@@ -93,7 +93,7 @@ Accelerated operations

----------------------

pandas has support for accelerating certain types of binary numerical and boolean operations using

the ``numexpr`` library (starting in 0.11.0) and the ``bottleneck`` libraries.

the ``numexpr`` library and the ``bottleneck`` libraries.

These libraries are especially useful when dealing with large data sets, and provide large

speedups. ``numexpr`` uses smart chunking, caching, and multiple cores. ``bottleneck`` is

@@ -114,6 +114,15 @@ Here is a sample (using 100 column x 100,000 row ``DataFrames``):

You are highly encouraged to install both libraries. See the section

:ref:`Recommended Dependencies <install.recommended_dependencies>` for more installation info.

These are both enabled to be used by default, you can control this by setting the options:

.. versionadded:: 0.20.0

.. code-block:: python

pd.set_option('compute.use_bottleneck', False)

pd.set_option('compute.use_numexpr', False)

.. _basics.binop:

Flexible binary operations

@@ -425,6 +425,10 @@ mode.use_inf_as_null False True means treat None, NaN, -IN

INF as null (old way), False means

None and NaN are null, but INF, -INF

are not null (new way).

compute.use_bottleneck True Use the bottleneck library to accelerate

computation if it is installed

compute.use_numexpr True Use the numexpr library to accelerate

computation if it is installed

=================================== ============ ==================================

@@ -538,4 +542,4 @@ Only ``'display.max_rows'`` are serialized and published.

.. ipython:: python

:suppress:

pd.reset_option('display.html.table_schema')

pd.reset_option('display.html.table_schema')

@@ -521,6 +521,7 @@ Other Enhancements

- The ``display.show_dimensions`` option can now also be used to specify

whether the length of a ``Series`` should be shown in its repr (:issue:`7117`).

- ``parallel_coordinates()`` has gained a ``sort_labels`` keyword arg that sorts class labels and the colours assigned to them (:issue:`15908`)

- Options added to allow one to turn on/off using ``bottleneck`` and ``numexpr``, see :ref:`here <basics.accelerate>` (:issue:`16157`)

.. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations

@@ -1217,7 +1218,7 @@ If indicated, a deprecation warning will be issued if you reference theses modul

"pandas.lib", "pandas._libs.lib", "X"

"pandas.tslib", "pandas._libs.tslib", "X"

"pandas.computation", "pandas.core.computation", ""

"pandas.computation", "pandas.core.computation", "X"

"pandas.msgpack", "pandas.io.msgpack", ""

"pandas.index", "pandas._libs.index", ""

"pandas.algos", "pandas._libs.algos", ""

@@ -0,0 +1,11 @@

import warnings

def set_use_numexpr(v=True):

warnings.warn("pandas.computation.expressions.set_use_numexpr is "

"deprecated and will be removed in a future version.\n"

"you can toggle usage of numexpr via "

"pandas.get_option('compute.use_numexpr')",

FutureWarning, stacklevel=2)

from pandas import set_option

set_option('compute.use_numexpr', v)

@@ -10,6 +10,7 @@

import numpy as np

from pandas.core.common import _values_from_object

from pandas.core.computation import _NUMEXPR_INSTALLED

from pandas.core.config import get_option

if _NUMEXPR_INSTALLED:

import numexpr as ne

@@ -156,7 +157,7 @@ def _where_numexpr(cond, a, b, raise_on_error=False):

# turn myself on

set_use_numexpr(True)

set_use_numexpr(get_option('compute.use_numexpr'))

def _has_bool_dtype(x):

@@ -15,8 +15,41 @@

from pandas.core.config import (is_int, is_bool, is_text, is_instance_factory,

is_one_of_factory, get_default_val,

is_callable)

from pandas.io.formats.format import detect_console_encoding

from pandas.io.formats.console import detect_console_encoding

# compute

use_bottleneck_doc = """

: bool

Use the bottleneck library to accelerate if it is installed,

the default is True

Valid values: False,True

"""

def use_bottleneck_cb(key):

from pandas.core import nanops

nanops.set_use_bottleneck(cf.get_option(key))

use_numexpr_doc = """

: bool

Use the numexpr library to accelerate computation if it is installed,

the default is True

Valid values: False,True

"""

def use_numexpr_cb(key):

from pandas.core.computation import expressions

expressions.set_use_numexpr(cf.get_option(key))

with cf.config_prefix('compute'):

cf.register_option('use_bottleneck', True, use_bottleneck_doc,

validator=is_bool, cb=use_bottleneck_cb)

cf.register_option('use_numexpr', True, use_numexpr_doc,

validator=is_bool, cb=use_numexpr_cb)

#

# options from the "display" namespace

@@ -91,6 +91,7 @@

import pandas.core.nanops as nanops

import pandas.core.ops as ops

import pandas.io.formats.format as fmt

import pandas.io.formats.console as console

from pandas.io.formats.printing import pprint_thing

import pandas.plotting._core as gfx

@@ -513,7 +514,7 @@ def _repr_fits_horizontal_(self, ignore_width=False):

GH3541, GH3573

"""

width, height = fmt.get_console_size()

width, height = console.get_console_size()

max_columns = get_option("display.max_columns")

nb_columns = len(self.columns)

@@ -577,7 +578,7 @@ def __unicode__(self):

max_cols = get_option("display.max_columns")

show_dimensions = get_option("display.show_dimensions")

if get_option("display.expand_frame_repr"):

width, _ = fmt.get_console_size()

width, _ = console.get_console_size()

else:

width = None

self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,

@@ -837,7 +837,8 @@ def _format_data(self):

"""

Return the formatted data as a unicode string

"""

from pandas.io.formats.format import get_console_size, _get_adjustment

from pandas.io.formats.console import get_console_size

from pandas.io.formats.format import _get_adjustment

display_width, _ = get_console_size()

if display_width is None:

display_width = get_option('display.width') or 80

@@ -1,14 +1,8 @@

import itertools

import functools

import numpy as np

import operator

try:

import bottleneck as bn

_USE_BOTTLENECK = True

except ImportError: # pragma: no cover

_USE_BOTTLENECK = False

import numpy as np

from pandas import compat

from pandas._libs import tslib, algos, lib

from pandas.core.dtypes.common import (

@@ -23,9 +17,27 @@

is_int_or_datetime_dtype, is_any_int_dtype)

from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask

from pandas.core.dtypes.missing import isnull, notnull

from pandas.core.config import get_option

from pandas.core.common import _values_from_object

try:

import bottleneck as bn

_BOTTLENECK_INSTALLED = True

except ImportError: # pragma: no cover

_BOTTLENECK_INSTALLED = False

_USE_BOTTLENECK = False

def set_use_bottleneck(v=True):

# set/unset to use bottleneck

global _USE_BOTTLENECK

if _BOTTLENECK_INSTALLED:

_USE_BOTTLENECK = v

set_use_bottleneck(get_option('compute.use_bottleneck'))

class disallow(object):