Detail: IndexNanosecondGO: Attribute#

IndexNanosecondGO.STATIC = False#

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.STATIC
False

IndexNanosecondGO.depth = 1#

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.depth
1

IndexNanosecondGO.dtype#

Return the dtype of the underlying NumPy array.

Returns:: numpy.dtype

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.dtype
datetime64[ns]

IndexNanosecondGO.index_types#

Return a Series of Index classes for each index depth.

Returns:: Series

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.index_types
<Series>
<Index>
None     <IndexNanosecondGO>
<object> <object>

IndexNanosecondGO.memory#

A MemoryDisplay, providing the size in memory of this object. For compound containers, component sizes will also be provided. Size can be interpreted through six combinations of three configurations:

L: Local: memory ignoring referenced array data provided via views. LM: Local Materialized: memory where arrays that are locally owned report their byte payload LMD: Local Materialized Data: locally owned memory of arrays byte payloads, excluding all other components

R: Referenced: memory including referenced array data provided via views RM: Referenced Materialized: memory where arrays that are locally owned or referenced report their byte payload RMD: Referenced Materialized Data: localy owned and referenced array byte payloads, excluding all other components

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.memory
          L    Lu    LM   LMu   LMD  LMDu  R    Ru    RM   RMu   RMD  RMDu
Name      16   B     16   B     16   B     16   B     16   B     16   B
Map       472  B     472  B     472  B     472  B     472  B     472  B
Labels    136  B     152  B     24   B     136  B     152  B     24   B
Positions 112  B     128  B     0    B     8.22 KB    152  B     24   B
Total     1.23 KB    1.26 KB    1.01 KB    9.34 KB    1.29 KB    1.04 KB

IndexNanosecondGO.mloc#: The memory location, represented as an integer, of the underlying NumPy array.

IndexNanosecondGO.name#

A hashable label attached to this container.

Returns:: Hashable

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.name

IndexNanosecondGO.names#

Provide a suitable iterable of names for usage in output formats that require a field name as string for the index.

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.names
('__index0__',)

IndexNanosecondGO.nbytes#

Return the total bytes of the underlying NumPy array.

Returns:: int

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.nbytes
24

IndexNanosecondGO.ndim#

Return the number of dimensions.

Returns:: int

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.ndim
1

IndexNanosecondGO.positions#

Return the immutable positions array.

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.positions
[0 1 2]

IndexNanosecondGO.shape#

Return a tuple describing the shape of the underlying NumPy array.

Returns:: tp.Tuple[int]

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.shape
(3,)

IndexNanosecondGO.size#

Return the size of the underlying NumPy array.

Returns:: int

>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix.size
3