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PERF: pd.Index.is_all_dates doesn't require full-scan inference #6341

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Merged
merged 1 commit into from
Feb 14, 2014
Merged

PERF: pd.Index.is_all_dates doesn't require full-scan inference #6341

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1 change: 1 addition & 0 deletions doc/source/release.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -74,6 +74,7 @@ Improvements to existing features
the func (:issue:`6289`)
- ``plot(legend='reverse')`` will now reverse the order of legend labels for most plot kinds.
(:issue:`6014`)
- improve performance of slice indexing on Series with string keys (:issue:`6341`)

.. _release.bug_fixes-0.14.0:

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2 changes: 1 addition & 1 deletion doc/source/v0.14.0.txt
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Expand Up @@ -46,7 +46,7 @@ Enhancements
and parse accordingly. (:issue:`6223`)
- ``plot(legend='reverse')`` will now reverse the order of legend labels for
most plot kinds. (:issue:`6014`)

- improve performance of slice indexing on Series with string keys (:issue:`6341`)

Performance
~~~~~~~~~~~
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4 changes: 2 additions & 2 deletions pandas/core/index.py
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Expand Up @@ -9,7 +9,7 @@
import pandas.lib as lib
import pandas.algos as _algos
import pandas.index as _index
from pandas.lib import Timestamp
from pandas.lib import Timestamp, is_datetime_array
from pandas.core.base import FrozenList, FrozenNDArray

from pandas.util.decorators import cache_readonly, deprecate
Expand Down Expand Up @@ -577,7 +577,7 @@ def is_type_compatible(self, typ):

@cache_readonly
def is_all_dates(self):
return self.inferred_type == 'datetime'
return is_datetime_array(self.values)

def __iter__(self):
return iter(self.values)
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11 changes: 10 additions & 1 deletion vb_suite/indexing.py
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Expand Up @@ -7,7 +7,7 @@
"""

#----------------------------------------------------------------------
# Series.__getitem__, get_value
# Series.__getitem__, get_value, __getitem__(slice)

setup = common_setup + """
tm.N = 1000
Expand All @@ -29,6 +29,15 @@
name='series_get_value',
start_date=datetime(2011, 11, 12))


setup = common_setup + """
index = tm.makeStringIndex(1000000)
s = Series(np.random.rand(1000000), index=index)
"""
series_getitem_slice = Benchmark("s[:800000]", setup,
name="series_getitem_slice")


#----------------------------------------------------------------------
# DataFrame __getitem__

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