Detail: IndexNanosecond: Constructor#

Overview: IndexNanosecond: Constructor

IndexNanosecond.__init__(labels, /, *, loc_is_iloc=False, name=<object object>, sort_status=SortStatus.UNKNOWN)#

Initializer.

Parameters:

labels – Iterable of hashable values to be used as the index labels. If strings, NumPy datetime conversions will be applied.
name – A hashable object to label the container.

>>> sf.IndexNanosecond(('1789-05-05', '1789-12-31', '1799-11-09'))
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
<datetime64[ns]>

classmethod IndexNanosecond.from_difference(*others)#

Construct a new Index based on the difference with Index, containers, or NumPy arrays. Retains order.

>>> ix1 = sf.IndexNanosecond.from_labels(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix1
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
<datetime64[ns]>
>>> ix2 = sf.IndexNanosecond.from_labels(('2022-04-01', '2021-12-31', '2018-06-30'))
>>> ix2
<IndexNanosecond>
2022-04-01T00:00:00.000000000
2021-12-31T00:00:00.000000000
2018-06-30T00:00:00.000000000
<datetime64[ns]>
>>> sf.IndexNanosecond.from_difference(ix1, ix2)
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
<datetime64[ns]>

classmethod IndexNanosecond.from_intersection(*others)#

Construct a new Index based on the intersection with Index, containers, or NumPy arrays. Identical comparisons retain order.

>>> ix1 = sf.IndexNanosecond.from_labels(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix1
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
<datetime64[ns]>
>>> ix2 = sf.IndexNanosecond.from_labels(('2022-04-01', '2021-12-31', '2018-06-30'))
>>> ix2
<IndexNanosecond>
2022-04-01T00:00:00.000000000
2021-12-31T00:00:00.000000000
2018-06-30T00:00:00.000000000
<datetime64[ns]>
>>> sf.IndexNanosecond.from_intersection(ix1, ix2)
<IndexNanosecond>
<datetime64[ns]>

classmethod IndexNanosecond.from_labels(labels, /, *, name=None)#

Construct an Index from an iterable of labels, where each label is a hashable. Provided for a compatible interface to IndexHierarchy.

>>> sf.IndexNanosecond.from_labels(('1789-05-05', '1789-12-31', '1799-11-09'))
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
<datetime64[ns]>

classmethod IndexNanosecond.from_pandas(value, /)#

Given a Pandas index, return the appropriate IndexBase derived class.

>>> ix = pd.Index(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> sf.IndexNanosecond.from_pandas(ix)
<Index>
1789-05-05
1789-12-31
1799-11-09
<object>

classmethod IndexNanosecond.from_union(*others)#

Construct a new Index based on the union with Index, containers, or NumPy arrays. Identical comparisons retain order.

>>> ix1 = sf.IndexNanosecond.from_labels(('1789-05-05', '1789-12-31', '1799-11-09'))
>>> ix1
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
<datetime64[ns]>
>>> ix2 = sf.IndexNanosecond.from_labels(('2022-04-01', '2021-12-31', '2018-06-30'))
>>> ix2
<IndexNanosecond>
2022-04-01T00:00:00.000000000
2021-12-31T00:00:00.000000000
2018-06-30T00:00:00.000000000
<datetime64[ns]>
>>> sf.IndexNanosecond.from_union(ix1, ix2)
<IndexNanosecond>
1789-05-05T00:00:00.000000000
1789-12-31T00:00:00.000000000
1799-11-09T00:00:00.000000000
2018-06-30T00:00:00.000000000
2021-12-31T00:00:00.000000000
2022-04-01T00:00:00.000000000
<datetime64[ns]>