本文整理汇总了Python中pandas.compat.PYPY属性的典型用法代码示例。如果您正苦于以下问题：Python compat.PYPY属性的具体用法？Python compat.PYPY怎么用？Python compat.PYPY使用的例子？那么, 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.compat的用法示例。
在下文中一共展示了compat.PYPY属性的6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: test_memory_usage
# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PYPY [as 别名]
def test_memory_usage(self):
cat = Categorical([1, 2, 3])
# .categories is an index, so we include the hashtable
assert 0 < cat.nbytes <= cat.memory_usage()
assert 0 < cat.nbytes <= cat.memory_usage(deep=True)
cat = Categorical(['foo', 'foo', 'bar'])
assert cat.memory_usage(deep=True) > cat.nbytes
if not PYPY:
# sys.getsizeof will call the .memory_usage with
# deep=True, and add on some GC overhead
diff = cat.memory_usage(deep=True) - sys.getsizeof(cat)
assert abs(diff) < 100 示例2: memory_usage
# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PYPY [as 别名]
def memory_usage(self, deep=False):
"""
Memory usage of the values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
See Also
--------
numpy.ndarray.nbytes
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy
"""
if hasattr(self.array, 'memory_usage'):
return self.array.memory_usage(deep=deep)
v = self.array.nbytes
if deep and is_object_dtype(self) and not PYPY:
v += lib.memory_usage_of_objects(self.array)
return v 示例3: memory_usage
# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PYPY [as 别名]
def memory_usage(self, deep=False):
values = self.sp_values
v = values.nbytes
if deep and is_object_dtype(self) and not PYPY:
v += lib.memory_usage_of_objects(values)
return v 示例4: memory_usage
# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PYPY [as 别名]
def memory_usage(self, deep=False):
"""
Memory usage of the values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy
See Also
--------
numpy.ndarray.nbytes
"""
if hasattr(self.values, 'memory_usage'):
return self.values.memory_usage(deep=deep)
v = self.values.nbytes
if deep and is_object_dtype(self) and not PYPY:
v += lib.memory_usage_of_objects(self.values)
return v 示例5: memory_usage
# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PYPY [as 别名]
def memory_usage(self, deep=False):
"""
Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy
See Also
--------
numpy.ndarray.nbytes
"""
if hasattr(self.values, 'memory_usage'):
return self.values.memory_usage(deep=deep)
v = self.values.nbytes
if deep and is_object_dtype(self) and not PYPY:
v += lib.memory_usage_of_objects(self.values)
return v 示例6: test_memory_usage
# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PYPY [as 别名]
def test_memory_usage(self):
cat = pd.Categorical([1, 2, 3])
# .categories is an index, so we include the hashtable
assert 0 < cat.nbytes <= cat.memory_usage()
assert 0 < cat.nbytes <= cat.memory_usage(deep=True)
cat = pd.Categorical(['foo', 'foo', 'bar'])
assert cat.memory_usage(deep=True) > cat.nbytes
if not PYPY:
# sys.getsizeof will call the .memory_usage with
# deep=True, and add on some GC overhead
diff = cat.memory_usage(deep=True) - sys.getsizeof(cat)
assert abs(diff) < 100