本文整理汇总了Python中pandas._libs.hashtable.ismember_int64方法的典型用法代码示例。如果您正苦于以下问题：Python hashtable.ismember_int64方法的具体用法？Python hashtable.ismember_int64怎么用？Python hashtable.ismember_int64使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas._libs.hashtable的用法示例。
在下文中一共展示了hashtable.ismember_int64方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: isin
# 需要导入模块: from pandas._libs import hashtable [as 别名]
# 或者: from pandas._libs.hashtable import ismember_int64 [as 别名]
def isin(comps, values):
"""
Compute the isin boolean array
Parameters
----------
comps: array-like
values: array-like
Returns
-------
boolean array same length as comps
"""
if not is_list_like(comps):
raise TypeError("only list-like objects are allowed to be passed"
" to isin(), you passed a [{comps_type}]"
.format(comps_type=type(comps).__name__))
if not is_list_like(values):
raise TypeError("only list-like objects are allowed to be passed"
" to isin(), you passed a [{values_type}]"
.format(values_type=type(values).__name__))
if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
values = lib.list_to_object_array(list(values))
comps, dtype, _ = _ensure_data(comps)
values, _, _ = _ensure_data(values, dtype=dtype)
# faster for larger cases to use np.in1d
f = lambda x, y: htable.ismember_object(x, values)
# GH16012
# Ensure np.in1d doesn't get object types or it *may* throw an exception
if len(comps) > 1000000 and not is_object_dtype(comps):
f = lambda x, y: np.in1d(x, y)
elif is_integer_dtype(comps):
try:
values = values.astype('int64', copy=False)
comps = comps.astype('int64', copy=False)
f = lambda x, y: htable.ismember_int64(x, y)
except (TypeError, ValueError):
values = values.astype(object)
comps = comps.astype(object)
elif is_float_dtype(comps):
try:
values = values.astype('float64', copy=False)
comps = comps.astype('float64', copy=False)
checknull = isna(values).any()
f = lambda x, y: htable.ismember_float64(x, y, checknull)
except (TypeError, ValueError):
values = values.astype(object)
comps = comps.astype(object)
return f(comps, values)