本文整理汇总了Python中pandas.core.dtypes.common._NS_DTYPE属性的典型用法代码示例。如果您正苦于以下问题：Python common._NS_DTYPE属性的具体用法？Python common._NS_DTYPE怎么用？Python common._NS_DTYPE使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类pandas.core.dtypes.common的用法示例。
在下文中一共展示了common._NS_DTYPE属性的3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: _hash_categories
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import _NS_DTYPE [as 别名]
def _hash_categories(categories, ordered=True):
from pandas.core.util.hashing import (
hash_array, _combine_hash_arrays, hash_tuples
)
from pandas.core.dtypes.common import is_datetime64tz_dtype, _NS_DTYPE
if len(categories) and isinstance(categories[0], tuple):
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
categories = list(categories) # breaks if a np.array of categories
cat_array = hash_tuples(categories)
else:
if categories.dtype == 'O':
types = [type(x) for x in categories]
if not len(set(types)) == 1:
# TODO: hash_array doesn't handle mixed types. It casts
# everything to a str first, which means we treat
# {'1', '2'} the same as {'1', 2}
# find a better solution
hashed = hash((tuple(categories), ordered))
return hashed
if is_datetime64tz_dtype(categories.dtype):
# Avoid future warning.
categories = categories.astype(_NS_DTYPE)
cat_array = hash_array(np.asarray(categories), categorize=False)
if ordered:
cat_array = np.vstack([
cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)
])
else:
cat_array = [cat_array]
hashed = _combine_hash_arrays(iter(cat_array),
num_items=len(cat_array))
return np.bitwise_xor.reduce(hashed) 示例2: _where_compat
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import _NS_DTYPE [as 别名]
def _where_compat(mask, arr1, arr2):
if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
return new_vals.view(_NS_DTYPE)
if arr1.dtype == _NS_DTYPE:
arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
if arr2.dtype == _NS_DTYPE:
arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))
return np.where(mask, arr1, arr2) 示例3: _unpickle_array
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import _NS_DTYPE [as 别名]
def _unpickle_array(bytes):
arr = read_array(BytesIO(bytes))
# All datetimes should be stored as M8[ns]. When unpickling with
# numpy1.6, it will read these as M8[us]. So this ensures all
# datetime64 types are read as MS[ns]
if is_datetime64_dtype(arr):
arr = arr.view(_NS_DTYPE)
return arr