))
+
+ def test_get_indexer(self):
+ actual = self.index.get_indexer([-1, 0, 0.5, 1, 1.5, 2, 3])
+ expected = [-1, -1, 0, 0, 1, 1, -1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = self.index.get_indexer(self.index)
+ expected = [0, 1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ index = IntervalIndex.from_breaks([0, 1, 2], closed='left')
+ actual = index.get_indexer([-1, 0, 0.5, 1, 1.5, 2, 3])
+ expected = [-1, 0, 0, 1, 1, -1, -1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = self.index.get_indexer(index[:1])
+ expected = [0]
+ self.assert_numpy_array_equal(actual, expected)
+
+ self.assertRaises(ValueError, self.index.get_indexer, index)
+
+ def test_get_indexer_subintervals(self):
+ # return indexers for wholly contained subintervals
+ target = IntervalIndex.from_breaks(np.linspace(0, 2, 5))
+ actual = self.index.get_indexer(target)
+ expected = [0, 0, 1, 1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ target = IntervalIndex.from_breaks([0, 0.67, 1.33, 2])
+ self.assertRaises(ValueError, self.index.get_indexer, target)
+
+ actual = self.index.get_indexer(target[[0, -1]])
+ expected = [0, 1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ target = IntervalIndex.from_breaks([0, 0.33, 0.67, 1], closed='left')
+ actual = self.index.get_indexer(target)
+ expected = [0, 0, 0]
+ self.assert_numpy_array_equal(actual, expected)
+
+ def test_contains(self):
+ self.assertNotIn(0, self.index)
+ self.assertIn(0.5, self.index)
+ self.assertIn(2, self.index)
+
+ self.assertIn(Interval(0, 1), self.index)
+ self.assertIn(Interval(0, 2), self.index)
+ self.assertIn(Interval(0, 0.5), self.index)
+ self.assertNotIn(Interval(3, 5), self.index)
+ self.assertNotIn(Interval(-1, 0, closed='left'), self.index)
+
+ def test_non_contiguous(self):
+ index = IntervalIndex.from_tuples([(0, 1), (2, 3)])
+ target = [0.5, 1.5, 2.5]
+ actual = index.get_indexer(target)
+ expected = [0, -1, 1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ self.assertNotIn(1.5, index)
+
+ def test_union(self):
+ other = IntervalIndex([2], [3])
+ expected = IntervalIndex(range(3), range(1, 4))
+ actual = self.index.union(other)
+ self.assertTrue(expected.equals(actual))
+
+ actual = other.union(self.index)
+ self.assertTrue(expected.equals(actual))
+
+ self.assert_numpy_array_equal(self.index.union(self.index), self.index)
+ self.assert_numpy_array_equal(self.index.union(self.index[:1]),
+ self.index)
+
+ def test_intersection(self):
+ other = IntervalIndex.from_breaks([1, 2, 3])
+ expected = IntervalIndex.from_breaks([1, 2])
+ actual = self.index.intersection(other)
+ self.assertTrue(expected.equals(actual))
+
+ self.assert_numpy_array_equal(self.index.intersection(self.index),
+ self.index)
+
+ def test_difference(self):
+ self.assert_numpy_array_equal(self.index.difference(self.index[:1]),
+ self.index[1:])
+
+ def test_sym_diff(self):
+ self.assert_numpy_array_equal(self.index[:1].sym_diff(self.index[1:]),
+ self.index)
+
+ def test_set_operation_errors(self):
+ self.assertRaises(ValueError, self.index.union, self.index.left)
+
+ other = IntervalIndex.from_breaks([0, 1, 2], closed='neither')
+ self.assertRaises(ValueError, self.index.union, other)
+
+ def test_isin(self):
+ actual = self.index.isin(self.index)
+ self.assert_numpy_array_equal([True, True], actual)
+
+ actual = self.index.isin(self.index[:1])
+ self.assert_numpy_array_equal([True, False], actual)
+
+ def test_comparison(self):
+ actual = Interval(0, 1) < self.index
+ expected = [False, True]
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = Interval(0.5, 1.5) < self.index
+ expected = [False, True]
+ self.assert_numpy_array_equal(actual, expected)
+ actual = self.index > Interval(0.5, 1.5)
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = self.index == self.index
+ expected = [True, True]
+ self.assert_numpy_array_equal(actual, expected)
+ actual = self.index <= self.index
+ self.assert_numpy_array_equal(actual, expected)
+ actual = self.index >= self.index
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = self.index < self.index
+ expected = [False, False]
+ self.assert_numpy_array_equal(actual, expected)
+ actual = self.index > self.index
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = self.index == IntervalIndex.from_breaks([0, 1, 2], 'left')
+ self.assert_numpy_array_equal(actual, expected)
+
+ actual = self.index == self.index.values
+ self.assert_numpy_array_equal(actual, [True, True])
+ actual = self.index.values == self.index
+ self.assert_numpy_array_equal(actual, [True, True])
+ actual = self.index <= self.index.values
+ self.assert_numpy_array_equal(actual, [True, True])
+ actual = self.index != self.index.values
+ self.assert_numpy_array_equal(actual, [False, False])
+ actual = self.index > self.index.values
+ self.assert_numpy_array_equal(actual, [False, False])
+ actual = self.index.values > self.index
+ self.assert_numpy_array_equal(actual, [False, False])
+
+ # invalid comparisons
+ actual = self.index == 0
+ self.assert_numpy_array_equal(actual, [False, False])
+ actual = self.index == self.index.left
+ self.assert_numpy_array_equal(actual, [False, False])
+
+ with self.assertRaisesRegexp(TypeError, 'unorderable types'):
+ self.index > 0
+ with self.assertRaisesRegexp(TypeError, 'unorderable types'):
+ self.index <= 0
+ with self.assertRaises(TypeError):
+ self.index > np.arange(2)
+ with self.assertRaises(ValueError):
+ self.index > np.arange(3)
+
+ def test_missing_values(self):
+ idx = pd.Index([np.nan, pd.Interval(0, 1), pd.Interval(1, 2)])
+ idx2 = pd.IntervalIndex([np.nan, 0, 1], [np.nan, 1, 2])
+ assert idx.equals(idx2)
+
+ with tm.assertRaisesRegexp(ValueError, 'both left and right sides'):
+ pd.IntervalIndex([np.nan, 0, 1], [0, 1, 2])
+
+ self.assert_numpy_array_equal(pd.isnull(idx), [True, False, False])
+
+ def test_order(self):
+ expected = IntervalIndex.from_breaks([1, 2, 3, 4])
+ actual = IntervalIndex.from_tuples([(3, 4), (1, 2), (2, 3)]).order()
+ self.assert_numpy_array_equal(expected, actual)
+
+ def test_datetime(self):
+ dates = pd.date_range('2000', periods=3)
+ idx = IntervalIndex.from_breaks(dates)
+
+ self.assert_numpy_array_equal(idx.left, dates[:2])
+ self.assert_numpy_array_equal(idx.right, dates[-2:])
+
+ expected = pd.date_range('2000-01-01T12:00', periods=2)
+ self.assert_numpy_array_equal(idx.mid, expected)
+
+ self.assertIn('2000-01-01T12', idx)
+
+ target = pd.date_range('1999-12-31T12:00', periods=7, freq='12H')
+ actual = idx.get_indexer(target)
+ expected = [-1, -1, 0, 0, 1, 1, -1]
+ self.assert_numpy_array_equal(actual, expected)
+
+ # def test_math(self):
+ # # add, subtract, multiply, divide with scalars should be OK
+ # actual = 2 * self.index + 1
+ # expected = IntervalIndex.from_breaks((2 * np.arange(3) + 1))
+ # self.assertTrue(expected.equals(actual))
+
+ # actual = self.index / 2.0 - 1
+ # expected = IntervalIndex.from_breaks((np.arange(3) / 2.0 - 1))
+ # self.assertTrue(expected.equals(actual))
+
+ # with self.assertRaises(TypeError):
+ # # doesn't make sense to add two IntervalIndex objects
+ # self.index + self.index
+
+ # def test_datetime_math(self):
+
+ # expected = IntervalIndex(pd.date_range('2000-01-02', periods=3))
+ # actual = idx + pd.to_timedelta(1, unit='D')
+ # self.assertTrue(expected.equals(actual))
+
+ # TODO: other set operations (left join, right join, intersection),
+ # set operations with conflicting IntervalIndex objects or other dtypes,
+ # groupby, cut, reset_index...
diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py
index eac6973bffb25..68eab2df0a516 100644
--- a/pandas/tools/tests/test_tile.py
+++ b/pandas/tools/tests/test_tile.py
@@ -4,12 +4,14 @@
import numpy as np
from pandas.compat import zip
-from pandas import DataFrame, Series, unique
+from pandas import DataFrame, Series, Index, unique, isnull
import pandas.util.testing as tm
from pandas.util.testing import assertRaisesRegexp
import pandas.core.common as com
from pandas.core.algorithms import quantile
+from pandas.core.categorical import Categorical
+from pandas.core.interval import Interval, IntervalIndex
from pandas.tools.tile import cut, qcut
import pandas.tools.tile as tmod
@@ -25,25 +27,29 @@ def test_simple(self):
def test_bins(self):
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1])
result, bins = cut(data, 3, retbins=True)
- tm.assert_numpy_array_equal(result.codes, [0, 0, 0, 1, 2, 0])
+ intervals = IntervalIndex.from_breaks(bins.round(3))
+ tm.assert_numpy_array_equal(result, intervals.take([0, 0, 0, 1, 2, 0]))
tm.assert_almost_equal(bins, [0.1905, 3.36666667, 6.53333333, 9.7])
def test_right(self):
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
result, bins = cut(data, 4, right=True, retbins=True)
- tm.assert_numpy_array_equal(result.codes, [0, 0, 0, 2, 3, 0, 0])
+ intervals = IntervalIndex.from_breaks(bins.round(3))
+ tm.assert_numpy_array_equal(result, intervals.take([0, 0, 0, 2, 3, 0, 0]))
tm.assert_almost_equal(bins, [0.1905, 2.575, 4.95, 7.325, 9.7])
def test_noright(self):
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
result, bins = cut(data, 4, right=False, retbins=True)
- tm.assert_numpy_array_equal(result.codes, [0, 0, 0, 2, 3, 0, 1])
+ intervals = IntervalIndex.from_breaks(bins.round(3), closed='left')
+ tm.assert_numpy_array_equal(result, intervals.take([0, 0, 0, 2, 3, 0, 1]))
tm.assert_almost_equal(bins, [0.2, 2.575, 4.95, 7.325, 9.7095])
def test_arraylike(self):
data = [.2, 1.4, 2.5, 6.2, 9.7, 2.1]
result, bins = cut(data, 3, retbins=True)
- tm.assert_numpy_array_equal(result.codes, [0, 0, 0, 1, 2, 0])
+ intervals = IntervalIndex.from_breaks(bins.round(3))
+ tm.assert_numpy_array_equal(result, intervals.take([0, 0, 0, 1, 2, 0]))
tm.assert_almost_equal(bins, [0.1905, 3.36666667, 6.53333333, 9.7])
def test_bins_not_monotonic(self):
@@ -72,14 +78,13 @@ def test_labels(self):
arr = np.tile(np.arange(0, 1.01, 0.1), 4)
result, bins = cut(arr, 4, retbins=True)
- ex_levels = ['(-0.001, 0.25]', '(0.25, 0.5]', '(0.5, 0.75]',
- '(0.75, 1]']
- self.assert_numpy_array_equal(result.categories, ex_levels)
+ ex_levels = IntervalIndex.from_breaks([-1e-3, 0.25, 0.5, 0.75, 1])
+ self.assert_numpy_array_equal(unique(result), ex_levels)
result, bins = cut(arr, 4, retbins=True, right=False)
- ex_levels = ['[0, 0.25)', '[0.25, 0.5)', '[0.5, 0.75)',
- '[0.75, 1.001)']
- self.assert_numpy_array_equal(result.categories, ex_levels)
+ ex_levels = IntervalIndex.from_breaks([0, 0.25, 0.5, 0.75, 1 + 1e-3],
+ closed='left')
+ self.assert_numpy_array_equal(unique(result), ex_levels)
def test_cut_pass_series_name_to_factor(self):
s = Series(np.random.randn(100), name='foo')
@@ -91,9 +96,8 @@ def test_label_precision(self):
arr = np.arange(0, 0.73, 0.01)
result = cut(arr, 4, precision=2)
- ex_levels = ['(-0.00072, 0.18]', '(0.18, 0.36]', '(0.36, 0.54]',
- '(0.54, 0.72]']
- self.assert_numpy_array_equal(result.categories, ex_levels)
+ ex_levels = IntervalIndex.from_breaks([-0.00072, 0.18, 0.36, 0.54, 0.72])
+ self.assert_numpy_array_equal(unique(result), ex_levels)
def test_na_handling(self):
arr = np.arange(0, 0.75, 0.01)
@@ -115,17 +119,16 @@ def test_inf_handling(self):
data = np.arange(6)
data_ser = Series(data,dtype='int64')
- result = cut(data, [-np.inf, 2, 4, np.inf])
- result_ser = cut(data_ser, [-np.inf, 2, 4, np.inf])
+ bins = [-np.inf, 2, 4, np.inf]
+ result = cut(data, bins)
+ result_ser = cut(data_ser, bins)
- ex_categories = ['(-inf, 2]', '(2, 4]', '(4, inf]']
-
- tm.assert_numpy_array_equal(result.categories, ex_categories)
- tm.assert_numpy_array_equal(result_ser.cat.categories, ex_categories)
- self.assertEqual(result[5], '(4, inf]')
- self.assertEqual(result[0], '(-inf, 2]')
- self.assertEqual(result_ser[5], '(4, inf]')
- self.assertEqual(result_ser[0], '(-inf, 2]')
+ ex_uniques = IntervalIndex.from_breaks(bins).values
+ tm.assert_numpy_array_equal(unique(result), ex_uniques)
+ self.assertEqual(result[5], Interval(4, np.inf))
+ self.assertEqual(result[0], Interval(-np.inf, 2))
+ self.assertEqual(result_ser[5], Interval(4, np.inf))
+ self.assertEqual(result_ser[0], Interval(-np.inf, 2))
def test_qcut(self):
arr = np.random.randn(1000)
@@ -148,7 +151,7 @@ def test_qcut_specify_quantiles(self):
factor = qcut(arr, [0, .25, .5, .75, 1.])
expected = qcut(arr, 4)
- self.assertTrue(factor.equals(expected))
+ self.assert_numpy_array_equal(factor, expected)
def test_qcut_all_bins_same(self):
assertRaisesRegexp(ValueError, "edges.*unique", qcut, [0,0,0,0,0,0,0,0,0,0], 3)
@@ -158,7 +161,7 @@ def test_cut_out_of_bounds(self):
result = cut(arr, [-1, 0, 1])
- mask = result.codes == -1
+ mask = isnull(result)
ex_mask = (arr < -1) | (arr > 1)
self.assert_numpy_array_equal(mask, ex_mask)
@@ -168,10 +171,11 @@ def test_cut_pass_labels(self):
labels = ['Small', 'Medium', 'Large']
result = cut(arr, bins, labels=labels)
+ exp = ['Medium'] + 4 * ['Small'] + ['Medium', 'Large']
+ self.assert_numpy_array_equal(result, exp)
- exp = cut(arr, bins)
- exp.categories = labels
-
+ result = cut(arr, bins, labels=Categorical.from_codes([0, 1, 2], labels))
+ exp = Categorical.from_codes([1] + 4 * [0] + [1, 2], labels)
self.assertTrue(result.equals(exp))
def test_qcut_include_lowest(self):
@@ -179,8 +183,9 @@ def test_qcut_include_lowest(self):
cats = qcut(values, 4)
- ex_levels = ['[0, 2.25]', '(2.25, 4.5]', '(4.5, 6.75]', '(6.75, 9]']
- self.assertTrue((cats.categories == ex_levels).all())
+ ex_levels = [Interval(0, 2.25, closed='both'), Interval(2.25, 4.5),
+ Interval(4.5, 6.75), Interval(6.75, 9)]
+ self.assert_numpy_array_equal(unique(cats), ex_levels)
def test_qcut_nas(self):
arr = np.random.randn(100)
@@ -189,9 +194,15 @@ def test_qcut_nas(self):
result = qcut(arr, 4)
self.assertTrue(com.isnull(result[:20]).all())
- def test_label_formatting(self):
- self.assertEqual(tmod._trim_zeros('1.000'), '1')
+ def test_qcut_index(self):
+ # the result is closed on a different side for the first interval, but
+ # we should still be able to make an index
+ result = qcut([0, 2], 2)
+ index = Index(result)
+ expected = Index([Interval(0, 1, closed='both'), Interval(1, 2)])
+ self.assert_numpy_array_equal(index, expected)
+ def test_round_frac(self):
# it works
result = cut(np.arange(11.), 2)
@@ -199,10 +210,15 @@ def test_label_formatting(self):
# #1979, negative numbers
- result = tmod._format_label(-117.9998, precision=3)
- self.assertEqual(result, '-118')
- result = tmod._format_label(117.9998, precision=3)
- self.assertEqual(result, '118')
+ result = tmod._round_frac(-117.9998, precision=3)
+ self.assertEqual(result, -118)
+ result = tmod._round_frac(117.9998, precision=3)
+ self.assertEqual(result, 118)
+
+ result = tmod._round_frac(117.9998, precision=2)
+ self.assertEqual(result, 118)
+ result = tmod._round_frac(0.000123456, precision=2)
+ self.assertEqual(result, 0.00012)
def test_qcut_binning_issues(self):
# #1978, 1979
@@ -214,9 +230,9 @@ def test_qcut_binning_issues(self):
starts = []
ends = []
- for lev in result.categories:
- s, e = lev[1:-1].split(',')
-
+ for lev in np.unique(result):
+ s = lev.left
+ e = lev.right
self.assertTrue(s != e)
starts.append(float(s))
@@ -228,33 +244,31 @@ def test_qcut_binning_issues(self):
self.assertTrue(ep < en)
self.assertTrue(ep <= sn)
- def test_cut_return_categorical(self):
- from pandas import Categorical
+ def test_cut_return_intervals(self):
s = Series([0,1,2,3,4,5,6,7,8])
res = cut(s,3)
- exp = Series(Categorical.from_codes([0,0,0,1,1,1,2,2,2],
- ["(-0.008, 2.667]", "(2.667, 5.333]", "(5.333, 8]"],
- ordered=True))
+ exp_bins = np.linspace(0, 8, num=4).round(3)
+ exp_bins[0] -= 0.008
+ exp = Series(IntervalIndex.from_breaks(exp_bins).take([0,0,0,1,1,1,2,2,2]))
tm.assert_series_equal(res, exp)
- def test_qcut_return_categorical(self):
- from pandas import Categorical
+ def test_qcut_return_intervals(self):
s = Series([0,1,2,3,4,5,6,7,8])
res = qcut(s,[0,0.333,0.666,1])
- exp = Series(Categorical.from_codes([0,0,0,1,1,1,2,2,2],
- ["[0, 2.664]", "(2.664, 5.328]", "(5.328, 8]"],
- ordered=True))
+ exp_levels = np.array([Interval(0, 2.664, closed='both'),
+ Interval(2.664, 5.328), Interval(5.328, 8)])
+ exp = Series(exp_levels.take([0,0,0,1,1,1,2,2,2]))
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)
- tm.assert_numpy_array_equal(result.cat.codes.values, [0, 0, 1, 1])
+ result, bins = cut(s, 2, retbins=True, labels=[0, 1])
+ tm.assert_numpy_array_equal(result, [0, 0, 1, 1])
tm.assert_almost_equal(bins, [-0.003, 1.5, 3])
- result, bins = qcut(s, 2, retbins=True)
- tm.assert_numpy_array_equal(result.cat.codes.values, [0, 0, 1, 1])
+ result, bins = qcut(s, 2, retbins=True, labels=[0, 1])
+ tm.assert_numpy_array_equal(result, [0, 0, 1, 1])
tm.assert_almost_equal(bins, [0, 1.5, 3])
diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py
index 416addfcf2ad5..85ec6b898a8f2 100644
--- a/pandas/tools/tile.py
+++ b/pandas/tools/tile.py
@@ -5,6 +5,7 @@
from pandas.core.api import DataFrame, Series
from pandas.core.categorical import Categorical
from pandas.core.index import _ensure_index
+from pandas.core.interval import IntervalIndex, Interval
import pandas.core.algorithms as algos
import pandas.core.common as com
import pandas.core.nanops as nanops
@@ -12,6 +13,8 @@
import numpy as np
+import warnings
+
def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
include_lowest=False):
@@ -39,9 +42,9 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
retbins : bool, optional
Whether to return the bins or not. Can be useful if bins is given
as a scalar.
- precision : int
+ precision : int, optional
The precision at which to store and display the bins labels
- include_lowest : bool
+ include_lowest : bool, optional
Whether the first interval should be left-inclusive or not.
Returns
@@ -84,12 +87,11 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
except AttributeError:
x = np.asarray(x)
sz = x.size
+
if sz == 0:
raise ValueError('Cannot cut empty array')
- # handle empty arrays. Can't determine range, so use 0-1.
- # rng = (0, 1)
- else:
- rng = (nanops.nanmin(x), nanops.nanmax(x))
+
+ rng = (nanops.nanmin(x), nanops.nanmax(x))
mn, mx = [mi + 0.0 for mi in rng]
if mn == mx: # adjust end points before binning
@@ -109,9 +111,8 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
if (np.diff(bins) < 0).any():
raise ValueError('bins must increase monotonically.')
- return _bins_to_cuts(x, bins, right=right, labels=labels,retbins=retbins, precision=precision,
- include_lowest=include_lowest)
-
+ return _bins_to_cuts(x, bins, right=right, labels=labels, retbins=retbins,
+ precision=precision, include_lowest=include_lowest)
def qcut(x, q, labels=None, retbins=False, precision=3):
@@ -133,7 +134,7 @@ def qcut(x, q, labels=None, retbins=False, precision=3):
retbins : bool, optional
Whether to return the bins or not. Can be useful if bins is given
as a scalar.
- precision : int
+ precision : int, optional
The precision at which to store and display the bins labels
Returns
@@ -165,13 +166,12 @@ def qcut(x, q, labels=None, retbins=False, precision=3):
else:
quantiles = q
bins = algos.quantile(x, quantiles)
- return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,precision=precision,
- include_lowest=True)
-
+ 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):
+ precision=None, name=None, include_lowest=False):
x_is_series = isinstance(x, Series)
series_index = None
@@ -196,102 +196,60 @@ def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False,
if labels is not False:
if labels is None:
- increases = 0
- while True:
- try:
- levels = _format_levels(bins, precision, right=right,
- include_lowest=include_lowest)
- except ValueError:
- increases += 1
- precision += 1
- if increases >= 20:
- raise
- else:
- break
+ closed = 'right' if right else 'left'
+ precision = _infer_precision(precision, bins)
+ breaks = [_round_frac(b, precision) for b in bins]
+ labels = IntervalIndex.from_breaks(breaks, closed=closed).values
+
+ if right and include_lowest:
+ labels[0] = Interval(labels[0].left, labels[0].right,
+ closed='both')
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
- levels = labels
- levels = np.asarray(levels, dtype=object)
+ if not com.is_categorical(labels):
+ labels = np.asarray(labels)
+
np.putmask(ids, na_mask, 0)
- fac = Categorical(ids - 1, levels, ordered=True, fastpath=True)
+ result = com.take_nd(labels, ids - 1)
+
else:
- fac = ids - 1
+ result = ids - 1
if has_nas:
- fac = fac.astype(np.float64)
- np.putmask(fac, na_mask, np.nan)
+ result = result.astype(np.float64)
+ np.putmask(result, na_mask, np.nan)
if x_is_series:
- fac = Series(fac, index=series_index, name=name)
+ result = Series(result, index=series_index, name=name)
if not retbins:
- return fac
-
- return fac, bins
+ return result
+ return result, bins
-def _format_levels(bins, prec, right=True,
- include_lowest=False):
- fmt = lambda v: _format_label(v, precision=prec)
- if right:
- levels = []
- for a, b in zip(bins, bins[1:]):
- fa, fb = fmt(a), fmt(b)
- if a != b and fa == fb:
- raise ValueError('precision too low')
-
- formatted = '(%s, %s]' % (fa, fb)
-
- levels.append(formatted)
-
- if include_lowest:
- levels[0] = '[' + levels[0][1:]
+def _round_frac(x, precision):
+ """Round the fractional part of the given number
+ """
+ if not np.isfinite(x) or x == 0:
+ return x
else:
- levels = ['[%s, %s)' % (fmt(a), fmt(b))
- for a, b in zip(bins, bins[1:])]
-
- return levels
-
-
-def _format_label(x, precision=3):
- fmt_str = '%%.%dg' % precision
- if np.isinf(x):
- return str(x)
- elif com.is_float(x):
frac, whole = np.modf(x)
- sgn = '-' if x < 0 else ''
- whole = abs(whole)
- if frac != 0.0:
- val = fmt_str % frac
-
- # rounded up or down
- if '.' not in val:
- if x < 0:
- return '%d' % (-whole - 1)
- else:
- return '%d' % (whole + 1)
-
- if 'e' in val:
- return _trim_zeros(fmt_str % x)
- else:
- val = _trim_zeros(val)
- if '.' in val:
- return sgn + '.'.join(('%d' % whole, val.split('.')[1]))
- else: # pragma: no cover
- return sgn + '.'.join(('%d' % whole, val))
+ if whole == 0:
+ digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision
else:
- return sgn + '%0.f' % whole
- else:
- return str(x)
+ digits = precision
+ return np.around(x, digits)
-def _trim_zeros(x):
- while len(x) > 1 and x[-1] == '0':
- x = x[:-1]
- if len(x) > 1 and x[-1] == '.':
- x = x[:-1]
- return x
+def _infer_precision(base_precision, bins):
+ """Infer an appropriate precision for _round_frac
+ """
+ for precision in range(base_precision, 20):
+ levels = [_round_frac(b, precision) for b in bins]
+ if algos.unique(levels).size == bins.size:
+ return precision
+ return base_precision # default
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index c01a7c1d2c240..f6fbeb217875a 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -36,7 +36,8 @@
from pandas.computation import expressions as expr
-from pandas import (bdate_range, CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex,
+from pandas import (bdate_range, CategoricalIndex, IntervalIndex,
+ DatetimeIndex, TimedeltaIndex, PeriodIndex,
Index, MultiIndex, Series, DataFrame, Panel, Panel4D)
from pandas.util.decorators import deprecate
from pandas import _testing
@@ -1121,6 +1122,11 @@ def makeCategoricalIndex(k=10, n=3, name=None):
x = rands_array(nchars=4, size=n)
return CategoricalIndex(np.random.choice(x,k), name=name)
+def makeIntervalIndex(k=10, name=None):
+ """ make a length k IntervalIndex """
+ x = np.linspace(0, 100, num=(k + 1))
+ return IntervalIndex.from_breaks(x, name=name)
+
def makeBoolIndex(k=10, name=None):
if k == 1:
return Index([True], name=name)
| closes #7640
closes #8625
This is a work in progress, but it's far enough along that I'd love to get
some feedback.
TODOs (more called out in the code):
- [ ] documentation
- [x] docstrings
- [x] implement an IntervalTree under the hood to handle index methods
- [x] index methods handle point queries properly:
- [x] `get_loc`
- [x] `get_indexer`
- [x] `slice_locs`
- [x] index methods handle interval queries properly
- [x] `get_loc`
- [x] `get_indexer` (for non-overlapping monotonic indexes)
- [x] comparison operations (should be lexicographic)
- [x] ensure `is_monotonic` always works (also lexicographic)
- [x] ensure `order` works
- [x] cast arrays of intervals when input to `pd.Index`
- [x] cythonize the bottlenecks:
- [x] `values`
- [x] `from_intervals`?
- [x] `Interval`?
- [ ] `from_tuples`?
- [ ] `Categorical`/`cut`
- [ ] nice to haves (not essential for MVP)
- [ ] `get_indexer` in full generality (for randomly ordered indexes)
- [ ] support for arithmetic operations
- [ ] `MultiIndex` -- should at least give a sane error messages
- [ ] serialization (HDF5) -- should at least raise `NotImplementedError`
- [ ] consider creating `interval_range` -- like `period_range`
- [ ] add `to_interval` for casting strings (e.g., `to_interval('(0, 1]')`)
- [ ] release the GIL in IntervalTree queries
- [ ] loads more tests:
- [x] `Series.loc.__getitem__`
- [ ] `Series.loc.__setitem__`
- [ ] indexing a dataframe
- [ ] groupby
- [x] reset_index
- [x] add tests in test_index for the subcommon API
CC @jreback @cpcloud @immerrr
| https://api.github.com/repos/pandas-dev/pandas/pulls/8707 | 2014-11-02T09:08:13Z | 2016-09-09T22:48:05Z | null | 2017-02-05T00:50:03Z |
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 |
BUG: setitem fails on mixed-type Panel4D | diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt
index e96adc2bd9559..96dd8e2fe8e59 100644
--- a/doc/source/whatsnew/v0.15.1.txt
+++ b/doc/source/whatsnew/v0.15.1.txt
@@ -227,3 +227,5 @@ 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`)
+
+- Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue: `8702`)
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 954acb0f95159..048e4af20d02f 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -355,7 +355,7 @@ def _setitem_with_indexer(self, indexer, value):
# if we have a partial multiindex, then need to adjust the plane
# indexer here
if (len(labels) == 1 and
- isinstance(self.obj[labels[0]].index, MultiIndex)):
+ isinstance(self.obj[labels[0]].axes[0], MultiIndex)):
item = labels[0]
obj = self.obj[item]
index = obj.index
diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py
index e88a8c3b2874c..6ef8c1820400a 100644
--- a/pandas/tests/test_panel4d.py
+++ b/pandas/tests/test_panel4d.py
@@ -372,6 +372,53 @@ def test_setitem(self):
self.panel4d['lP'] = self.panel4d['l1'] > 0
self.assertEqual(self.panel4d['lP'].values.dtype, np.bool_)
+ def test_setitem_by_indexer(self):
+
+ # Panel
+ panel4dc = self.panel4d.copy()
+ p = panel4dc.iloc[0]
+ def func():
+ self.panel4d.iloc[0] = p
+ self.assertRaises(NotImplementedError, func)
+
+ # DataFrame
+ panel4dc = self.panel4d.copy()
+ df = panel4dc.iloc[0,0]
+ df.iloc[:] = 1
+ panel4dc.iloc[0,0] = df
+ self.assertTrue((panel4dc.iloc[0,0].values == 1).all())
+
+ # Series
+ panel4dc = self.panel4d.copy()
+ s = panel4dc.iloc[0,0,:,0]
+ s.iloc[:] = 1
+ panel4dc.iloc[0,0,:,0] = s
+ self.assertTrue((panel4dc.iloc[0,0,:,0].values == 1).all())
+
+ # scalar
+ panel4dc = self.panel4d.copy()
+ panel4dc.iloc[0] = 1
+ panel4dc.iloc[1] = True
+ panel4dc.iloc[2] = 'foo'
+ self.assertTrue((panel4dc.iloc[0].values == 1).all())
+ self.assertTrue(panel4dc.iloc[1].values.all())
+ self.assertTrue((panel4dc.iloc[2].values == 'foo').all())
+
+ def test_setitem_by_indexer_mixed_type(self):
+ # GH 8702
+ self.panel4d['foo'] = 'bar'
+
+ # scalar
+ panel4dc = self.panel4d.copy()
+ panel4dc.iloc[0] = 1
+ panel4dc.iloc[1] = True
+ panel4dc.iloc[2] = 'foo'
+ self.assertTrue((panel4dc.iloc[0].values == 1).all())
+ self.assertTrue(panel4dc.iloc[1].values.all())
+ self.assertTrue((panel4dc.iloc[2].values == 'foo').all())
+
+
+
def test_comparisons(self):
p1 = tm.makePanel4D()
p2 = tm.makePanel4D()
| Trying to set a value on a mixed-type Panel4D currently fails:
```
In [1]: from pandas.util import testing as tmd
In [2]: p4d = tmd.makePanel4D()
In [3]: p4d['foo'] = 'bar'
In [4]: p4d.iloc[0,0,0,0] = 0
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-bb74b95f7244> in <module>()
----> 1 p4d.iloc[0,0,0,0] = 0
/home/stahlous/pd_dev/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
119 indexer = self._convert_to_indexer(key, is_setter=True)
120
--> 121 self._setitem_with_indexer(indexer, value)
122
123 def _has_valid_type(self, k, axis):
/home/stahlous/pd_dev/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
356 # indexer here
357 if (len(labels) == 1 and
--> 358 isinstance(self.obj[labels[0]].index, MultiIndex)):
359 item = labels[0]
360 obj = self.obj[item]
/home/stahlous/pd_dev/pandas/pandas/core/generic.py in __getattr__(self, name)
1927 """
1928 if name in self._internal_names_set:
-> 1929 return object.__getattribute__(self, name)
1930 elif name in self._metadata:
1931 return object.__getattribute__(self, name)
AttributeError: 'Panel' object has no attribute 'index'
```
Fortunately, it looks like a simple fix. If this looks OK. I'll add a note to `v0.15.1.txt` (doesn't have a GH issue number yet).
This fix is needed for my latest attempt at the fillna bug #8395.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8702 | 2014-11-01T18:38:12Z | 2014-11-02T14:09:38Z | null | 2014-11-02T15:37:28Z |
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 | |
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/8690 | 2014-10-31T07:56:15Z | 2014-10-31T08:29:48Z | null | 2014-10-31T09:48:46Z | |
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 |
DOC: update docs on direct plotting with matplotlib (GH8614) | diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index f30d6c9d5d4c0..dcaed58e61af4 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -1566,15 +1566,11 @@ customization is not (yet) supported by pandas. Series and DataFrame objects
behave like arrays and can therefore be passed directly to matplotlib functions
without explicit casts.
-pandas also automatically registers formatters and locators that recognize date
-indices, thereby extending date and time support to practically all plot types
-available in matplotlib. Although this formatting does not provide the same
-level of refinement you would get when plotting via pandas, it can be faster
-when plotting a large number of points.
-
.. note::
- The speed up for large data sets only applies to pandas 0.14.0 and later.
+ Starting from pandas 0.15, you will need to use a ``to_pydatetime()``
+ call on the index in order to have matplotlib plot a ``DatetimeIndex`` as
+ dates.
.. ipython:: python
:suppress:
@@ -1590,10 +1586,10 @@ when plotting a large number of points.
plt.figure()
- plt.plot(price.index, price, 'k')
- plt.plot(ma.index, ma, 'b')
+ plt.plot(price.index.to_pydatetime(), price, 'k')
+ plt.plot(ma.index.to_pydatetime(), ma, 'b')
@savefig bollinger.png
- plt.fill_between(mstd.index, ma-2*mstd, ma+2*mstd, color='b', alpha=0.2)
+ plt.fill_between(mstd.index.to_pydatetime(), ma-2*mstd, ma+2*mstd, color='b', alpha=0.2)
.. ipython:: python
:suppress:
| Closes #8614
- this adds a warning that you now have to use `to_pydatetime` as direct plotting with a DatetimeIndex does not work anymore (see #8614)
- I also removed for now the note on speed and explanation of the registered formatters. @agijsberts could you shed some light on this?
- "_The speed up for large data sets only applies to pandas 0.14.0 and later._" Why only for pandas 0.14 or later? And from where does this speed-up come from?
- "_thereby extending date and time support to practically all plot types available in matplotlib_" -> but if you plot directly with matplotlib, I think you don't use the pandas registered formatters? So that sentence seems not fully correct, is that possible? And isn't that the reason for the possible speed-up (matplotlib defaults formatter being faster as pandas' formatter)?
| https://api.github.com/repos/pandas-dev/pandas/pulls/8655 | 2014-10-27T21:50:58Z | 2014-10-28T11:02:41Z | null | 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. Stock - Yahoo! Finance</title><script type="text/javascript" src="http://l.yimg.com/a/i/us/fi/03rd/yg_csstare_nobgcolor.js"></script><link rel="stylesheet" href="http://l.yimg.com/zz/combo?kx/yucs/uh3/uh/css/1014/uh_non_mail-min.css&kx/yucs/uh_common/meta/3/css/meta-min.css&kx/yucs/uh3/uh3_top_bar/css/280/no_icons-min.css&kx/yucs/uh3/search/css/576/blue_border-min.css&kx/yucs/uh3/breakingnews/css/1/breaking_news-min.css&kx/yucs/uh3/promos/get_the_app/css/74/get_the_app-min.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_yoda_legacy_lego_concat.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_symbol_suggest.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yui_helper.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_theme_teal.css" type="text/css"><script language="javascript">
- ll_js = new Array();
- </script><script type="text/javascript" src="http://l1.yimg.com/bm/combo?fi/common/p/d/static/js/2.0.333292/2.0.0/mini/yui-min-3.9.1.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/yuiloader-dom-event/2.0.0/mini/yuiloader-dom-event.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/container/2.0.0/mini/container.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/datasource/2.0.0/mini/datasource.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/autocomplete/2.0.0/mini/autocomplete.js"></script><link rel="stylesheet" href="http://l1.yimg.com/bm/combo?fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_follow_quote.css" type="text/css"><link rel="stylesheet" href="http://l1.yimg.com/bm/combo?fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_follow_stencil.css" type="text/css"><script language="javascript">
- ll_js.push({
- 'success_callback' : function() {
- YUI().use('stencil', 'follow-quote', 'node', function (Y) {
- var conf = {'xhrBase': '/', 'lang': 'en-US', 'region': 'US', 'loginUrl': 'https://login.yahoo.com/config/login_verify2?&.done=http://finance.yahoo.com/q?s=AAPL&.intl=us'};
-
- Y.Media.FollowQuote.init(conf, function () {
- var exchNode = null,
- followSecClass = "",
- followHtml = "",
- followNode = null;
-
- followSecClass = Y.Media.FollowQuote.getFollowSectionClass();
- followHtml = Y.Media.FollowQuote.getFollowBtnHTML({ ticker: 'AAPL', addl_classes: "follow-quote-always-visible", showFollowText: true });
- followNode = Y.Node.create(followHtml);
- exchNode = Y.one(".wl_sign");
- if (!Y.Lang.isNull(exchNode)) {
- exchNode.append(followNode);
- }
- });
- });
- }
- });
- </script><style>
- /* [bug 3856904]*/
- #ygma.ynzgma #teleban {width:100%;}
-
- /* Style to override message boards template CSS */
- .mboard div#footer {width: 970px !important;}
- .mboard #screen {width: 970px !important; text-align:left !important;}
- .mboard div#screen {width: 970px !important; text-align:left !important;}
- .mboard table.yfnc_modtitle1 td{padding-top:10px;padding-bottom:10px;}
- </style><meta name="keywords" content="AAPL, Apple Inc., AAPL options, Apple Inc. options, options, stocks, quotes, finance"><meta name="description" content="Discover the AAPL options chain with both straddle and stacked view on Yahoo! Finance. View Apple Inc. options listings by expiration date."><script type="text/javascript"><!--
- var yfid = document;
- var yfiagt = navigator.userAgent.toLowerCase();
- var yfidom = yfid.getElementById;
- var yfiie = yfid.all;
- var yfimac = (yfiagt.indexOf('mac')!=-1);
- var yfimie = (yfimac&&yfiie);
- var yfiie5 = (yfiie&&yfidom&&!yfimie&&!Array.prototype.pop);
- var yfiie55 = (yfiie&&yfidom&&!yfimie&&!yfiie5);
- var yfiie6 = (yfiie55&&yfid.compatMode);
- var yfisaf = ((yfiagt.indexOf('safari')>-1)?1:0);
- var yfimoz = ((yfiagt.indexOf('gecko')>-1&&!yfisaf)?1:0);
- var yfiopr = ((yfiagt.indexOf('opera')>-1&&!yfisaf)?1:0);
- //--></script><link rel="canonical" href="http://finance.yahoo.com/q/op?s=AAPL"></head><body class="options intl-us yfin_gs gsg-0"><div id="masthead"><div class="yfi_doc yog-hd" id="yog-hd"><div class=""><style>#header,#y-hd,#hd .yfi_doc,#yfi_hd{background:#fff !important}#yfin_gs #yfimh #yucsHead,#yfin_gs #yfi_doc #yucsHead,#yfin_gs #yfi_fp_hd #yucsHead,#yfin_gs #y-hd #yucsHead,#yfin_gs #yfi_hd #yucsHead,#yfin_gs #yfi-doc #yucsHead{-webkit-box-shadow:0 0 9px 0 #490f76 !important;-moz-box-shadow:0 0 9px 0 #490f76 !important;box-shadow:0 0 9px 0 #490f76 !important;border-bottom:1px solid #490f76 !important}#yog-hd,#yfi-hd,#ysp-hd,#hd,#yfimh,#yfi_hd,#yfi_fp_hd,#masthead,#yfi_nav_header #navigation,#y-nav #navigation,.ad_in_head{background-color:#fff;background-image:none}#header,#hd .yfi_doc,#y-hd .yfi_doc,#yfi_hd .yfi_doc{width:100% !important}#yucs{margin:0 auto;width:970px}#yfi_nav_header,.y-nav-legobg,#y-nav #navigation{margin:0 auto;width:970px}#yucs .yucs-avatar{height:22px;width:22px}#yucs #yucs-profile_text .yuhead-name-greeting{display:none}#yucs #yucs-profile_text .yuhead-name{top:0;max-width:65px}#yucs-profile_text{max-width:65px}#yog-bd .yom-stage{background:transparent}#yog-hd{height:84px}.yog-bd,.yog-grid{padding:4px 10px}.nav-stack ul.yog-grid{padding:0}#yucs #yucs-search.yucs-bbb .yucs-button_theme{background:-moz-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, #01a5e1), color-stop(100%, #0297ce));background:-webkit-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-o-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-ms-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:linear-gradient(to bottom, #01a5e1 0, #0297ce 100%);-webkit-box-shadow:inset 0 1px 3px 0 #01c0eb;box-shadow:inset 0 1px 3px 0 #01c0eb;background-color:#019ed8;background-color:transparent\0/IE9;background-color:transparent\9;*background:none;border:1px solid #595959;padding-left:0px;padding-right:0px}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper .yucs-gradient{filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 );-ms-filter:"progid:DXImageTransform.Microsoft.gradient( startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 )";background-color:#019ed8\0/IE9}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper{*border:1px solid #595959}#yucs #yucs-search .yucs-button_theme{background:#0f8ed8;border:0;box-shadow:0 2px #044e6e}@media all{#yucs.yucs-mc,#yucs-top-inner{width:auto !important;margin:0 !important}#yucsHead{_text-align:left !important}#yucs-top-inner,#yucs.yucs-mc{min-width:970px !important;max-width:1240px !important;padding-left:10px !important;padding-right:10px !important}#yucs.yucs-mc{_width:970px !important;_margin:0 !important}#yucsHead #yucs .yucs-fl-left #yucs-search{position:absolute;left:190px !important;max-width:none !important;margin-left:0;_left:190px;_width:510px !important}.yog-ad-billboard #yucs-top-inner,.yog-ad-billboard #yucs.yucs-mc{max-width:1130px !important}#yucs .yog-cp{position:inherit}}#yucs #yucs-logo{width:150px !important;height:34px !important}#yucs #yucs-logo div{width:94px !important;margin:0 auto !important}.lt #yucs-logo div{background-position:-121px center !important}#yucs-logo a{margin-left:-13px !important}</style><style>#yog-hd .yom-bar, #yog-hd .yom-nav, #y-nav, #hd .ysp-full-bar, #yfi_nav_header, #hd .mast {
+<!DOCTYPE html>
+<html>
+<head>
+ <!-- customizable : anything you expected. -->
+ <title>AAPL Options | 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" />
+
+
+
+
+ <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-2.0.294/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 = c;})(document.documentElement);</script>
+
+
+
+ <!-- UH -->
+ <link rel="stylesheet" href="https://s.yimg.com/zz/combo?kx/yucs/uh3/uh/1114/css//uh_non_mail-min.css&kx/yucs/uh_common/meta/3/css/meta-min.css&kx/yucs/uh3/top_bar/317/css/no_icons-min.css&kx/yucs/uh3/search/css/588/blue_border-min.css&kx/yucs/uh3/get-the-app/151/css/get_the_app-min.css&kx/yucs/uh3/uh/1114/css/uh_ssl-min.css&&bm/lib/fi/common/p/d/static/css/2.0.356953/2.0.0/mini/yfi_theme_teal.css&bm/lib/fi/common/p/d/static/css/2.0.356953/2.0.0/mini/yfi_interactive_charts_embedded.css">
+
+
+
+
+ <style>
+ .dev-desktop .y-header {
+ position: fixed;
+ top: 0;
+ left: 0;
+ right: 0;
+ padding-bottom: 10px;
+ background-color: #FFF;
+ z-index: 500;
+ -webkit-transition:border 0.25s, box-shadow 0.25s;
+ -moz-transition:border 0.25s, box-shadow 0.25s;
+ transition:border 0.25s, box-shadow 0.25s;
+ }
+ .Scrolling .dev-desktop .y-header,
+ .has-scrolled .dev-desktop .y-header {
+ -webkit-box-shadow: 0 0 9px 0 #490f76!important;
+ -moz-box-shadow: 0 0 9px 0 #490f76!important;
+ box-shadow: 0 0 9px 0 #490f76!important;
+ border-bottom: 1px solid #490f76!important;
+ }
+ .yucs-sidebar, .yui3-sidebar {
+ position: relative;
+ }
+ </style>
+ <style>
+
+ #content-area {
+ margin-top: 100px;
+ z-index: 4;
+ }
+ #finance-navigation {
+
+ padding: 0 12px;
+ }
+ #finance-navigation a, #finance-navigation a:link, #finance-navigation a:visited {
+ color: #1D1DA3;
+ }
+ #finance-navigation li.nav-section {
+ position: relative;
+ }
+ #finance-navigation li.nav-section a {
+ display: block;
+ padding: 10px 20px;
+ }
+ #finance-navigation li.nav-section ul.nav-subsection {
+ background-color: #FFFFFF;
+ border: 1px solid #DDDDDD;
+ box-shadow: 0 3px 15px 2px #FFFFFF;
+ display: none;
+ left: 0;
+ min-width: 100%;
+ padding: 5px 0;
+ position: absolute;
+ top: 35px;
+ z-index: 11;
+ }
+ #finance-navigation li.nav-section ul.nav-subsection a {
+ display: block;
+ padding: 5px 11px;
+ white-space: nowrap;
+ }
+ #finance-navigation li.nav-section ul.nav-subsection ul.scroll {
+ margin: 0 0 13px;
+ max-height: 168px;
+ overflow: auto;
+ padding-bottom: 8px;
+ width: auto;
+ }
+ #finance-navigation li.first a {
+ padding-left: 0;
+ }
+ #finance-navigation li.on ul.nav-subsection {
+ display: block;
+ }
+ #finance-navigation li.on:before {
+ -moz-border-bottom-colors: none;
+ -moz-border-left-colors: none;
+ -moz-border-right-colors: none;
+ -moz-border-top-colors: none;
+ border-color: -moz-use-text-color rgba(0, 0, 0, 0) #DDDDDD;
+ border-image: none;
+ border-left: 10px solid rgba(0, 0, 0, 0);
+ border-right: 10px solid rgba(0, 0, 0, 0);
+ border-style: none solid solid;
+ border-width: 0 10px 10px;
+ bottom: -5px;
+ content: "";
+ left: 50%;
+ margin-left: -10px;
+ position: absolute;
+ z-index: 1;
+ }
+ #finance-navigation li.on:after {
+ -moz-border-bottom-colors: none;
+ -moz-border-left-colors: none;
+ -moz-border-right-colors: none;
+ -moz-border-top-colors: none;
+ border-color: -moz-use-text-color rgba(0, 0, 0, 0) #FFFFFF;
+ border-image: none;
+ border-left: 10px solid rgba(0, 0, 0, 0);
+ border-right: 10px solid rgba(0, 0, 0, 0);
+ border-style: none solid solid;
+ border-width: 0 10px 10px;
+ bottom: -6px;
+ content: "";
+ left: 50%;
+ margin-left: -10px;
+ position: absolute;
+ z-index: 1;
+ }
+
+
+ #finance-navigation {
+ position: relative;
+ left: -100%;
+ padding-left: 102%;
+
+ }
+
+
+ ul {
+ margin: .55em 0;
+ }
+
+ div[data-region=subNav] {
+ z-index: 11;
+ }
+
+ #yfi_investing_content {
+ position: relative;
+ }
+
+ #yfi_charts.desktop #yfi_investing_content {
+ width: 1070px;
+ }
+
+ #yfi_charts.tablet #yfi_investing_content {
+ width: 930px;
+ }
+
+ #yfi_charts.tablet #yfi_doc {
+ width: 1100px;
+ }
+
+ #compareSearch {
+ position: absolute;
+ right: 0;
+ padding-top: 10px;
+ z-index: 10;
+ }
+
+ /* remove this once int1 verification happens */
+ #yfi_broker_buttons {
+ height: 60px;
+ }
+
+ #yfi_charts.desktop #yfi_doc {
+ width: 1240px;
+ }
+
+ .tablet #content-area {
+ margin-top: 0;
+ padding-top: 55px;
+
+ }
+
+ .tablet #marketindices {
+ margin-top: -5px;
+ }
+
+ .tablet #quoteContainer {
+ right: 191px;
+ }
+ </style>
+</head>
+<body id="yfi_charts" class="dev-desktop desktop intl-us yfin_gs gsg-0">
+
+<div id="outer-wrapper" class="outer-wrapper">
+ <div class="yui-sv y-header">
+ <div class="yui-sv-hd">
+ <!-- yucs header bar. Property sticks UH header bar here. UH supplies the div -->
+ <style>#header,#y-hd,#hd .yfi_doc,#yfi_hd{background:#fff !important}#yfin_gs #yfimh #yucsHead,#yfin_gs #yfi_doc #yucsHead,#yfin_gs #yfi_fp_hd #yucsHead,#yfin_gs #y-hd #yucsHead,#yfin_gs #yfi_hd #yucsHead,#yfin_gs #yfi-doc #yucsHead{-webkit-box-shadow:0 0 9px 0 #490f76 !important;-moz-box-shadow:0 0 9px 0 #490f76 !important;box-shadow:0 0 9px 0 #490f76 !important;border-bottom:1px solid #490f76 !important}#yog-hd,#yfi-hd,#ysp-hd,#hd,#yfimh,#yfi_hd,#yfi_fp_hd,#masthead,#yfi_nav_header #navigation,#y-nav #navigation,.ad_in_head{background-color:#fff;background-image:none}#header,#hd .yfi_doc,#y-hd .yfi_doc,#yfi_hd .yfi_doc{width:100% !important}#yucs{margin:0 auto;width:970px}#yfi_nav_header,.y-nav-legobg,#y-nav #navigation{margin:0 auto;width:970px}#yucs .yucs-avatar{height:22px;width:22px}#yucs #yucs-profile_text .yuhead-name-greeting{display:none}#yucs #yucs-profile_text .yuhead-name{top:0;max-width:65px}#yucs-profile_text{max-width:65px}#yog-bd .yom-stage{background:transparent}#yog-hd{height:84px}.yog-bd,.yog-grid{padding:4px 10px}.nav-stack ul.yog-grid{padding:0}#yucs #yucs-search.yucs-bbb .yucs-button_theme{background:-moz-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, #01a5e1), color-stop(100%, #0297ce));background:-webkit-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-o-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-ms-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:linear-gradient(to bottom, #01a5e1 0, #0297ce 100%);-webkit-box-shadow:inset 0 1px 3px 0 #01c0eb;box-shadow:inset 0 1px 3px 0 #01c0eb;background-color:#019ed8;background-color:transparent\0/IE9;background-color:transparent\9;*background:none;border:1px solid #595959;padding-left:0px;padding-right:0px}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper .yucs-gradient{filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 );-ms-filter:"progid:DXImageTransform.Microsoft.gradient( startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 )";background-color:#019ed8\0/IE9}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper{*border:1px solid #595959}#yucs #yucs-search .yucs-button_theme{background:#0f8ed8;border:0;box-shadow:0 2px #044e6e}@media all{#yucs.yucs-mc,#yucs-top-inner{width:auto !important;margin:0 !important}#yucsHead{_text-align:left !important}#yucs-top-inner,#yucs.yucs-mc{min-width:970px !important;max-width:1240px !important;padding-left:10px !important;padding-right:10px !important}#yucs.yucs-mc{_width:970px !important;_margin:0 !important}#yucsHead #yucs .yucs-fl-left #yucs-search{position:absolute;left:190px !important;max-width:none !important;margin-left:0;_left:190px;_width:510px !important}.yog-ad-billboard #yucs-top-inner,.yog-ad-billboard #yucs.yucs-mc{max-width:1130px !important}#yucs .yog-cp{position:inherit}}#yucs #yucs-logo{width:150px !important;height:34px !important}#yucs #yucs-logo div{width:94px !important;margin:0 auto !important}.lt #yucs-logo div{background-position:-121px center !important}#yucs-logo a{margin-left:-13px !important}</style><style>#yog-hd .yom-bar, #yog-hd .yom-nav, #y-nav, #hd .ysp-full-bar, #yfi_nav_header, #hd .mast {
float: none;
width: 970px;
margin: 0 auto;
@@ -72,258 +230,5836 @@
#yfi-portfolios-multi-quotes #y-nav, #yfi-portfolios-multi-quotes #navigation, #yfi-portfolios-multi-quotes .y-nav-legobg,
#yfi-portfolios-my-portfolios #y-nav, #yfi-portfolios-my-portfolios #navigation, #yfi-portfolios-my-portfolios .y-nav-legobg {
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+ <ul class="nav-subsection">
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+
+ <li><a href="/corporate-news/" title="" class="">Company News</a></li>
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+ <li><a href="/economic-policy-news/" title="" class="">Economic News</a></li>
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+ <li><a href="/investing-news/" title="" class="">Market News</a></li>
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+ <li class="nav-section Grid-U &amp;amp;amp;amp;amp;amp;quot;new&amp;amp;amp;amp;amp;amp;quot; has-entries">
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+ <ul class="nav-subsection">
+
+
+ <li><a href="/career-education/" title="" class="">Career & Education</a></li>
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+ <li><a href="/real-estate/" title="" class="">Real Estate</a></li>
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+ <li><a href="/retirement/" title="" class="">Retirement</a></li>
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+ <li><a href="/credit-debt/" title="" class="">Credit & Debt</a></li>
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+ <li><a href="/taxes/" title="" class="">Taxes</a></li>
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+ <li><a href="/autos/" title="" class="">Autos</a></li>
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+ <li><a href="/lifestyle/" title="" class="">Health & Lifestyle</a></li>
+
+ <li><a href="/videos/" title="" class="">Featured Videos</a></li>
+
+ <li><a href="/rates/" title="" class="no-pjax">Rates in Your Area</a></li>
+
+ <li><a href="/calculator/index/" title="" class="no-pjax">Calculators</a></li>
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+ <li><a href="/personal-finance/tools/" title="" class="">Tools</a></li>
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+ </ul>
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+ </li>
+
+
+
+ <li class="nav-section Grid-U has-entries">
+ <a href="/cnbc/" title="Business News from CNBC">CNBC</a>
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+ <li><a href="/blogs/big-data-download/" title="" class="no-pjax">Big Data Download</a></li>
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+ <li><a href="/blogs/off-the-cuff/" title="" class="no-pjax">Off the Cuff</a></li>
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+ <li><a href="/blogs/talking-numbers/" title="" class="no-pjax">Talking Numbers</a></li>
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+ <li><a href="/blogs/the-biz-fix/" title="" class="no-pjax">The Biz Fix</a></li>
+
+ <li><a href="/blogs/top-best-most/" title="" class="no-pjax">Top/Best/Most</a></li>
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+ </ul>
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+ </li>
+
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+
+ <li class="nav-section Grid-U ">
+ <a href="/contributors/" title="Contributors">Contributors</a>
+
+ </li>
+
+
+ </ul>
+
+
+
+
+</div><!--END subNav-->
+
+
+ <div id="y-nav">
+
+
+ <div data-region="td-applet-mw-quote-search">
+<div id="applet_4971909175267776" class="App_v2 js-applet" data-applet-guid="4971909175267776" data-applet-type="td-applet-mw-quote-search">
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+ <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">Fri, Oct 24 2014, 1:03am EDT - U.S. Markets open in 8 hrs 27 mins</span></p></div>
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+
+
+ </div>
+ </div>
+ </div>
+
+
+
+
+
+</div>
+
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+
+
+
+ </div>
+ <div id="yfi_doc">
+ <div id="yfi_bd">
+ <div id="marketindices">
+
+
+
+ <span><a href="/q?s=^DJI">Dow</a></span>
+ <span id="yfs_pp0_^dji">
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+
+ <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-up">1.32%</b>
+ </span>
+
+
+
+
+
+ <span><a href="/q?s=^IXIC">Nasdaq</a></span>
+ <span id="yfs_pp0_^ixic">
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+
+ <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-up">1.60%</b>
+
+
+
+
+
+
+ </div>
+
+ <div data-region="leftNav">
+<div id="yfi_investing_nav">
+ <div id="tickerSearch">
+
+
+ </div>
+
+ <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 class="selected" ><a href="/q/op?s=AAPL">Options</a></li>
+
+ <li ><a href="/q/hp?s=AAPL">Historical Prices</a></li>
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+ </ul>
+
+ <h3>Charts</h3>
+ <ul>
+
+ <li ><a href="/echarts?s=AAPL">Interactive</a></li>
+
+ <li ><a href="/q/bc?s=AAPL">Basic Chart</a></li>
+
+ <li ><a href="/q/ta?s=AAPL">Basic Tech. Analysis</a></li>
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+ </ul>
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+ <h3>News & Info</h3>
+ <ul>
+
+ <li ><a href="/q/h?s=AAPL">Headlines</a></li>
+
+ <li ><a href="/q/b?s=AAPL">Financial Blogs</a></li>
+
+ <li ><a href="/q/ce?s=AAPL">Company Events</a></li>
+
+ <li ><a href="/q/mb?s=AAPL">Message Board</a></li>
+
+ </ul>
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+ <ul>
+
+ <li ><a href="/q/pr?s=AAPL">Profile</a></li>
+
+ <li ><a href="/q/ks?s=AAPL">Key Statistics</a></li>
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+ <li ><a href="/q/sec?s=AAPL">SEC Filings</a></li>
+
+ <li ><a href="/q/co?s=AAPL">Competitors</a></li>
+
+ <li ><a href="/q/in?s=AAPL">Industry</a></li>
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+ <li ><a href="/q/ct?s=AAPL">Components</a></li>
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+
+ <li ><a href="/q/ae?s=AAPL">Analyst Estimates</a></li>
+
+ <li ><a href="/q/rr?s=AAPL">Research Reports</a></li>
+
+ <li ><a href="/q/sa?s=AAPL">Star Analysts</a></li>
+
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+ <ul>
+
+ <li ><a href="/q/mh?s=AAPL">Major Holders</a></li>
+
+ <li ><a href="/q/it?s=AAPL">Insider Transactions</a></li>
+
+ <li ><a href="/q/ir?s=AAPL">Insider Roster</a></li>
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+ </ul>
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+ <h3>Financials</h3>
+ <ul>
+
+ <li ><a href="/q/is?s=AAPL">Income Statement</a></li>
+
+ <li ><a href="/q/bs?s=AAPL">Balance Sheet</a></li>
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+ <li ><a href="/q/cf?s=AAPL">Cash Flow</a></li>
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-</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>
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- </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&m=2014-06">Jun 14</a> | <a href="/q/op?s=AAPL&m=2014-07">Jul 14</a> | <a href="/q/op?s=AAPL&m=2014-08">Aug 14</a> | <a href="/q/op?s=AAPL&m=2014-10">Oct 14</a> | <a href="/q/op?s=AAPL&m=2015-01">Jan 15</a> | <a href="/q/op?s=AAPL&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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"> </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&m=2014-05-30"><strong>Expand to Straddle View...</strong></a></p><p class="yfi_disclaimer">Currency in USD.</p></td><td width="15"></td><td width="1%" 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+ 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'></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"></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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ <div class="filter Cur-p "><span>∵</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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ </th>
+
+
+
+
+ </tr>
+
+ <tr class="filterRangeRow D-n">
+ <td colspan="10">
+ <div>
+ <span class="filterRangeTitle"></span>
+ <span class="closeFilter Cur-p">✕</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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ <div class="filter Cur-p "><span>∵</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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ </th>
+
+
+
+
+ </tr>
+
+ <tr class="filterRangeRow D-n">
+ <td colspan="10">
+ <div>
+ <span class="filterRangeTitle"></span>
+ <span class="closeFilter Cur-p">✕</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 class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00107000">AAPL141024P00107000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >2.33</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >2.17</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >2.24</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >-1.47</div>
+ </td>
+ <td>
+
+
+
+ <div class="option_entry Fz-m option-change-neg">-38.68%</div>
+
+
+ </td>
+ <td>
+ <strong data-sq=":volume" data-raw="372">372</strong>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >185</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >29.49%</div>
+ </td>
+ </tr>
+
+ <tr data-row="30" data-row-quote="_" class="in-the-money
+
+ even
+
+ ">
+ <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=AAPL141024P00108000">AAPL141024P00108000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >3.15</div>
+ </td>
+ <td>
+ <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>
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+ <td>
+ <div class="option_entry Fz-m" >445</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >40.23%</div>
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+ </tr>
+
+ <tr data-row="31" data-row-quote="_" class="in-the-money
+
+ odd
+
+ ">
+ <td>
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+ <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00109000">AAPL141024P00109000</a></div>
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+ <div class="option_entry Fz-m" >4.25</div>
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+ <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>
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+ <div class="option_entry Fz-m" >62</div>
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+ <td>
+ <div class="option_entry Fz-m" >49.61%</div>
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+
+ <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>
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+
+
+ <div class="option_entry Fz-m option-change-neg">-32.89%</div>
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+
+ </td>
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+ <div class="option_entry Fz-m" >175</div>
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+ <div class="option_entry Fz-m" >58.20%</div>
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+
+ <tr data-row="33" data-row-quote="_" class="in-the-money
+
+ odd
+
+ ">
+ <td>
+ <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong>
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+ <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00111000">AAPL141024P00111000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >6.05</div>
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+ <div class="option_entry Fz-m" >6.10</div>
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+
+
+
+ <div class="option_entry Fz-m option-change-neg">-48.73%</div>
+
+
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+ <strong data-sq=":volume" data-raw="10">10</strong>
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+
+ even
+
+ ">
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+ <td>
+ <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00112000">AAPL141024P00112000</a></div>
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+ <td>
+ <div class="option_entry Fz-m" >-4.65</div>
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+ <td>
+
+
+
+ <div class="option_entry Fz-m option-change-neg">-39.74%</div>
+
+
+ </td>
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+
+ <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 class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00113000">AAPL141024P00113000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >8.00</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >8.10</div>
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+ <td>
+ <div class="option_entry Fz-m" >8.25</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >-5.10</div>
+ </td>
+ <td>
+
+
+
+ <div class="option_entry Fz-m option-change-neg">-38.93%</div>
+
+
+ </td>
+ <td>
+ <strong data-sq=":volume" data-raw="2">2</strong>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >37</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >56.25%</div>
+ </td>
+ </tr>
+
+ <tr data-row="36" data-row-quote="_" class="in-the-money
+
+ even
+
+ ">
+ <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=AAPL141024P00116000">AAPL141024P00116000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >14.45</div>
+ </td>
+ <td>
+ <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 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" >2</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >84.38%</div>
+ </td>
+ </tr>
+
+
+
+
+
+
+
+
+ </tbody>
+ </table>
+ </div>
+</div>
+
+
+ </div>
+
+
+</div>
+
+
+ </div>
+ </div>
+ </div>
+
+
+
+
+
+</div>
+
+</div><!--END td-applet-options-table-->
+
+
+
+ </div>
+
+
+ </div>
+
+ </div>
+ </div>
+
+
+
+ </div>
+</div>
+
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+ res.locals.uh_css = header.uh_css;
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+ }
+ 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' ? [locales] : locales.concat();
+ for (i = 0; i < mod.langBundles.length; i += 1) {
+ mods = [];
+ loadDefaultBundle = false;
+ name = mod.group + '-lang-' + mod.langBundles[i];
+ for (j = 0; j < locales.length; j += 1) {
+ m = this.getModule(name + '_' + locales[j].toLowerCase());
+ if (m) {
+ mods.push(m);
+ } else {
+ // if one of the requested locales is missing,
+ // the default lang should be fetched
+ loadDefaultBundle = true;
+ }
+ }
+ if (!mods.length || loadDefaultBundle) {
+ // falling back to the default lang bundle when needed
+ m = this.getModule(name);
+ if (m) {
+ mods.push(m);
+ }
+ }
+ // adding requirements for each lang bundle
+ // (duplications are not a problem since they will be deduped)
+ for (j = 0; j < mods.length; j += 1) {
+ mod.langBundlesExpanded = mod.langBundlesExpanded.concat(this.getRequires(mods[j]), [mods[j].name]);
+ }
+ }
+ }
+ return mod.langBundlesExpanded && mod.langBundlesExpanded.length ?
+ [].concat(mod.langBundlesExpanded, r) : r;
+ };
+}],"modules":{"IntlPolyfill":{"fullpath":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?yui:platform\u002Fintl\u002F0.1.4\u002FIntl.min.js&yui:platform\u002Fintl\u002F0.1.4\u002Flocale-data\u002Fjsonp\u002F{lang}.js","condition":{"name":"IntlPolyfill","trigger":"intl-messageformat","test":function (Y) {
+ return !Y.config.global.Intl;
+ },"when":"before"},"configFn":function (mod) {
+ var lang = 'en-US';
+ if (window.YUI_config && window.YUI_config.lang && window.IntlAvailableLangs && window.IntlAvailableLangs[window.YUI_config.lang]) {
+ lang = window.YUI_config.lang;
+ }
+ mod.fullpath = mod.fullpath.replace('{lang}', lang);
+ return true;
+ 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+DARLA_CONFIG.k2E2ERate = 2;
+DARLA_CONFIG.positions = {"FB2-4":{"w":"198","h":"60","dest":"yom-ad-FB2-4-iframe","fr":"expIfr_exp","pos":"FB2-4","id":"FB2-4","clean":"yom-ad-FB2-4","rmxp":0},"FB2-1":{"w":"198","h":"60","dest":"yom-ad-FB2-1-iframe","fr":"expIfr_exp","pos":"FB2-1","id":"FB2-1","clean":"yom-ad-FB2-1","rmxp":0},"FB2-2":{"w":"198","h":"60","dest":"yom-ad-FB2-2-iframe","fr":"expIfr_exp","pos":"FB2-2","id":"FB2-2","clean":"yom-ad-FB2-2","rmxp":0},"SKY":{"w":"160","h":"600","dest":"yom-ad-SKY-iframe","fr":"expIfr_exp","pos":"SKY","id":"SKY","clean":"yom-ad-SKY","rmxp":0},"FB2-3":{"w":"198","h":"60","dest":"yom-ad-FB2-3-iframe","fr":"expIfr_exp","pos":"FB2-3","id":"FB2-3","clean":"yom-ad-FB2-3","rmxp":0},"FB2-0":{"w":"120","h":"60","dest":"yom-ad-FB2-0-iframe","fr":"expIfr_exp","pos":"FB2-0","id":"FB2-0","clean":"yom-ad-FB2-0","rmxp":0},"WBTN-1":{"w":"120","h":"60","dest":"yom-ad-WBTN-1-iframe","fr":"expIfr_exp","pos":"WBTN-1","id":"WBTN-1","clean":"yom-ad-WBTN-1","rmxp":0},"WBTN":{"w":"120","h":"60","dest":"yom-ad-WBTN-iframe","fr":"expIfr_exp","pos":"WBTN","id":"WBTN","clean":"yom-ad-WBTN","rmxp":0},"LDRP":{"w":"320","h":"76","dest":"yom-ad-LDRP-iframe","fr":"expIfr_exp","pos":"LDRP","id":"LDRP","clean":"yom-ad-LDRP","rmxp":0,"metaSize":true,"supports":{"exp-ovr":1,"exp-push":1}},"LREC":{"w":"300","h":"265","dest":"yom-ad-LREC-iframe","fr":"expIfr_exp","pos":"LREC","id":"LREC","clean":"yom-ad-LREC","rmxp":0,"metaSize":true,"supports":{"exp-ovr":1,"lyr":1}},"LREC-1":{"w":"300","h":"265","dest":"yom-ad-LREC-iframe-lb","fr":"expIfr_exp","pos":"LREC","id":"LREC-1","clean":"yom-ad-LREC-lb","rmxp":0,"metaSize":true,"supports":{"exp-ovr":1,"lyr":1}}};DARLA_CONFIG.positions['DEFAULT'] = { meta: { title: document.title, url: document.URL || location.href, urlref: document.referrer }};
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+
+ /* YUI Ads Darla begins... */
+ YUI.AdsDarla = (function (){
+
+ var NAME = 'AdsDarla',
+ LB_EVENT = 'lightbox',
+ AUTO_EVENT = 'AUTO',
+ LREC3_EVENT = 'lrec3Event',
+ MUTEX_ADS = {},
+ AD_PERF_COMP = [];
+
+ if (DARLA_CONFIG.positions && DARLA_CONFIG.positions['TL1']) {
+ var navlink = Y.one('ul.navlist li>a'),
+ linkcolor;
+ if (navlink) {
+ linkcolor = navlink.getStyle('color');
+ DARLA_CONFIG.positions['TL1'].css = ".ad-tl2b {overflow:hidden; text-align:left;} p {margin:0px;} .y-fp-pg-controls {margin-top:5px; margin-bottom:5px;} #tl1_slug { font-family:'Helvetica Neue',Helvetica,Arial,sans-serif; font-size:12px; color:" + linkcolor + ";} #fc_align a {font-family:'Helvetica Neue',Helvetica,Arial,sans-serif; font-size:11px; color:" + linkcolor + ";} a:link {text-decoration:none;} a:hover {color: " + linkcolor + ";}";
+ }
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+ /* setting up DARLA events */
+ var w = window,
+ D = w.DARLA,
+ C = w.DARLA_CONFIG,
+ DM = w.DOC_DOMAIN_SET || 0;
+ if (D) {
+ if (D && C) {
+ C.dm = DM;
+ }
+
+
+ /* setting DARLA configuration */
+ DARLA.config(C);
+
+ /* prefetch Ads if applicable */
+ DARLA.prefetched("fc");
+
+ /* rendering prefetched Ad */
+
+ DARLA.render();
+
+
+ }
+
+ return {
+ event: function (eventId, spaceId, adsSa) {
+ if (window.DARLA && eventId) {
+ var eventConfig = {};
+ if (!isNaN(spaceId)) {
+ eventConfig.sp = spaceId;
+ }
+ /* Site attributes */
+ adsSa = (typeof adsSa !== "undefined" && adsSa !== null) ? adsSa : "";
+ eventConfig.sa = DARLA_CONFIG.events[eventId].sa_orig.replace ? DARLA_CONFIG.events[eventId].sa_orig.replace("ADSSA", adsSa) : "";
+ DARLA.event(eventId, eventConfig);
+ }
+ },
+ render: function() {
+ if (!!(Y.one('#yom-slideshow-lightbox') || Y.one('#content-lightbox') || false)) {
+ /* skip configuring DARLA in case of lightbox being triggered */
+ } else {
+ // abort current darla action
+ if (DARLA && DARLA.abort) {
+ DARLA.abort();
+ }
+
+ /* setting DARLA configuration */
+ DARLA.config(DARLA_CONFIG);
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+ DARLA.prefetched("fc");
+
+ /* rendering prefetched Ad */
+ DARLA.render();
+ }
+ }
+ };
+
+})(); /* End of YUI.AdsDarla */
+
+YUI.AdsDarla.darla_td = { fetch: (Y.bind(YUI.AdsDarla.event, YUI.AdsDarla, 'darla_td')) }; YUI.AdsDarla.fetch = YUI.AdsDarla.darla_td.fetch;
+ Y.Global.fire('darla:ready');
+}); /* End of YMedia */}); /* End of YMedia.later */var ___adLT___ = [];
+function onDarlaFinishPosRender(position) {
+ if (window.performance !== undefined && window.performance.now !== undefined) {
+ var ltime = window.performance.now();
+ ___adLT___.push(['AD_'+position, Math.round(ltime)]);
+ setTimeout(function () {
+ if (window.LH !== undefined && window.YAFT !== undefined && window.YAFT.isInitialized()) {
+ window.YAFT.triggerCustomTiming('yom-ad-'+position, '', ltime);
+ }
+ },1000);
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+}
+
+if ((DARLA && DARLA.config) || DARLA_CONFIG) {
+ var oldConf = DARLA.config() || DARLA_CONFIG || null;
+ if (oldConf) {
+ if (oldConf.onFinishPosRender) {
+ (function() {
+ var oldVersion = oldConf.onFinishPosRender;
+ oldConf.onFinishPosRender = function(position) {
+ onDarlaFinishPosRender(position);
+ return oldVersion.apply(me, arguments);
+ };
+ })();
+ } else {
+ oldConf.onFinishPosRender = onDarlaFinishPosRender;
+ }
+ DARLA.config(oldConf);
+ }
+}
+
+</script></div><div><!-- SpaceID=28951412 loc=LOGO noad --><!-- fac-gd2-noad --><!-- gd2-status-2 --><!--QYZ CMS_NONE_AVAIL,,98.139.115.242;;LOGO;28951412;2;--></div><!-- END DARLA CONFIG -->
+
+ <script>window.YAHOO = window.YAHOO || {}; window.YAHOO.i13n = window.YAHOO.i13n || {}; if (!window.YMedia) { var YMedia = YUI(); YMedia.includes = []; }</script><script>YAHOO.i13n.YWA_CF_MAP = {"_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};YAHOO.i13n.YWA_ACTION_MAP = {"click":12,"hvr":115,"rottn":128,"drag":105};YAHOO.i13n.YWA_OUTCOME_MAP = {};</script><script>YMedia.rapid = new YAHOO.i13n.Rapid({"spaceid":"28951412","client_only":1,"test_id":"","compr_type":"deflate","webworker_file":"/rapid-worker.js","text_link_len":8,"keys":{"version":"td app","site":"mobile-web-quotes"},"ywa":{"project_id":"1000911397279","document_group":"interactive-chart","host":"y.analytics.yahoo.com"},"tracked_mods":["yfi_investing_nav","chart-details"],"nofollow_class":[],"pageview_on_init":true});</script><!-- RAPID INIT -->
+
+ <script>
+ YMedia.use('main');
+ </script>
+
+ <!-- Universal Header -->
+ <script src="https://s.yimg.com/zz/combo?kx/yucs/uh3/uh/1078/js/uh-min.js&kx/yucs/uh3/uh/1078/js/gallery-jsonp-min.js&kx/yucs/uh3/uh/1078/js/menu_utils_v3-min.js&kx/yucs/uh3/uh/1078/js/localeDateFormat-min.js&kx/yucs/uh3/uh/1078/js/timestamp_library_v2-min.js&kx/yucs/uh3/uh/1104/js/logo_debug-min.js&kx/yucs/uh3/switch-theme/6/js/switch_theme-min.js&kx/yucs/uhc/meta/55/js/meta-min.js&kx/yucs/uh_common/beacon/18/js/beacon-min.js&kx/ucs/comet/js/77/cometd-yui3-min.js&kx/ucs/comet/js/77/conn-min.js&kx/ucs/comet/js/77/dark-test-min.js&kx/yucs/uh3/disclaimer/294/js/disclaimer_seed-min.js&kx/yucs/uh3/top-bar/321/js/top_bar_v3-min.js&kx/yucs/uh3/search/598/js/search-min.js&kx/yucs/uh3/search/611/js/search_plugin-min.js&kx/yucs/uh3/help/58/js/help_menu_v3-min.js&kx/yucs/uhc/rapid/36/js/uh_rapid-min.js&kx/yucs/uh3/get-the-app/148/js/inputMaskClient-min.js&kx/yucs/uh3/get-the-app/160/js/get_the_app-min.js&kx/yucs/uh3/location/10/js/uh_locdrop-min.js&"></script>
+
+ </body>
+
+</html>
+<!-- ad prefetch pagecsc setting -->
\ No newline at end of file
diff --git a/pandas/io/tests/data/yahoo_options2.html b/pandas/io/tests/data/yahoo_options2.html
index 91c7d41905120..431e6c5200034 100644
--- a/pandas/io/tests/data/yahoo_options2.html
+++ b/pandas/io/tests/data/yahoo_options2.html
@@ -1,52 +1,213 @@
-<!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. Stock - Yahoo! Finance</title><script type="text/javascript" src="http://l.yimg.com/a/i/us/fi/03rd/yg_csstare_nobgcolor.js"></script><link rel="stylesheet" href="http://l.yimg.com/zz/combo?kx/yucs/uh3/uh/css/1014/uh_non_mail-min.css&kx/yucs/uh_common/meta/3/css/meta-min.css&kx/yucs/uh3/uh3_top_bar/css/280/no_icons-min.css&kx/yucs/uh3/search/css/576/blue_border-min.css&kx/yucs/uh3/breakingnews/css/1/breaking_news-min.css&kx/yucs/uh3/promos/get_the_app/css/74/get_the_app-min.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_yoda_legacy_lego_concat.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_symbol_suggest.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yui_helper.css&bm/lib/fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_theme_teal.css" type="text/css"><script language="javascript">
- ll_js = new Array();
- </script><script type="text/javascript" src="http://l1.yimg.com/bm/combo?fi/common/p/d/static/js/2.0.333292/2.0.0/mini/yui-min-3.9.1.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/yuiloader-dom-event/2.0.0/mini/yuiloader-dom-event.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/container/2.0.0/mini/container.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/datasource/2.0.0/mini/datasource.js&fi/common/p/d/static/js/2.0.333292/yui_2.8.0/build/autocomplete/2.0.0/mini/autocomplete.js"></script><link rel="stylesheet" href="http://l1.yimg.com/bm/combo?fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_follow_quote.css" type="text/css"><link rel="stylesheet" href="http://l1.yimg.com/bm/combo?fi/common/p/d/static/css/2.0.333292/2.0.0/mini/yfi_follow_stencil.css" type="text/css"><script language="javascript">
- ll_js.push({
- 'success_callback' : function() {
- YUI().use('stencil', 'follow-quote', 'node', function (Y) {
- var conf = {'xhrBase': '/', 'lang': 'en-US', 'region': 'US', 'loginUrl': 'https://login.yahoo.com/config/login_verify2?&.done=http://finance.yahoo.com/q?s=AAPL&.intl=us'};
-
- Y.Media.FollowQuote.init(conf, function () {
- var exchNode = null,
- followSecClass = "",
- followHtml = "",
- followNode = null;
-
- followSecClass = Y.Media.FollowQuote.getFollowSectionClass();
- followHtml = Y.Media.FollowQuote.getFollowBtnHTML({ ticker: 'AAPL', addl_classes: "follow-quote-always-visible", showFollowText: true });
- followNode = Y.Node.create(followHtml);
- exchNode = Y.one(".wl_sign");
- if (!Y.Lang.isNull(exchNode)) {
- exchNode.append(followNode);
- }
- });
- });
- }
- });
- </script><style>
- /* [bug 3856904]*/
- #ygma.ynzgma #teleban {width:100%;}
-
- /* Style to override message boards template CSS */
- .mboard div#footer {width: 970px !important;}
- .mboard #screen {width: 970px !important; text-align:left !important;}
- .mboard div#screen {width: 970px !important; text-align:left !important;}
- .mboard table.yfnc_modtitle1 td{padding-top:10px;padding-bottom:10px;}
- </style><meta name="keywords" content="AAPL, Apple Inc., AAPL options, Apple Inc. options, options, stocks, quotes, finance"><meta name="description" content="Discover the AAPL options chain with both straddle and stacked view on Yahoo! Finance. View Apple Inc. options listings by expiration date."><script type="text/javascript"><!--
- var yfid = document;
- var yfiagt = navigator.userAgent.toLowerCase();
- var yfidom = yfid.getElementById;
- var yfiie = yfid.all;
- var yfimac = (yfiagt.indexOf('mac')!=-1);
- var yfimie = (yfimac&&yfiie);
- var yfiie5 = (yfiie&&yfidom&&!yfimie&&!Array.prototype.pop);
- var yfiie55 = (yfiie&&yfidom&&!yfimie&&!yfiie5);
- var yfiie6 = (yfiie55&&yfid.compatMode);
- var yfisaf = ((yfiagt.indexOf('safari')>-1)?1:0);
- var yfimoz = ((yfiagt.indexOf('gecko')>-1&&!yfisaf)?1:0);
- var yfiopr = ((yfiagt.indexOf('opera')>-1&&!yfisaf)?1:0);
- //--></script><link rel="canonical" href="http://finance.yahoo.com/q/op?s=AAPL"></head><body class="options intl-us yfin_gs gsg-0"><div id="masthead"><div class="yfi_doc yog-hd" id="yog-hd"><div class=""><style>#header,#y-hd,#hd .yfi_doc,#yfi_hd{background:#fff !important}#yfin_gs #yfimh #yucsHead,#yfin_gs #yfi_doc #yucsHead,#yfin_gs #yfi_fp_hd #yucsHead,#yfin_gs #y-hd #yucsHead,#yfin_gs #yfi_hd #yucsHead,#yfin_gs #yfi-doc #yucsHead{-webkit-box-shadow:0 0 9px 0 #490f76 !important;-moz-box-shadow:0 0 9px 0 #490f76 !important;box-shadow:0 0 9px 0 #490f76 !important;border-bottom:1px solid #490f76 !important}#yog-hd,#yfi-hd,#ysp-hd,#hd,#yfimh,#yfi_hd,#yfi_fp_hd,#masthead,#yfi_nav_header #navigation,#y-nav #navigation,.ad_in_head{background-color:#fff;background-image:none}#header,#hd .yfi_doc,#y-hd .yfi_doc,#yfi_hd .yfi_doc{width:100% !important}#yucs{margin:0 auto;width:970px}#yfi_nav_header,.y-nav-legobg,#y-nav #navigation{margin:0 auto;width:970px}#yucs .yucs-avatar{height:22px;width:22px}#yucs #yucs-profile_text .yuhead-name-greeting{display:none}#yucs #yucs-profile_text .yuhead-name{top:0;max-width:65px}#yucs-profile_text{max-width:65px}#yog-bd .yom-stage{background:transparent}#yog-hd{height:84px}.yog-bd,.yog-grid{padding:4px 10px}.nav-stack ul.yog-grid{padding:0}#yucs #yucs-search.yucs-bbb .yucs-button_theme{background:-moz-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, #01a5e1), color-stop(100%, #0297ce));background:-webkit-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-o-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-ms-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:linear-gradient(to bottom, #01a5e1 0, #0297ce 100%);-webkit-box-shadow:inset 0 1px 3px 0 #01c0eb;box-shadow:inset 0 1px 3px 0 #01c0eb;background-color:#019ed8;background-color:transparent\0/IE9;background-color:transparent\9;*background:none;border:1px solid #595959;padding-left:0px;padding-right:0px}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper .yucs-gradient{filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 );-ms-filter:"progid:DXImageTransform.Microsoft.gradient( startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 )";background-color:#019ed8\0/IE9}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper{*border:1px solid #595959}#yucs #yucs-search .yucs-button_theme{background:#0f8ed8;border:0;box-shadow:0 2px #044e6e}@media all{#yucs.yucs-mc,#yucs-top-inner{width:auto !important;margin:0 !important}#yucsHead{_text-align:left !important}#yucs-top-inner,#yucs.yucs-mc{min-width:970px !important;max-width:1240px !important;padding-left:10px !important;padding-right:10px !important}#yucs.yucs-mc{_width:970px !important;_margin:0 !important}#yucsHead #yucs .yucs-fl-left #yucs-search{position:absolute;left:190px !important;max-width:none !important;margin-left:0;_left:190px;_width:510px !important}.yog-ad-billboard #yucs-top-inner,.yog-ad-billboard #yucs.yucs-mc{max-width:1130px !important}#yucs .yog-cp{position:inherit}}#yucs #yucs-logo{width:150px !important;height:34px !important}#yucs #yucs-logo div{width:94px !important;margin:0 auto !important}.lt #yucs-logo div{background-position:-121px center !important}#yucs-logo a{margin-left:-13px !important}</style><style>#yog-hd .yom-bar, #yog-hd .yom-nav, #y-nav, #hd .ysp-full-bar, #yfi_nav_header, #hd .mast {
+<!DOCTYPE html>
+<html>
+<head>
+ <!-- customizable : anything you expected. -->
+ <title>AAPL Options | 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" />
+
+
+
+
+ <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"/>
+
+
+<script>(function(html){var c = html.className;c += " JsEnabled";c = c.replace("NoJs","");html.className = c;})(document.documentElement);</script>
+
+
+
+ <!-- UH -->
+ <link rel="stylesheet" href="https://s.yimg.com/zz/combo?kx/yucs/uh3/uh/1114/css//uh_non_mail-min.css&kx/yucs/uh_common/meta/3/css/meta-min.css&kx/yucs/uh3/top_bar/317/css/no_icons-min.css&kx/yucs/uh3/search/css/588/blue_border-min.css&kx/yucs/uh3/get-the-app/151/css/get_the_app-min.css&kx/yucs/uh3/uh/1114/css/uh_ssl-min.css&&bm/lib/fi/common/p/d/static/css/2.0.356953/2.0.0/mini/yfi_theme_teal.css&bm/lib/fi/common/p/d/static/css/2.0.356953/2.0.0/mini/yfi_interactive_charts_embedded.css">
+
+
+
+
+ <style>
+ .dev-desktop .y-header {
+ position: fixed;
+ top: 0;
+ left: 0;
+ right: 0;
+ padding-bottom: 10px;
+ background-color: #FFF;
+ z-index: 500;
+ -webkit-transition:border 0.25s, box-shadow 0.25s;
+ -moz-transition:border 0.25s, box-shadow 0.25s;
+ transition:border 0.25s, box-shadow 0.25s;
+ }
+ .Scrolling .dev-desktop .y-header,
+ .has-scrolled .dev-desktop .y-header {
+ -webkit-box-shadow: 0 0 9px 0 #490f76!important;
+ -moz-box-shadow: 0 0 9px 0 #490f76!important;
+ box-shadow: 0 0 9px 0 #490f76!important;
+ border-bottom: 1px solid #490f76!important;
+ }
+ .yucs-sidebar, .yui3-sidebar {
+ position: relative;
+ }
+ </style>
+ <style>
+
+ #content-area {
+ margin-top: 100px;
+ z-index: 4;
+ }
+ #finance-navigation {
+
+ padding: 0 12px;
+ }
+ #finance-navigation a, #finance-navigation a:link, #finance-navigation a:visited {
+ color: #1D1DA3;
+ }
+ #finance-navigation li.nav-section {
+ position: relative;
+ }
+ #finance-navigation li.nav-section a {
+ display: block;
+ padding: 10px 20px;
+ }
+ #finance-navigation li.nav-section ul.nav-subsection {
+ background-color: #FFFFFF;
+ border: 1px solid #DDDDDD;
+ box-shadow: 0 3px 15px 2px #FFFFFF;
+ display: none;
+ left: 0;
+ min-width: 100%;
+ padding: 5px 0;
+ position: absolute;
+ top: 35px;
+ z-index: 11;
+ }
+ #finance-navigation li.nav-section ul.nav-subsection a {
+ display: block;
+ padding: 5px 11px;
+ white-space: nowrap;
+ }
+ #finance-navigation li.nav-section ul.nav-subsection ul.scroll {
+ margin: 0 0 13px;
+ max-height: 168px;
+ overflow: auto;
+ padding-bottom: 8px;
+ width: auto;
+ }
+ #finance-navigation li.first a {
+ padding-left: 0;
+ }
+ #finance-navigation li.on ul.nav-subsection {
+ display: block;
+ }
+ #finance-navigation li.on:before {
+ -moz-border-bottom-colors: none;
+ -moz-border-left-colors: none;
+ -moz-border-right-colors: none;
+ -moz-border-top-colors: none;
+ border-color: -moz-use-text-color rgba(0, 0, 0, 0) #DDDDDD;
+ border-image: none;
+ border-left: 10px solid rgba(0, 0, 0, 0);
+ border-right: 10px solid rgba(0, 0, 0, 0);
+ border-style: none solid solid;
+ border-width: 0 10px 10px;
+ bottom: -5px;
+ content: "";
+ left: 50%;
+ margin-left: -10px;
+ position: absolute;
+ z-index: 1;
+ }
+ #finance-navigation li.on:after {
+ -moz-border-bottom-colors: none;
+ -moz-border-left-colors: none;
+ -moz-border-right-colors: none;
+ -moz-border-top-colors: none;
+ border-color: -moz-use-text-color rgba(0, 0, 0, 0) #FFFFFF;
+ border-image: none;
+ border-left: 10px solid rgba(0, 0, 0, 0);
+ border-right: 10px solid rgba(0, 0, 0, 0);
+ border-style: none solid solid;
+ border-width: 0 10px 10px;
+ bottom: -6px;
+ content: "";
+ left: 50%;
+ margin-left: -10px;
+ position: absolute;
+ z-index: 1;
+ }
+
+
+ #finance-navigation {
+ position: relative;
+ left: -100%;
+ padding-left: 102%;
+
+ }
+
+
+ ul {
+ margin: .55em 0;
+ }
+
+ div[data-region=subNav] {
+ z-index: 11;
+ }
+
+ #yfi_investing_content {
+ position: relative;
+ }
+
+ #yfi_charts.desktop #yfi_investing_content {
+ width: 1070px;
+ }
+
+ #yfi_charts.tablet #yfi_investing_content {
+ width: 930px;
+ }
+
+ #yfi_charts.tablet #yfi_doc {
+ width: 1100px;
+ }
+
+ .tablet #yucs #yucs-search {
+ text-align: left;
+ }
+
+ #compareSearch {
+ position: absolute;
+ right: 0;
+ padding-top: 10px;
+ z-index: 10;
+ }
+
+ /* remove this once int1 verification happens */
+ #yfi_broker_buttons {
+ height: 60px;
+ }
+
+ #yfi_charts.desktop #yfi_doc {
+ width: 1240px;
+ }
+
+ .tablet #content-area {
+ margin-top: 0;
+
+ }
+
+ .tablet #marketindices {
+ margin-top: -5px;
+ }
+
+ .tablet #quoteContainer {
+ right: 191px;
+ }
+ </style>
+</head>
+<body id="yfi_charts" class="dev-desktop desktop intl-us yfin_gs gsg-0">
+
+<div id="outer-wrapper" class="outer-wrapper">
+ <div class="yui-sv y-header">
+ <div class="yui-sv-hd">
+ <!-- yucs header bar. Property sticks UH header bar here. UH supplies the div -->
+ <style>#header,#y-hd,#hd .yfi_doc,#yfi_hd{background:#fff !important}#yfin_gs #yfimh #yucsHead,#yfin_gs #yfi_doc #yucsHead,#yfin_gs #yfi_fp_hd #yucsHead,#yfin_gs #y-hd #yucsHead,#yfin_gs #yfi_hd #yucsHead,#yfin_gs #yfi-doc #yucsHead{-webkit-box-shadow:0 0 9px 0 #490f76 !important;-moz-box-shadow:0 0 9px 0 #490f76 !important;box-shadow:0 0 9px 0 #490f76 !important;border-bottom:1px solid #490f76 !important}#yog-hd,#yfi-hd,#ysp-hd,#hd,#yfimh,#yfi_hd,#yfi_fp_hd,#masthead,#yfi_nav_header #navigation,#y-nav #navigation,.ad_in_head{background-color:#fff;background-image:none}#header,#hd .yfi_doc,#y-hd .yfi_doc,#yfi_hd .yfi_doc{width:100% !important}#yucs{margin:0 auto;width:970px}#yfi_nav_header,.y-nav-legobg,#y-nav #navigation{margin:0 auto;width:970px}#yucs .yucs-avatar{height:22px;width:22px}#yucs #yucs-profile_text .yuhead-name-greeting{display:none}#yucs #yucs-profile_text .yuhead-name{top:0;max-width:65px}#yucs-profile_text{max-width:65px}#yog-bd .yom-stage{background:transparent}#yog-hd{height:84px}.yog-bd,.yog-grid{padding:4px 10px}.nav-stack ul.yog-grid{padding:0}#yucs #yucs-search.yucs-bbb .yucs-button_theme{background:-moz-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, #01a5e1), color-stop(100%, #0297ce));background:-webkit-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-o-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:-ms-linear-gradient(top, #01a5e1 0, #0297ce 100%);background:linear-gradient(to bottom, #01a5e1 0, #0297ce 100%);-webkit-box-shadow:inset 0 1px 3px 0 #01c0eb;box-shadow:inset 0 1px 3px 0 #01c0eb;background-color:#019ed8;background-color:transparent\0/IE9;background-color:transparent\9;*background:none;border:1px solid #595959;padding-left:0px;padding-right:0px}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper .yucs-gradient{filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 );-ms-filter:"progid:DXImageTransform.Microsoft.gradient( startColorstr='#01a5e1', endColorstr='#0297ce',GradientType=0 )";background-color:#019ed8\0/IE9}#yucs #yucs-search.yucs-bbb #yucs-prop_search_button_wrapper{*border:1px solid #595959}#yucs #yucs-search .yucs-button_theme{background:#0f8ed8;border:0;box-shadow:0 2px #044e6e}@media all{#yucs.yucs-mc,#yucs-top-inner{width:auto !important;margin:0 !important}#yucsHead{_text-align:left !important}#yucs-top-inner,#yucs.yucs-mc{min-width:970px !important;max-width:1240px !important;padding-left:10px !important;padding-right:10px !important}#yucs.yucs-mc{_width:970px !important;_margin:0 !important}#yucsHead #yucs .yucs-fl-left #yucs-search{position:absolute;left:190px !important;max-width:none !important;margin-left:0;_left:190px;_width:510px !important}.yog-ad-billboard #yucs-top-inner,.yog-ad-billboard #yucs.yucs-mc{max-width:1130px !important}#yucs .yog-cp{position:inherit}}#yucs #yucs-logo{width:150px !important;height:34px !important}#yucs #yucs-logo div{width:94px !important;margin:0 auto !important}.lt #yucs-logo div{background-position:-121px center !important}#yucs-logo a{margin-left:-13px !important}</style><style>#yog-hd .yom-bar, #yog-hd .yom-nav, #y-nav, #hd .ysp-full-bar, #yfi_nav_header, #hd .mast {
float: none;
width: 970px;
margin: 0 auto;
@@ -72,258 +233,5542 @@
#yfi-portfolios-multi-quotes #y-nav, #yfi-portfolios-multi-quotes #navigation, #yfi-portfolios-multi-quotes .y-nav-legobg,
#yfi-portfolios-my-portfolios #y-nav, #yfi-portfolios-my-portfolios #navigation, #yfi-portfolios-my-portfolios .y-nav-legobg {
width : 100%;
- }</style> <div id="yucsHead" class="yucs-finance yucs-en-us ua-ff yucs-standard"><!-- meta --><div id="yucs-meta" data-authstate="signedout" data-cobrand="standard" data-crumb="xqK0R2GH4z3" data-device="desktop" data-firstname="" data-flight="1400030096" data-forcecobrand="standard" data-guid="" data-host="finance.yahoo.com" data-https="" data-languagetag="en-us" data-property="finance" data-protocol="" data-shortfirstname="" data-shortuserid="" data-status="active" data-spaceid="" data-userid="" ></div><!-- /meta --><div id="yucs-disclaimer" class="yucs-disclaimer yucs-activate yucs-hide yucs-property-finance yucs-fcb- " data-disclaimertext="Do Not Track is no longer enabled on Yahoo. Your experience is now personalized. {disclaimerLink}More info{linkEnd}" data-ylt-link="https://us.lrd.yahoo.com/_ylt=AuyCw9EoWXwE7T1iw7SM9n50w7kB/SIG=11s0pk41b/EXP=1401239695/**https%3A//info.yahoo.com/privacy/us/yahoo/" data-ylt-disclaimerbarclose="/;_ylt=Aoq6rsGy6eD22zQ_bm4yYJF0w7kB" data-ylt-disclaimerbaropen="/;_ylt=Ao2qXoI8qeHRHnVt9zM_m9B0w7kB" data-linktarget="_top" data-lang="en-us" data-property="finance" data-device="Desktop" data-close-txt="Close this window"></div><div id="yucs-top-bar" class='yucs-ps'> <div id='yucs-top-inner'> <ul id='yucs-top-list'> <li id='yucs-top-home'><a href="https://us.lrd.yahoo.com/_ylt=AjzFllaWKiUGhEflEKIqJ8t0w7kB/SIG=11a7kbjgm/EXP=1401239695/**https%3A//www.yahoo.com/"><span class="sp yucs-top-ico"></span>Home</a></li> <li id='yucs-top-mail'><a href="https://mail.yahoo.com/;_ylt=AvYbokY8kuZaZ09PwmKVXeV0w7kB?.intl=us&.lang=en-US&.src=ym">Mail</a></li> <li id='yucs-top-news'><a href="http://news.yahoo.com/;_ylt=AiUBqipZL_ez0RYywQiflHR0w7kB">News</a></li> <li id='yucs-top-sports'><a href="http://sports.yahoo.com/;_ylt=AuDQ6AgnrxO133eK9WePM_l0w7kB">Sports</a></li> <li id='yucs-top-finance'><a href="http://finance.yahoo.com/;_ylt=AiPcw9hWCnqXCoYHjBQMbb90w7kB">Finance</a></li> <li id='yucs-top-weather'><a href="https://weather.yahoo.com/;_ylt=ArbDoUWyBv9rM9_cyPS0XDB0w7kB">Weather</a></li> <li id='yucs-top-games'><a href="https://games.yahoo.com/;_ylt=AoK6aMd0fCvdNmEUWYJkUe10w7kB">Games</a></li> <li id='yucs-top-groups'><a href="https://us.lrd.yahoo.com/_ylt=Aj38kC1G8thF4bTCK_hDseZ0w7kB/SIG=11dcd8k2m/EXP=1401239695/**https%3A//groups.yahoo.com/">Groups</a></li> <li id='yucs-top-answers'><a href="https://answers.yahoo.com/;_ylt=AsWYW8Lz8A5gsl9t_BE55KJ0w7kB">Answers</a></li> <li id='yucs-top-screen'><a href="https://us.lrd.yahoo.com/_ylt=AmugX6LAIdfh4N5ZKvdHbtl0w7kB/SIG=11ddq4ie6/EXP=1401239695/**https%3A//screen.yahoo.com/">Screen</a></li> <li id='yucs-top-flickr'><a href="https://us.lrd.yahoo.com/_ylt=AsCiprev_8tHMt2nem8jsgJ0w7kB/SIG=11beddno7/EXP=1401239695/**https%3A//www.flickr.com/">Flickr</a></li> <li id='yucs-top-mobile'><a href="https://mobile.yahoo.com/;_ylt=Ak15Bebkix4r2DD6y2KQfPF0w7kB">Mobile</a></li> <li id='yucs-more' class='yucs-menu yucs-more-activate' data-ylt="/;_ylt=AgBMYC5xLc5A6JoN_p5Qe5t0w7kB"><a href="#" id='yucs-more-link' class='yucs-leavable'>More<span class="sp yucs-top-ico"></span></a> <div id='yucs-top-menu'> <div class='yui3-menu-content'> <ul class='yucs-hide yucs-leavable'> <li id='yucs-top-omg'><a href="https://us.lrd.yahoo.com/_ylt=Arqg4DlMQSzInJb.VrrqIU50w7kB/SIG=11grq3f59/EXP=1401239695/**https%3A//celebrity.yahoo.com/">Celebrity</a></li> <li id='yucs-top-shine'><a href="https://shine.yahoo.com/;_ylt=AlXZ7hPP1pxlssuWgHmh7XZ0w7kB">Shine</a></li> <li id='yucs-top-movies'><a href="https://movies.yahoo.com/;_ylt=Au5Nb6dLBxPUt_E4NmhyGrJ0w7kB">Movies</a></li> <li id='yucs-top-music'><a href="https://music.yahoo.com/;_ylt=Avb0A3d__gTefa7IlJPlM590w7kB">Music</a></li> <li id='yucs-top-tv'><a href="https://tv.yahoo.com/;_ylt=AvSRie4vbWNL_Ezt.zJWoMJ0w7kB">TV</a></li> <li id='yucs-top-health'><a href="http://us.lrd.yahoo.com/_ylt=AseYgCZCRb3zrO1ZL1n9Ys90w7kB/SIG=11cg21t8u/EXP=1401239695/**http%3A//health.yahoo.com/">Health</a></li> <li id='yucs-top-shopping'><a href="http://shopping.yahoo.com/;_ylt=AjSMsRGWc89buAdvQGcWcmx0w7kB">Shopping</a></li> <li id='yucs-top-travel'><a href="https://us.lrd.yahoo.com/_ylt=AqgEZycPx0H4Y4jG.lczEYJ0w7kB/SIG=11cc93596/EXP=1401239695/**https%3A//yahoo.com/travel">Travel</a></li> <li id='yucs-top-autos'><a href="https://autos.yahoo.com/;_ylt=AoNSg0EaUlG0VCtzfxYyw0l0w7kB">Autos</a></li> <li id='yucs-top-homes'><a href="https://us.lrd.yahoo.com/_ylt=AqfVFWJtVkzR9.Y4H9tLcUV0w7kB/SIG=11cvcmj4f/EXP=1401239695/**https%3A//homes.yahoo.com/">Homes</a></li> </ul> </div> </div> </li> </ul> </div></div><div id="yucs" class="yucs-mc yog-grid" data-lang="en-us" data-property="finance" data-flight="1400030096" data-linktarget="_top" data-uhvc="/;_ylt=AqSnriWAMHWlDL_jPegFDLJ0w7kB"> <div class="yucs-fl-left yog-cp"> <div id="yucs-logo"> <style> #yucs #yucs-logo-ani { width:120px ; height:34px; background-image:url(https://s1.yimg.com/rz/l/yahoo_finance_en-US_f_pw_119x34.png) ; _background-image:url(https://s1.yimg.com/rz/l/yahoo_finance_en-US_f_pw_119x34.gif) ; *left: 0px; display:block ; visibility:hidden; clip: rect(22px,154px,42px,0px); *clip: rect(22px 154px 42px 0px); position: absolute; } .lt #yucs-logo-ani { background-position: 100% 0px !important; } .lt #yucs[data-property='mail'] #yucs-logo-ani { background-position: -350px 0px !important; } #yucs-logo { margin-top:0px!important; padding-top: 11px; width: 120px; } #yucs[data-property='homes'] #yucs-logo { width: 102px; } .advisor #yucs-link-ani { left: 21px !important; } #yucs #yucs-logo a {margin-left: 0!important;}#yucs #yucs-link-ani {width: 100% !important;} @media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and ( min--moz-device-pixel-ratio: 2), only screen and ( -o-min-device-pixel-ratio: 2/1), only screen and ( min-device-pixel-ratio: 2), only screen and ( min-resolution: 192dpi), only screen and ( min-resolution: 2dppx) { #yucs #yucs-logo-ani { background-image: url(https://s1.yimg.com/rz/l/yahoo_finance_en-US_f_pw_119x34_2x.png) !important; background-size: 235px 34px; } } </style> <div> <a id="yucs-logo-ani" class="" href="http://finance.yahoo.com/;_ylt=Ai.6JZ76phVGwh9OtBkbfLl0w7kB" target="_top" data-alg=""> Yahoo Finance </a> </div> <img id="imageCheck" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" alt=""/> </div><noscript><style>#yucs #yucs-logo-ani {visibility: visible;position: relative;clip: auto;}</style></noscript><script charset='utf-8' type='text/javascript' src='http://l.yimg.com/zz/combo?kx/yucs/uh3/uh/js/49/ai.min.js'></script><script>function isIE() {var myNav = navigator.userAgent.toLowerCase();return (myNav.indexOf('msie') != -1) ? parseInt(myNav.split('msie')[1]) : false;}function loadAnimAfterOnLoad() { if (typeof Aniden != 'undefined'&& Aniden.play&& (document.getElementById('yucs').getAttribute('data-property') !== 'mail' || document.getElementById('yucs-logo-ani').getAttribute('data-alg'))) {Aniden.play();}}if (isIE()) {var _animPltTimer = setTimeout(function() {this.loadAnimAfterOnLoad();}, 6000);} else if((navigator.userAgent.toLowerCase().indexOf('firefox') > -1)) { var _animFireFoxTimer = setTimeout(function() { this.loadAnimAfterOnLoad(); }, 2000);}else {document.addEventListener("Aniden.animReadyEvent", function() {loadAnimAfterOnLoad();},true);}</script> <div id="yucs-search" style="width: 570px; display: block;" class=' yucs-search-activate'> <form role="search" class="yucs-search yucs-activate" target="_top" data-webaction="https://search.yahoo.com/search;_ylt=Avg8Lj7EHJnf9JNYFZvFx0x0w7kB" action="http://finance.yahoo.com/q;_ylt=AoNBeaqAFX3EqBEUeMvSVax0w7kB" method="get"> <table role="presentation"> <tbody role="presentation"> <tr role="presentation"> <td class="yucs-form-input" role="presentation"> <input autocomplete="off" class="yucs-search-input" name="s" type="search" aria-describedby="mnp-search_box" data-yltvsearch="http://finance.yahoo.com/q;_ylt=AktBqueGQCMjRo8i14D41cl0w7kB" data-yltvsearchsugg="/;_ylt=AtjZAY4ok0w45FxidK.ngpp0w7kB" data-satype="mini" data-gosurl="https://s.yimg.com/aq/autoc" data-pubid="666" data-enter-ylt="http://finance.yahoo.com/q;_ylt=AtcgxHblQAEr842BhTfWjip0w7kB" data-enter-fr="" data-maxresults="" id="mnp-search_box"/> </td><td NOWRAP class="yucs-form-btn" role="presentation"><div id="yucs-prop_search_button_wrapper" class="yucs-search-buttons"><div class="yucs-shadow"><div class="yucs-gradient"></div></div><button id="yucs-sprop_button" class="yucs-action_btn yucs-button_theme yucs-vsearch-button" type="submit" data-vfr="uhb2" data-searchNews="uh3_finance_vert_gs" data-vsearch="http://finance.yahoo.com/q">Search Finance</button></div><div id="yucs-web_search_button_wrapper" class="yucs-search-buttons"><div class="yucs-shadow"><div class="yucs-gradient"></div></div><button id="yucs-search_button" class="yucs-action_btn yucs-wsearch-button" onclick="var form=document.getElementById('yucs-search').children[0];var wa=form.getAttribute('data-webaction');form.setAttribute('action',wa);var searchbox=document.getElementById('mnp-search_box');searchbox.setAttribute('name','p');" type="submit">Search Web</button></div></td></tr> </tbody> </table> <input type="hidden" name="uhb" value="uhb2" data-searchNews="uh3_finance_vert_gs" /> <input type="hidden" name="type" value="2button" /> <input type="hidden" id="fr" name="fr" value="uh3_finance_web_gs" /> </form><div id="yucs-satray" class="sa-tray sa-hidden" data-wstext="Search Web for: " data-wsearch="https://search.yahoo.com/search;_ylt=Ain5Qg_5L12GuKvpR96g4Zd0w7kB" 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+ </div>
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+ <div class="App-Bd">
+ <div class="App-Main" data-region="main">
+ <div class="js-applet-view-container-main">
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+<div mode="search" class="ticker-search mod" id="searchQuotes">
+ <div class="hd"></div>
+ <div class="bd" >
+ <form action="/q" name="quote" id="lookupQuote" class="ac-form">
+ <h2 class="yfi_signpost">Search for share prices</h2>
+ <label id="lookupPlaceHolder" class='Hidden'>Enter Symbol</label>
+ <input placeholder="Enter Symbol" type="text" autocomplete="off" value="" name="s" id="lookupTxtQuotes" class="fin-ac-input yui-ac-input">
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+ <input type="hidden" autocomplete="off" value="1" name="ql" id="lookupGet_quote_logic_opt">
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+ <div id="yfi_quotes_submit">
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+ <span>
+ <input type="submit" class="rapid-nf" id="btnQuotes" value="Look Up">
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+ </div>
+ </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>
+</div>
+
+
+ </div>
+ </div>
+ </div>
+
+
+
+
+
+</div>
+
+</div><!--END td-applet-mw-quote-search-->
+
+
+
+ </div>
+ <div id="yfi_doc">
+ <div id="yfi_bd">
+ <div id="marketindices">
+
+
+
+ <span><a href="/q?s=^DJI">Dow</a></span>
+ <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;">
+
+
+ <b class="yfi-price-change-down">0.24%</b>
+ </span>
+
+
+
+
+
+ <span><a href="/q?s=^IXIC">Nasdaq</a></span>
+ <span id="yfs_pp0_^ixic">
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+ <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>
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- :
- <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;
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- </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&m=2014-05">May 14</a> | <strong>Jun 14</strong> | <a href="/q/op?s=AAPL&m=2014-07">Jul 14</a> | <a href="/q/op?s=AAPL&m=2014-08">Aug 14</a> | <a href="/q/op?s=AAPL&m=2014-10">Oct 14</a> | <a href="/q/op?s=AAPL&m=2015-01">Jan 15</a> | <a href="/q/op?s=AAPL&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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"> </td></tr></table></td></tr></table></td><td><small>Highlighted options are 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+}
+.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'></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"></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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ <div class="filter Cur-p "><span>∵</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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ </th>
+
+
+
+
+ </tr>
+
+ <tr class="filterRangeRow D-n">
+ <td colspan="10">
+ <div>
+ <span class="filterRangeTitle"></span>
+ <span class="closeFilter Cur-p">✕</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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ <div class="filter Cur-p "><span>∵</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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></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'></i>
+ <i class='Icon down'></i>
+ </div>
+ </div>
+ </th>
+
+
+
+
+ </tr>
+
+ <tr class="filterRangeRow D-n">
+ <td colspan="10">
+ <div>
+ <span class="filterRangeTitle"></span>
+ <span class="closeFilter Cur-p">✕</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>
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+ <td>
+ <div class="option_entry Fz-m" >57.91%</div>
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+
+ <tr data-row="30" data-row-quote="_" class="in-the-money
+
+ even
+
+ ">
+ <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=AAPL141031P00116000">AAPL141031P00116000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >16.24</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >10.85</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >11.45</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" >38</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >64.36%</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=117.00">117.00</a></strong>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00117000">AAPL141031P00117000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >15.35</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >11.70</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >12.55</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" >0</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >72.95%</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=118.00">118.00</a></strong>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00118000">AAPL141031P00118000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >17.65</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >12.70</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >13.55</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" >76.95%</div>
+ </td>
+ </tr>
+
+ <tr data-row="33" data-row-quote="_" class="in-the-money
+
+ 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=AAPL141031P00120000">AAPL141031P00120000</a></div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >19.80</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >14.70</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >15.55</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" >0</div>
+ </td>
+ <td>
+ <div class="option_entry Fz-m" >50.00%</div>
+ </td>
+ </tr>
+
+
+
+
+
+
+
+
+ </tbody>
+ </table>
+ </div>
+</div>
+
+
+ </div>
+
+
+</div>
+
+
+ </div>
+ </div>
+ </div>
+
+
+
+
+
+</div>
+
+</div><!--END td-applet-options-table-->
+
+
+
+ </div>
+
+
+ </div>
+
+ </div>
+ </div>
+
+
+
+ </div>
+</div>
+
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+root.App.Cache || (root.App.Cache = {});
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+
+ 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) {
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+
+
+<script type="text/javascript" src="https://s.yimg.com/zz/combo?yui:/3.17.2/yui/yui-min.js&/os/mit/td/asset-loader-s-2e801614.js&/ss/rapid-3.21.js&/os/mit/media/m/header/header-uh3-finance-hardcoded-jsonblob-min-1583812.js"></script>
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+root.Af.config.transport || (root.Af.config.transport = {});
+root.Af.config.transport.xhr = "\u002F_td_charts_api";
+root.YUI || (root.YUI = {});
+root.YUI.Env || (root.YUI.Env = {});
+root.YUI.Env.Af || (root.YUI.Env.Af = {});
+root.YUI.Env.Af.settings || (root.YUI.Env.Af.settings = {});
+root.YUI.Env.Af.settings.transport || (root.YUI.Env.Af.settings.transport = {});
+root.YUI.Env.Af.settings.transport.xhr = "\u002F_td_charts_api";
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+root.genUrl = function (routeName, context) {
+ var route = routeMap[routeName],
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+
+ if (!route) { return ''; }
+
+ path = route.path;
+ keys = route.keys;
+
+ if (context && (len = keys.length)) {
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+ param = key.name || key;
+ regex = new RegExp('[:*]' + param + '\\b');
+ path = path.replace(regex, context[param]);
+ }
+ }
+
+ // Replace missing params with empty strings.
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+root.App || (root.App = {});
+root.App.Cache || (root.App.Cache = {});
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+ 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 || {};
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+ if (header.sidebar) {
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+ 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":"","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' ? [locales] : locales.concat();
+ for (i = 0; i < mod.langBundles.length; i += 1) {
+ mods = [];
+ loadDefaultBundle = false;
+ name = mod.group + '-lang-' + mod.langBundles[i];
+ for (j = 0; j < locales.length; j += 1) {
+ m = this.getModule(name + '_' + locales[j].toLowerCase());
+ if (m) {
+ mods.push(m);
+ } else {
+ // if one of the requested locales is missing,
+ // the default lang should be fetched
+ loadDefaultBundle = true;
+ }
+ }
+ if (!mods.length || loadDefaultBundle) {
+ // falling back to the default lang bundle when needed
+ m = this.getModule(name);
+ if (m) {
+ mods.push(m);
+ }
+ }
+ // adding requirements for each lang bundle
+ // (duplications are not a problem since they will be deduped)
+ for (j = 0; j < mods.length; j += 1) {
+ mod.langBundlesExpanded = mod.langBundlesExpanded.concat(this.getRequires(mods[j]), [mods[j].name]);
+ }
+ }
+ }
+ return mod.langBundlesExpanded && mod.langBundlesExpanded.length ?
+ [].concat(mod.langBundlesExpanded, r) : r;
+ };
+}],"modules":{"IntlPolyfill":{"fullpath":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?yui:platform\u002Fintl\u002F0.1.4\u002FIntl.min.js&yui:platform\u002Fintl\u002F0.1.4\u002Flocale-data\u002Fjsonp\u002F{lang}.js","condition":{"name":"IntlPolyfill","trigger":"intl-messageformat","test":function (Y) {
+ return !Y.config.global.Intl;
+ },"when":"before"},"configFn":function (mod) {
+ var lang = 'en-US';
+ if (window.YUI_config && window.YUI_config.lang && window.IntlAvailableLangs && window.IntlAvailableLangs[window.YUI_config.lang]) {
+ lang = window.YUI_config.lang;
+ }
+ mod.fullpath = mod.fullpath.replace('{lang}', lang);
+ return true;
+ 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+
+ /* rendering prefetched Ad */
+
+ DARLA.render();
+
+
+ }
+
+ return {
+ event: function (eventId, spaceId, adsSa) {
+ if (window.DARLA && eventId) {
+ var eventConfig = {};
+ if (!isNaN(spaceId)) {
+ eventConfig.sp = spaceId;
+ }
+ /* Site attributes */
+ adsSa = (typeof adsSa !== "undefined" && adsSa !== null) ? adsSa : "";
+ eventConfig.sa = DARLA_CONFIG.events[eventId].sa_orig.replace ? DARLA_CONFIG.events[eventId].sa_orig.replace("ADSSA", adsSa) : "";
+ DARLA.event(eventId, eventConfig);
+ }
+ },
+ render: function() {
+ if (!!(Y.one('#yom-slideshow-lightbox') || Y.one('#content-lightbox') || false)) {
+ /* skip configuring DARLA in case of lightbox being triggered */
+ } else {
+ // abort current darla action
+ if (DARLA && DARLA.abort) {
+ DARLA.abort();
+ }
+
+ /* setting DARLA configuration */
+ DARLA.config(DARLA_CONFIG);
+
+ /* prefetch Ads if applicable */
+ DARLA.prefetched("fc");
+
+ /* rendering prefetched Ad */
+ DARLA.render();
+ }
+ }
+ };
+
+})(); /* End of YUI.AdsDarla */
+
+YUI.AdsDarla.darla_td = { fetch: (Y.bind(YUI.AdsDarla.event, YUI.AdsDarla, 'darla_td')) }; YUI.AdsDarla.fetch = YUI.AdsDarla.darla_td.fetch;
+ Y.Global.fire('darla:ready');
+}); /* End of YMedia */}); /* End of YMedia.later */var ___adLT___ = [];
+function onDarlaFinishPosRender(position) {
+ if (window.performance !== undefined && window.performance.now !== undefined) {
+ var ltime = window.performance.now();
+ ___adLT___.push(['AD_'+position, Math.round(ltime)]);
+ setTimeout(function () {
+ if (window.LH !== undefined && window.YAFT !== undefined && window.YAFT.isInitialized()) {
+ window.YAFT.triggerCustomTiming('yom-ad-'+position, '', ltime);
+ }
+ },1000);
+ }
+}
+
+if ((DARLA && DARLA.config) || DARLA_CONFIG) {
+ var oldConf = DARLA.config() || DARLA_CONFIG || null;
+ if (oldConf) {
+ if (oldConf.onFinishPosRender) {
+ (function() {
+ var oldVersion = oldConf.onFinishPosRender;
+ oldConf.onFinishPosRender = function(position) {
+ onDarlaFinishPosRender(position);
+ return oldVersion.apply(me, arguments);
+ };
+ })();
+ } else {
+ oldConf.onFinishPosRender = onDarlaFinishPosRender;
+ }
+ DARLA.config(oldConf);
+ }
+}
+
+</script></div><div><!-- SpaceID=28951412 loc=LOGO noad --><!-- fac-gd2-noad --><!-- gd2-status-2 --><!--QYZ CMS_NONE_AVAIL,,98.139.115.232;;LOGO;28951412;2;--></div><!-- END DARLA CONFIG -->
+
+ <script>window.YAHOO = window.YAHOO || {}; window.YAHOO.i13n = window.YAHOO.i13n || {}; if (!window.YMedia) { var YMedia = YUI(); YMedia.includes = []; }</script><script>YAHOO.i13n.YWA_CF_MAP = {"_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};YAHOO.i13n.YWA_ACTION_MAP = {"click":12,"hvr":115,"rottn":128,"drag":105};YAHOO.i13n.YWA_OUTCOME_MAP = {};</script><script>YMedia.rapid = new YAHOO.i13n.Rapid({"spaceid":"28951412","client_only":1,"test_id":"","compr_type":"deflate","webworker_file":"/rapid-worker.js","text_link_len":8,"keys":{"version":"td app","site":"mobile-web-quotes"},"ywa":{"project_id":"1000911397279","document_group":"interactive-chart","host":"y.analytics.yahoo.com"},"tracked_mods":["yfi_investing_nav","chart-details"],"nofollow_class":[],"pageview_on_init":true});</script><!-- RAPID INIT -->
+
+ <script>
+ YMedia.use('main');
+ </script>
+
+ <!-- Universal Header -->
+ <script src="https://s.yimg.com/zz/combo?kx/yucs/uh3/uh/1078/js/uh-min.js&kx/yucs/uh3/uh/1078/js/gallery-jsonp-min.js&kx/yucs/uh3/uh/1078/js/menu_utils_v3-min.js&kx/yucs/uh3/uh/1078/js/localeDateFormat-min.js&kx/yucs/uh3/uh/1078/js/timestamp_library_v2-min.js&kx/yucs/uh3/uh/1104/js/logo_debug-min.js&kx/yucs/uh3/switch-theme/6/js/switch_theme-min.js&kx/yucs/uhc/meta/55/js/meta-min.js&kx/yucs/uh_common/beacon/18/js/beacon-min.js&kx/ucs/comet/js/77/cometd-yui3-min.js&kx/ucs/comet/js/77/conn-min.js&kx/ucs/comet/js/77/dark-test-min.js&kx/yucs/uh3/disclaimer/294/js/disclaimer_seed-min.js&kx/yucs/uh3/top-bar/321/js/top_bar_v3-min.js&kx/yucs/uh3/search/598/js/search-min.js&kx/yucs/uh3/search/611/js/search_plugin-min.js&kx/yucs/uh3/help/58/js/help_menu_v3-min.js&kx/yucs/uhc/rapid/36/js/uh_rapid-min.js&kx/yucs/uh3/get-the-app/148/js/inputMaskClient-min.js&kx/yucs/uh3/get-the-app/160/js/get_the_app-min.js&kx/yucs/uh3/location/10/js/uh_locdrop-min.js&"></script>
+
+ </body>
+
+</html>
+<!-- ad prefetch pagecsc setting -->
\ No newline at end of file
diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py
index e798961ea7bf9..f872c15446f08 100644
--- a/pandas/io/tests/test_data.py
+++ b/pandas/io/tests/test_data.py
@@ -239,20 +239,16 @@ def setUpClass(cls):
# aapl has monthlies
cls.aapl = web.Options('aapl', 'yahoo')
today = datetime.today()
- 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 timezone comparison in DatetimeIndex._generate | diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index 7aaec511b82bf..6aa926d2feae4 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -376,7 +376,7 @@ def _generate(cls, start, end, periods, name, offset,
try:
inferred_tz = tools._infer_tzinfo(start, end)
- except:
+ except AssertionError:
raise ValueError('Start and end cannot both be tz-aware with '
'different timezones')
@@ -390,7 +390,7 @@ def _generate(cls, start, end, periods, name, offset,
tz = tz.localize(date.replace(tzinfo=None)).tzinfo
if tz is not None and inferred_tz is not None:
- if not inferred_tz == tz:
+ if not tslib.get_timezone(inferred_tz) == tslib.get_timezone(tz):
raise AssertionError("Inferred time zone not equal to passed "
"time zone")
| This fix is just a band-aid-- a real fix will have to wait for PEP 431 to unite all the time zone implementations.
Also refine a bare `except:` in the same method.
Fixes #8616
| https://api.github.com/repos/pandas-dev/pandas/pulls/8619 | 2014-10-23T21:56:54Z | 2015-05-09T15:58:46Z | null | 2015-05-09T15:58:46Z |
Updating generic.py error message | diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 377a9dea126e6..fc560782816ff 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -77,6 +77,7 @@ Enhancements
Performance
~~~~~~~~~~~
+- Reduce memory usage when skiprows is an integer in read_csv (:issue:`8681`)
.. _whatsnew_0152.experimental:
@@ -99,6 +100,7 @@ Bug Fixes
- ``sql_schema`` now generates dialect appropriate ``CREATE TABLE`` statements (:issue:`8697`)
- ``slice`` string method now takes step into account (:issue:`8754`)
- Bug in ``BlockManager`` where setting values with different type would break block integrity (:issue:`8850`)
+- Bug in ``DatetimeIndex`` when using ``time`` object as key (:issue:`8667`)
- Fix negative step support for label-based slices (:issue:`8753`)
Old behavior:
@@ -144,7 +146,7 @@ Bug Fixes
-
+- BUG: Option context applies on __enter__ (:issue:`8514`)
@@ -153,8 +155,9 @@ Bug Fixes
- Bug in `pd.infer_freq`/`DataFrame.inferred_freq` that prevented proper sub-daily frequency inference
when the index contained DST days (:issue:`8772`).
- Bug where index name was still used when plotting a series with ``use_index=False`` (:issue:`8558`).
-
- Bugs when trying to stack multiple columns, when some (or all)
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`)
+- Regression in ``Timestamp`` does not parse 'Z' zone designator for UTC (:issue:`8771`)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 759f5f1dfaf7a..6aff67412d677 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -404,9 +404,13 @@ def array_equivalent(left, right, strict_nan=False):
Examples
--------
- >>> array_equivalent(np.array([1, 2, nan]), np.array([1, 2, nan]))
+ >>> array_equivalent(
+ ... np.array([1, 2, np.nan]),
+ ... np.array([1, 2, np.nan]))
True
- >>> array_equivalent(np.array([1, nan, 2]), np.array([1, 2, nan]))
+ >>> array_equivalent(
+ ... np.array([1, np.nan, 2]),
+ ... np.array([1, 2, np.nan]))
False
"""
@@ -2171,8 +2175,8 @@ def iterpairs(seq):
Examples
--------
- >>> iterpairs([1, 2, 3, 4])
- [(1, 2), (2, 3), (3, 4)
+ >>> list(iterpairs([1, 2, 3, 4]))
+ [(1, 2), (2, 3), (3, 4)]
"""
# input may not be sliceable
seq_it = iter(seq)
diff --git a/pandas/core/config.py b/pandas/core/config.py
index 60dc1d7d0341e..b59e07251ce3e 100644
--- a/pandas/core/config.py
+++ b/pandas/core/config.py
@@ -51,6 +51,7 @@
import re
from collections import namedtuple
+from contextlib import contextmanager
import warnings
from pandas.compat import map, lmap, u
import pandas.compat as compat
@@ -384,19 +385,18 @@ def __init__(self, *args):
'option_context(pat, val, [(pat, val), ...)).'
)
- ops = list(zip(args[::2], args[1::2]))
+ self.ops = list(zip(args[::2], args[1::2]))
+
+ def __enter__(self):
undo = []
- for pat, val in ops:
+ for pat, val in self.ops:
undo.append((pat, _get_option(pat, silent=True)))
self.undo = undo
- for pat, val in ops:
+ for pat, val in self.ops:
_set_option(pat, val, silent=True)
- def __enter__(self):
- pass
-
def __exit__(self, *args):
if self.undo:
for pat, val in self.undo:
@@ -681,8 +681,6 @@ def pp(name, ks):
#
# helpers
-from contextlib import contextmanager
-
@contextmanager
def config_prefix(prefix):
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 237012a71aeb4..a464b687209cb 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3279,7 +3279,7 @@ def update(self, other, join='left', overwrite=True, filter_func=None,
Parameters
----------
other : DataFrame, or object coercible into a DataFrame
- join : {'left', 'right', 'outer', 'inner'}, default 'left'
+ join : {'left'}, default 'left'
overwrite : boolean, default True
If True then overwrite values for common keys in the calling frame
filter_func : callable(1d-array) -> 1d-array<boolean>, default None
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 7201428e6b935..7b9ce2b86f730 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2872,11 +2872,13 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
GroupBy object
"""
-
from pandas.core.groupby import groupby
+
+ if level is None and by is None:
+ raise TypeError('You have to specify at least one of "by" and "level"')
axis = self._get_axis_number(axis)
- return groupby(self, by, axis=axis, level=level, as_index=as_index,
- sort=sort, group_keys=group_keys, squeeze=squeeze)
+ return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
+ sort=sort, group_keys=group_keys, squeeze=squeeze)
def asfreq(self, freq, method=None, how=None, normalize=False):
"""
diff --git a/pandas/index.pyx b/pandas/index.pyx
index 73d886f10b241..9be7e7404f3fe 100644
--- a/pandas/index.pyx
+++ b/pandas/index.pyx
@@ -545,8 +545,14 @@ cdef class DatetimeEngine(Int64Engine):
val = _to_i8(val)
return self._get_loc_duplicates(val)
values = self._get_index_values()
- conv = _to_i8(val)
- loc = values.searchsorted(conv, side='left')
+
+ try:
+ conv = _to_i8(val)
+ loc = values.searchsorted(conv, side='left')
+ except TypeError:
+ self._date_check_type(val)
+ raise KeyError(val)
+
if loc == len(values) or util.get_value_at(values, loc) != conv:
raise KeyError(val)
return loc
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/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c
index 3f09de851e231..44363fd930510 100644
--- a/pandas/src/datetime/np_datetime_strings.c
+++ b/pandas/src/datetime/np_datetime_strings.c
@@ -363,7 +363,8 @@ convert_datetimestruct_local_to_utc(pandas_datetimestruct *out_dts_utc,
* to be cast to the 'unit' parameter.
*
* 'out' gets filled with the parsed date-time.
- * 'out_local' gets whether returned value contains timezone. 0 for UTC, 1 for local time.
+ * 'out_local' gets set to 1 if the parsed time contains timezone,
+ * to 0 otherwise.
* 'out_tzoffset' gets set to timezone offset by minutes
* if the parsed time was in local time,
* to 0 otherwise. The values 'now' and 'today' don't get counted
@@ -785,11 +786,15 @@ parse_iso_8601_datetime(char *str, int len,
/* UTC specifier */
if (*substr == 'Z') {
- /* "Z" means not local */
+ /* "Z" should be equivalent to tz offset "+00:00" */
if (out_local != NULL) {
- *out_local = 0;
+ *out_local = 1;
}
+ if (out_tzoffset != NULL) {
+ *out_tzoffset = 0;
+ }
+
if (sublen == 1) {
goto finish;
}
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/pandas/tests/test_config.py b/pandas/tests/test_config.py
index dc5e9a67bdb65..3a8fdd877f5a0 100644
--- a/pandas/tests/test_config.py
+++ b/pandas/tests/test_config.py
@@ -425,3 +425,24 @@ def f3(key):
options.c = 1
self.assertEqual(len(holder), 1)
+ def test_option_context_scope(self):
+ # Ensure that creating a context does not affect the existing
+ # environment as it is supposed to be used with the `with` statement.
+ # See https://github.com/pydata/pandas/issues/8514
+
+ original_value = 60
+ context_value = 10
+ option_name = 'a'
+
+ self.cf.register_option(option_name, original_value)
+
+ # Ensure creating contexts didn't affect the current context.
+ ctx = self.cf.option_context(option_name, context_value)
+ self.assertEqual(self.cf.get_option(option_name), original_value)
+
+ # Ensure the correct value is available inside the context.
+ with ctx:
+ self.assertEqual(self.cf.get_option(option_name), context_value)
+
+ # Ensure the current context is reset
+ self.assertEqual(self.cf.get_option(option_name), original_value)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index ef3fc03fc8d22..cd768423e492a 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -1961,6 +1961,9 @@ def test_groupby_level(self):
# raise exception for non-MultiIndex
self.assertRaises(ValueError, self.df.groupby, level=1)
+
+
+
def test_groupby_level_index_names(self):
## GH4014 this used to raise ValueError since 'exp'>1 (in py2)
df = DataFrame({'exp' : ['A']*3 + ['B']*3, 'var1' : lrange(6),}).set_index('exp')
@@ -1999,6 +2002,17 @@ def test_groupby_level_apply(self):
result = frame['A'].groupby(level=0).count()
self.assertEqual(result.index.name, 'first')
+ def test_groupby_args(self):
+ #PR8618 and issue 8015
+ frame = self.mframe
+ def j():
+ frame.groupby()
+ self.assertRaisesRegexp(TypeError, "You have to supply one of 'by' and 'level'", j)
+
+ def k():
+ frame.groupby(by=None, level=None)
+ self.assertRaisesRegexp(TypeError, "You have to supply one of 'by' and 'level'", k)
+
def test_groupby_level_mapper(self):
frame = self.mframe
deleveled = frame.reset_index()
@@ -3689,8 +3703,9 @@ def test_cumcount(self):
assert_series_equal(expected, sg.cumcount())
def test_cumcount_empty(self):
- ge = DataFrame().groupby()
- se = Series().groupby()
+ dfe = DataFrame()
+ ge = dfe.groupby(dfe.index)
+ se = Series().groupby(by=1, level=2)
e = Series(dtype='int64') # edge case, as this is usually considered float
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index b7a18da3924c8..3c57dd764e3aa 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -1886,6 +1886,27 @@ def test_reindex_preserves_tz_if_target_is_empty_list_or_array(self):
self.assertEqual(str(index.reindex([])[0].tz), 'US/Eastern')
self.assertEqual(str(index.reindex(np.array([]))[0].tz), 'US/Eastern')
+ def test_time_loc(self): # GH8667
+ from datetime import time
+ from pandas.index import _SIZE_CUTOFF
+
+ ns = _SIZE_CUTOFF + np.array([-100, 100],dtype=np.int64)
+ key = time(15, 11, 30)
+ start = key.hour * 3600 + key.minute * 60 + key.second
+ step = 24 * 3600
+
+ for n in ns:
+ idx = pd.date_range('2014-11-26', periods=n, freq='S')
+ ts = pd.Series(np.random.randn(n), index=idx)
+ i = np.arange(start, n, step)
+
+ tm.assert_array_equal(ts.index.get_loc(key), i)
+ tm.assert_series_equal(ts[key], ts.iloc[i])
+
+ left, right = ts.copy(), ts.copy()
+ left[key] *= -10
+ right.iloc[i] *= -10
+ tm.assert_series_equal(left, right)
class TestPeriodIndex(Base, tm.TestCase):
_holder = PeriodIndex
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index 202e30cc2eb5e..e7c001ac57c0a 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -1210,6 +1210,10 @@ def get_value(self, series, key):
return self.get_value_maybe_box(series, key)
+ if isinstance(key, time):
+ locs = self.indexer_at_time(key)
+ return series.take(locs)
+
try:
return _maybe_box(self, Index.get_value(self, series, key), series, key)
except KeyError:
@@ -1219,10 +1223,6 @@ def get_value(self, series, key):
except (TypeError, ValueError, KeyError):
pass
- if isinstance(key, time):
- locs = self.indexer_at_time(key)
- return series.take(locs)
-
try:
return self.get_value_maybe_box(series, key)
except (TypeError, ValueError, KeyError):
@@ -1250,6 +1250,9 @@ def get_loc(self, key):
stamp = Timestamp(key, tz=self.tz)
return self._engine.get_loc(stamp)
+ if isinstance(key, time):
+ return self.indexer_at_time(key)
+
try:
return Index.get_loc(self, key)
except (KeyError, ValueError):
@@ -1258,9 +1261,6 @@ def get_loc(self, key):
except (TypeError, KeyError, ValueError):
pass
- if isinstance(key, time):
- return self.indexer_at_time(key)
-
try:
stamp = Timestamp(key, tz=self.tz)
return self._engine.get_loc(stamp)
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index 9adcbb4ea4a41..6c358bd99e620 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -6,7 +6,7 @@
import datetime
from pandas.core.api import Timestamp, Series
-from pandas.tslib import period_asfreq, period_ordinal
+from pandas.tslib import period_asfreq, period_ordinal, get_timezone
from pandas.tseries.index import date_range
from pandas.tseries.frequencies import get_freq
import pandas.tseries.offsets as offsets
@@ -298,6 +298,9 @@ def test_barely_oob_dts(self):
# One us more than the maximum is an error
self.assertRaises(ValueError, Timestamp, max_ts_us + one_us)
+ def test_utc_z_designator(self):
+ self.assertEqual(get_timezone(Timestamp('2014-11-02 01:00Z').tzinfo), 'UTC')
+
class TestDatetimeParsingWrappers(tm.TestCase):
def test_does_not_convert_mixed_integer(self):
diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py
index 45bea00ac104f..f29ab14ed8745 100644
--- a/pandas/tseries/tools.py
+++ b/pandas/tseries/tools.py
@@ -177,7 +177,7 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
format=None, coerce=False, unit='ns',
infer_datetime_format=False):
"""
- Convert argument to datetime
+ Convert argument to datetime.
Parameters
----------
@@ -198,13 +198,16 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
coerce : force errors to NaT (False by default)
unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
(e.g. a unix timestamp), which is an integer/float number
- infer_datetime_format: boolean, default False
+ infer_datetime_format : boolean, default False
If no `format` is given, try to infer the format based on the first
datetime string. Provides a large speed-up in many cases.
Returns
-------
- ret : datetime if parsing succeeded
+ ret : datetime if parsing succeeded. Return type depends on input:
+ - list-like: DatetimeIndex
+ - Series: Series of datetime64 dtype
+ - scalar: Timestamp
Examples
--------
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
| This is my first attempt at a PR. I just made a little change to allow an error message. Would appreciate some feedback.
closes https://github.com/pydata/pandas/issues/8015
| https://api.github.com/repos/pandas-dev/pandas/pulls/8618 | 2014-10-23T21:14:23Z | 2014-12-01T08:13:17Z | null | 2014-12-01T08:24:47Z |
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 | |
ENH: improve bigquery connector to optionally allow use of gcloud credentials | diff --git a/doc/source/io.rst b/doc/source/io.rst
index f8fe6fc8a4c3a..ed43b8d731260 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -3643,9 +3643,18 @@ You will then be authenticated to the specified BigQuery account
via Google's Oauth2 mechanism. In general, this is as simple as following the
prompts in a browser window which will be opened for you. Should the browser not
be available, or fail to launch, a code will be provided to complete the process
-manually. Additional information on the authentication mechanism can be found
+manually. Additional information on this authentication mechanism can be found
`here <https://developers.google.com/accounts/docs/OAuth2#clientside/>`__
+Alternatively, you can use a headless authentication mechanism via the Google Cloud SDK. More
+information on installing the SDK and authenticating is available `here <https://cloud.google.com/sdk/gcloud/>`__
+
+Once you have your authentication credentials setup, you can use this approach by including the gcloud_credentials parameter. It will accept either a boolean True (in which case it uses the SDK's default credentials path), or string filepath to the credentials file:
+
+.. code-block:: python
+
+ data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table', project_id = projectid, gcloud_credentials = True)
+
You can define which column from BigQuery to use as an index in the
destination DataFrame as well as a preferred column order as follows:
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 937dc421e3926..7ab88edd77d4b 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -2573,13 +2573,13 @@ def is_hashable(arg):
>>> is_hashable(a)
False
"""
- # don't consider anything not collections.Hashable, so as not to broaden
- # the definition of hashable beyond that. For example, old-style classes
- # are not collections.Hashable but they won't fail hash().
- if not isinstance(arg, collections.Hashable):
- return False
+ # unfortunately, we can't use isinstance(arg, collections.Hashable), which
+ # can be faster than calling hash, because numpy scalars on Python 3 fail
+ # this test
+
+ # reconsider this decision once this numpy bug is fixed:
+ # https://github.com/numpy/numpy/issues/5562
- # narrow the definition of hashable if hash(arg) fails in practice
try:
hash(arg)
except TypeError:
diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py
index 572a8be5c65e8..f921c027513e2 100644
--- a/pandas/io/gbq.py
+++ b/pandas/io/gbq.py
@@ -1,9 +1,11 @@
from datetime import datetime
+import os
import json
import logging
import sys
from time import sleep
import uuid
+import traceback
import numpy as np
@@ -33,20 +35,22 @@
try:
from apiclient.discovery import build
from apiclient.http import MediaFileUpload
- from apiclient.errors import HttpError
+ from apiclient.errors import HttpError
from oauth2client.client import OAuth2WebServerFlow
from oauth2client.client import AccessTokenRefreshError
from oauth2client.client import flow_from_clientsecrets
+ from oauth2client.client import Credentials
from oauth2client.file import Storage
from oauth2client.tools import run
_GOOGLE_API_CLIENT_INSTALLED=True
_GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution('google-api-python-client').version
- if LooseVersion(_GOOGLE_API_CLIENT_VERSION) >= '1.2.0':
+ if LooseVersion(_GOOGLE_API_CLIENT_VERSION) >= '1.2':
_GOOGLE_API_CLIENT_VALID_VERSION = True
except ImportError:
+ traceback.format_exc()
_GOOGLE_API_CLIENT_INSTALLED = False
@@ -72,6 +76,13 @@
logger = logging.getLogger('pandas.io.gbq')
logger.setLevel(logging.ERROR)
+class MissingOauthCredentials(PandasError, IOError):
+ """
+ Raised when Google BigQuery authentication credentials
+ file is missing, but was needed.
+ """
+ pass
+
class InvalidPageToken(PandasError, IOError):
"""
Raised when Google BigQuery fails to return,
@@ -119,11 +130,12 @@ class InvalidColumnOrder(PandasError, IOError):
pass
class GbqConnector:
- def __init__(self, project_id, reauth=False):
- self.project_id = project_id
- self.reauth = reauth
- self.credentials = self.get_credentials()
- self.service = self.get_service(self.credentials)
+ def __init__(self, project_id, reauth=False, gcloud_credentials=None):
+ self.project_id = project_id
+ self.reauth = reauth
+ self.gcloud_credentials = gcloud_credentials
+ self.credentials = self.get_credentials()
+ self.service = self.get_service(self.credentials)
def get_credentials(self):
flow = OAuth2WebServerFlow(client_id='495642085510-k0tmvj2m941jhre2nbqka17vqpjfddtd.apps.googleusercontent.com',
@@ -131,8 +143,19 @@ def get_credentials(self):
scope='https://www.googleapis.com/auth/bigquery',
redirect_uri='urn:ietf:wg:oauth:2.0:oob')
- storage = Storage('bigquery_credentials.dat')
- credentials = storage.get()
+ if self.gcloud_credentials is not None:
+ gcfp = self.gcloud_credentials # a bit of mangling since this is dual-typed, str or bool
+ if self.gcloud_credentials == True:
+ gcfp = '~/.config/gcloud/credentials'
+ credfn = os.path.expanduser(gcfp)
+ if not os.path.exists(credfn):
+ raise MissingOauthCredentials("Required google cloud authentication credentials file {0} missing.".format(credfn))
+ gcloud_cred = json.loads(open(credfn).read())['data'][0]['credential']
+ credentials = Credentials.new_from_json(json.dumps(gcloud_cred))
+ return credentials
+ else:
+ storage = Storage('bigquery_credentials.dat')
+ credentials = storage.get()
if credentials is None or credentials.invalid or self.reauth:
credentials = run(flow, storage)
@@ -328,7 +351,8 @@ def _test_imports():
if not _HTTPLIB2_INSTALLED:
raise ImportError("pandas requires httplib2 for Google BigQuery support")
-def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=False):
+def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=False,
+ gcloud_credentials=None):
"""Load data from Google BigQuery.
THIS IS AN EXPERIMENTAL LIBRARY
@@ -353,6 +377,12 @@ def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=Fa
reauth : boolean (default False)
Force Google BigQuery to reauthenticate the user. This is useful
if multiple accounts are used.
+ gcloud_credentials : boolean or str (default None)
+ Use oauth2 credentials from gcloud auth login. This is useful
+ if pandas is being run in an ipython notebook, and the user
+ has pre-existing authentication tokens.
+ Set to True to use the default path, ~/.config/gcloud/credentials.
+ Else provide an explicit path to file to use for credentials.
Returns
-------
@@ -366,7 +396,7 @@ def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=Fa
if not project_id:
raise TypeError("Missing required parameter: project_id")
- connector = GbqConnector(project_id, reauth = reauth)
+ connector = GbqConnector(project_id, reauth = reauth, gcloud_credentials = gcloud_credentials)
schema, pages = connector.run_query(query)
dataframe_list = []
while len(pages) > 0:
@@ -401,7 +431,7 @@ def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=Fa
return final_df
def to_gbq(dataframe, destination_table, project_id=None, chunksize=10000,
- verbose=True, reauth=False):
+ verbose=True, reauth=False, gcloud_credentials=None):
"""Write a DataFrame to a Google BigQuery table.
THIS IS AN EXPERIMENTAL LIBRARY
@@ -430,6 +460,12 @@ def to_gbq(dataframe, destination_table, project_id=None, chunksize=10000,
reauth : boolean (default False)
Force Google BigQuery to reauthenticate the user. This is useful
if multiple accounts are used.
+ gcloud_credentials : boolean or str (default None)
+ Use oauth2 credentials from gcloud auth login. This is useful
+ if pandas is being run in an ipython notebook, and the user
+ has pre-existing authentication tokens.
+ Set to True to use the default path, ~/.config/gcloud/credentials.
+ Else provide an explicit path to file to use for credentials.
"""
_test_imports()
@@ -440,7 +476,7 @@ def to_gbq(dataframe, destination_table, project_id=None, chunksize=10000,
if not '.' in destination_table:
raise NotFoundException("Invalid Table Name. Should be of the form 'datasetId.tableId' ")
- connector = GbqConnector(project_id, reauth = reauth)
+ connector = GbqConnector(project_id, reauth = reauth, gcloud_credentials = gcloud_credentials)
dataset_id, table_id = destination_table.rsplit('.',1)
connector.load_data(dataframe, dataset_id, table_id, chunksize, verbose)
diff --git a/pandas/io/tests/data/gcloud_credentials b/pandas/io/tests/data/gcloud_credentials
new file mode 100644
index 0000000000000..d0bd0381048a8
--- /dev/null
+++ b/pandas/io/tests/data/gcloud_credentials
@@ -0,0 +1,62 @@
+{
+ "data": [
+ {
+ "credential": {
+ "_class": "OAuth2Credentials",
+ "_module": "oauth2client.client",
+ "access_token": "ya29.xXx",
+ "client_id": "1112223456.apps.googleusercontent.com",
+ "client_secret": "aBc467",
+ "id_token": {
+ "at_hash": "OlAp__aV",
+ "aud": "1112223456.apps.googleusercontent.com",
+ "azp": "1112223456.apps.googleusercontent.com",
+ "cid": "1112223456.apps.googleusercontent.com",
+ "email": "luv-python-pandas@somemail.com",
+ "email_verified": true,
+ "exp": 1414558238,
+ "iat": 1414554338,
+ "id": "113403125016275849302",
+ "iss": "accounts.google.com",
+ "sub": "113403125016229475663",
+ "token_hash": "OlAp__aV",
+ "verified_email": true
+ },
+ "invalid": @INVALID@,
+ "refresh_token": "1/asf87bbEGsb78",
+ "revoke_uri": "https://accounts.google.com/o/oauth2/revoke",
+ "token_expiry": "2014-10-29T04:50:38Z",
+ "token_response": {
+ "access_token": "ya29.bYsadfiU8542B5",
+ "expires_in": 3600,
+ "id_token": {
+ "at_hash": "OlAp__aV",
+ "aud": "11112233456.apps.googleusercontent.com",
+ "azp": "11112223456.apps.googleusercontent.com",
+ "cid": "11112223456.apps.googleusercontent.com",
+ "email": "luv-python-pandas@somemail.com",
+ "email_verified": true,
+ "exp": 1414558238,
+ "iat": 1414554338,
+ "id": "11340312501621345098732",
+ "iss": "accounts.google.com",
+ "sub": "1134031250162435660892",
+ "token_hash": "OlAp__aV",
+ "verified_email": true
+ },
+ "refresh_token": "1/6v6asdf6NrR92",
+ "token_type": "Bearer"
+ },
+ "token_uri": "https://accounts.google.com/o/oauth2/token",
+ "user_agent": "Cloud SDK Command Line Tool"
+ },
+ "key": {
+ "account": "luv-python-pandas@somemail.com",
+ "clientId": "11112223456.apps.googleusercontent.com",
+ "scope": "https://www.googleapis.com/auth/appengine.admin https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/compute https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/userinfo.email https://www.googleapis.com/auth/ndev.cloudman https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/sqlservice.admin https://www.googleapis.com/auth/prediction https://www.googleapis.com/auth/projecthosting",
+ "type": "google-cloud-sdk"
+ }
+ }
+ ],
+ "file_version": 1
+}
diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py
index 2f79cc8ba1826..97e4d76a2eb20 100644
--- a/pandas/io/tests/test_gbq.py
+++ b/pandas/io/tests/test_gbq.py
@@ -9,6 +9,7 @@
import sys
import platform
from time import sleep
+from tempfile import NamedTemporaryFile
import numpy as np
@@ -36,6 +37,27 @@ def test_requirements():
raise nose.SkipTest(import_exception)
class TestGBQConnectorIntegration(tm.TestCase):
+
+ @classmethod
+ def setUpClass(cls):
+ with open(os.path.join(tm.get_data_path(), 'gcloud_credentials'), 'r') as fin:
+ creds_json = fin.read()
+
+ creds_json_invalid = creds_json.replace('@INVALID@', '"true"')
+ creds_json_valid = creds_json.replace('@INVALID@', '"false"')
+
+ cls.creds_file_valid = NamedTemporaryFile()
+ cls.creds_file_valid.write(creds_json_valid.encode('UTF-8'))
+ cls.creds_file_valid.flush()
+
+ cls.creds_file_invalid = NamedTemporaryFile()
+ cls.creds_file_invalid.write(creds_json_invalid.encode('UTF-8'))
+ cls.creds_file_invalid.flush()
+
+ cls.non_creds_file = NamedTemporaryFile()
+ cls.non_creds_file.write('{"token": "50414e444153204556455259574845524521"}'.encode('UTF-8'))
+ cls.non_creds_file.flush()
+
def setUp(self):
test_requirements()
@@ -64,6 +86,28 @@ def test_should_be_able_to_get_results_from_query(self):
schema, pages = self.sut.run_query('SELECT 1')
self.assertTrue(pages is not None)
+ def test_should_raise_exception_with_invalid_gcloud_creds_path(self):
+ with tm.assertRaises(gbq.MissingOauthCredentials):
+ gbq.GbqConnector(PROJECT_ID, gcloud_credentials='missing_file')
+
+ def test_should_fail_with_invalid_gcloud_credentials(self):
+ credentials = gbq.GbqConnector(PROJECT_ID, gcloud_credentials=self.creds_file_invalid.name).credentials
+ self.assertEqual(credentials.invalid, "true")
+
+ def test_should_be_able_to_get_valid_gcloud_credentials(self):
+ credentials = gbq.GbqConnector(PROJECT_ID, gcloud_credentials=self.creds_file_valid.name).credentials
+ self.assertEqual(credentials.invalid, "false")
+
+ def test_should_fail_if_gcloud_credentials_incorrectly_formatted(self):
+ with tm.assertRaises(KeyError):
+ gbq.GbqConnector(PROJECT_ID, gcloud_credentials=self.non_creds_file.name)
+
+ @classmethod
+ def tearDownClass(cls):
+ cls.creds_file_valid.close()
+ cls.creds_file_invalid.close()
+ cls.non_creds_file.close()
+
class TestReadGBQUnitTests(tm.TestCase):
def setUp(self):
test_requirements()
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index d8ce98350627d..d0ae7c9988c8d 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -424,7 +424,7 @@ def __hash__(self):
raise TypeError("Not hashable")
hashable = (
- 1, 'a', tuple(), (1,), HashableClass(),
+ 1, 3.14, np.float64(3.14), 'a', tuple(), (1,), HashableClass(),
)
not_hashable = (
[], UnhashableClass1(),
@@ -434,13 +434,10 @@ def __hash__(self):
)
for i in hashable:
- assert isinstance(i, collections.Hashable)
assert com.is_hashable(i)
for i in not_hashable:
- assert not isinstance(i, collections.Hashable)
assert not com.is_hashable(i)
for i in abc_hashable_not_really_hashable:
- assert isinstance(i, collections.Hashable)
assert not com.is_hashable(i)
# numpy.array is no longer collections.Hashable as of
@@ -455,7 +452,7 @@ class OldStyleClass():
pass
c = OldStyleClass()
assert not isinstance(c, collections.Hashable)
- assert not com.is_hashable(c)
+ assert com.is_hashable(c)
hash(c) # this will not raise
| closes #8489
The pandas google bigquery connector can be difficult to use from an ipython notebook: when no authentication token exists, it fails silently (and can hang the notebook). This happens because the oauth2client library is trying to bring up a browser on the console, to request an auth token.
This PR provides an improved mechanism: an optional `use_gcloud_auth` argument which specifies that the user wants to employ a pre-existing authentication token, generated using [gcloud auth login](https://cloud.google.com/sdk/gcloud/reference/auth/login). An error is raised if the credentials file is missing.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8590 | 2014-10-21T01:06:54Z | 2015-05-09T16:08:56Z | null | 2022-10-13T00:16:11Z |
BUG: LooseVersion was used incorrectly | diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py
index abb743d3ba04e..20c1e9f591081 100644
--- a/pandas/io/gbq.py
+++ b/pandas/io/gbq.py
@@ -43,7 +43,7 @@
_GOOGLE_API_CLIENT_INSTALLED=True
_GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution('google-api-python-client').version
- if LooseVersion(_GOOGLE_API_CLIENT_VERSION >= '1.2.0'):
+ if LooseVersion(_GOOGLE_API_CLIENT_VERSION) >= '1.2.0':
_GOOGLE_API_CLIENT_VALID_VERSION = True
except ImportError:
@@ -56,7 +56,7 @@
_GOOGLE_FLAGS_VERSION = pkg_resources.get_distribution('python-gflags').version
- if LooseVersion(_GOOGLE_FLAGS_VERSION >= '2.0.0'):
+ if LooseVersion(_GOOGLE_FLAGS_VERSION) >= '2.0':
_GOOGLE_FLAGS_VALID_VERSION = True
except ImportError:
| closes #8581
Version comparison had a formatting error resulting in "TypeError: expected string or buffer" and preventing Spyder from loading (and probably other issues). Fixed.
| https://api.github.com/repos/pandas-dev/pandas/pulls/8587 | 2014-10-20T00:50:35Z | 2014-10-20T12:23:30Z | null | 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 |
BUG/FIX: Fix dtype inference issue in query | diff --git a/doc/source/v0.15.0.txt b/doc/source/v0.15.0.txt
index a40e1d87776d9..ee964b4431c44 100644
--- a/doc/source/v0.15.0.txt
+++ b/doc/source/v0.15.0.txt
@@ -1121,3 +1121,6 @@ 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``.
diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py
index 81526b88c2b51..9df9975b4b61c 100644
--- a/pandas/computation/ops.py
+++ b/pandas/computation/ops.py
@@ -1,10 +1,8 @@
"""Operator classes for eval.
"""
-import re
import operator as op
from functools import partial
-from itertools import product, islice, chain
from datetime import datetime
import numpy as np
@@ -69,7 +67,6 @@ def evaluate(self, *args, **kwargs):
return self
def _resolve_name(self):
- key = self.name
res = self.env.resolve(self.local_name, is_local=self.is_local)
self.update(res)
@@ -491,3 +488,13 @@ def __call__(self, env):
def __unicode__(self):
return com.pprint_thing('{0}({1})'.format(self.op, self.operand))
+
+ @property
+ def return_type(self):
+ operand = self.operand
+ if operand.return_type == np.dtype('bool'):
+ return np.dtype('bool')
+ if (isinstance(operand, Op) and
+ (operand.op in _cmp_ops_dict or operand.op in _bool_ops_dict)):
+ return np.dtype('bool')
+ return np.dtype('int')
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py
index 1193dbb3c0d81..7ca67bec12e20 100644
--- a/pandas/computation/tests/test_eval.py
+++ b/pandas/computation/tests/test_eval.py
@@ -1627,6 +1627,19 @@ def test_inf():
yield check_inf, engine, parser
+def check_negate_lt_eq_le(engine, parser):
+ tm.skip_if_no_ne(engine)
+ df = pd.DataFrame([[0, 10], [1, 20]], columns=['cat', 'count'])
+ result = df.query('~(cat > 0)', engine=engine, parser=parser)
+ expected = df[~(df.cat > 0)]
+ tm.assert_frame_equal(result, expected)
+
+
+def test_negate_lt_eq_le():
+ for engine, parser in product(_engines, expr._parsers):
+ yield check_negate_lt_eq_le, engine, parser
+
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| closes #8568
| https://api.github.com/repos/pandas-dev/pandas/pulls/8576 | 2014-10-18T00:01:47Z | 2014-10-18T12:55:27Z | null | 2015-04-30T01:56:20Z |
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 |
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