_sequence_to_dt64ns(data, dtype=None, copy=False, tz=None, dayfirst=False, yearfirst=False, ambiguous='raise', *, allow_mixed: 'bool' = False, require_iso8601: 'bool' = False)

Parameters

data : list-like

dtype : dtype, str, or None, default None

copy : bool, default False

tz : tzinfo, str, or None, default None

dayfirst : bool, default False

yearfirst : bool, default False

ambiguous : str, bool, or arraylike, default 'raise'

See pandas._libs.tslibs.tzconversion.tz_localize_to_utc.

allow_mixed : bool, default False

Interpret integers as timestamps when datetime objects are also present.

require_iso8601 : bool, default False

Only consider ISO-8601 formats when parsing strings.

Raises

TypeError : PeriodDType data is passed

Returns

result : numpy.ndarray

The sequence converted to a numpy array with dtype datetime64[ns] .

tz : tzinfo or None

Either the user-provided tzinfo or one inferred from the data.

inferred_freq : Tick or None

The inferred frequency of the sequence.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /pandas/core/arrays/datetimes.py#1994
type: <class 'function'>
Commit: