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
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
pc_precision_doc = """
: int
"""
config for datetime formatting
"""
from pandas._config import config as cf
pc_date_dayfirst_doc = """
: boolean
When True, prints and parses dates with the day first, eg 20/01/2005
"""
pc_date_yearfirst_doc = """
: boolean
When True, prints and parses dates with the year first, eg 2005/01/20
"""
with cf.config_prefix('display'):
# Needed upstream of `_libs` because these are used in tslibs.parsing
cf.register_option('date_dayfirst', False, pc_date_dayfirst_doc,
validator=cf.is_bool)
cf.register_option('date_yearfirst', False, pc_date_yearfirst_doc,
validator=cf.is_bool)
"""
config for datetime formatting
"""
from __future__ import annotations
from pandas._config import config as cf
pc_date_dayfirst_doc = """
: boolean
When True, prints and parses dates with the day first, eg 20/01/2005
"""
pc_date_yearfirst_doc = """
: boolean
When True, prints and parses dates with the year first, eg 2005/01/20
"""
with cf.config_prefix("display"):
# Needed upstream of `_libs` because these are used in tslibs.parsing
cf.register_option("date_dayfirst",
False,
pc_date_dayfirst_doc,
validator=cf.is_bool)
cf.register_option("date_yearfirst",
False,
pc_date_yearfirst_doc,
validator=cf.is_bool)
# try again for something better
if not encoding or 'ascii' in encoding.lower():
try:
encoding = locale.getpreferredencoding()
except Exception:
pass
# when all else fails. this will usually be "ascii"
if not encoding or 'ascii' in encoding.lower():
encoding = sys.getdefaultencoding()
# GH#3360, save the reported defencoding at import time
# MPL backends may change it. Make available for debugging.
if not _initial_defencoding:
_initial_defencoding = sys.getdefaultencoding()
return encoding
pc_encoding_doc = """
: str/unicode
Defaults to the detected encoding of the console.
Specifies the encoding to be used for strings returned by to_string,
these are generally strings meant to be displayed on the console.
"""
with cf.config_prefix('display'):
cf.register_option('encoding', detect_console_encoding(), pc_encoding_doc,
validator=cf.is_text)
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
pc_precision_doc = """
: int
Floating point output precision (number of significant digits). This is
only a suggestion
"""
pc_colspace_doc = """
: int
