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