Detail: IndexNanosecondGO: Method
Overview: IndexNanosecondGO: Method
- IndexNanosecondGO.__array__(dtype=None)
Support the __array__ interface, returning an array of values.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.__array__() ['1789-05-05T00:00:00.000000000' '1789-12-31T00:00:00.000000000' '1799-11-09T00:00:00.000000000']
- IndexNanosecondGO.__array_ufunc__(ufunc, method, *args, **kwargs)
Support for NumPy elements or arrays on the left hand of binary operators.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> np.array((0, 1, 0)) * ix UFuncTypeError(<ufunc 'multiply'>, (dtype('int64'), dtype('<M8[ns]')))
- IndexNanosecondGO.__bool__()
Raises ValueError to prohibit ambiguous use of truthy evaluation.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> bool(ix) ErrorNotTruthy('The truth value of a container is ambiguous. For a truthy indicator of non-empty status, use the `size` attribute.')
- IndexNanosecondGO.__copy__()
Return shallow copy of this Index.
>>> import copy >>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> copy.copy(ix) <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.__deepcopy__(memo)
>>> import copy >>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> copy.deepcopy(ix) <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.__len__()
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> len(ix) 3
- IndexNanosecondGO.all(axis=0, skipna=True, out=None)
Logical
and
over values along the specified axis.- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.all() True
- IndexNanosecondGO.any(axis=0, skipna=True, out=None)
Logical
or
over values along the specified axis.- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.any() True
- IndexNanosecondGO.append(value)
Specialize for fixed-typed indices: convert value argument; do not need to resolve_dtype with each addition; self._map is never None
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.append('f') ValueError('Error parsing datetime string "f" at position 0') >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.astype(dtype)
Return an Index with type determined by dtype argument. If a datetime64 dtype is provided, the appropriate
Index
subclass will be returned. Note that for Index, this is a simple function, whereas forIndexHierarchy
, this is an interface exposing both a callable and a getitem interface.- Parameters:
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.astype(str) <IndexGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <<U48>
- IndexNanosecondGO.copy()
Return shallow copy of this Index.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.copy() <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.cumprod(axis=0, skipna=True)
Return the cumulative product over the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.cumprod() UFuncTypeError(<ufunc 'multiply'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.cumsum(axis=0, skipna=True)
Return the cumulative sum over the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.cumsum() UFuncTypeError(<ufunc 'add'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.difference(*others)
Perform difference with another Index, container, or NumPy array. Retains order.
>>> ix1 = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix1 <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix2 = sf.IndexNanosecondGO(('2022-04-01', '2021-12-31', '2022-06-30')) >>> ix2 <IndexNanosecondGO> 2022-04-01T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]> >>> ix1.difference(ix2) <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.dropfalsy()
Return a new
Index
after removing values of NaN or None.>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 NaT 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.dropfalsy() <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.dropna()
Return a new
Index
after removing values of NaN or None.>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 NaT 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.dropna() <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.equals(other, *, compare_name=False, compare_dtype=False, compare_class=False, skipna=True)
Return a
bool
from comparison to any other object.- Parameters:
compare_name – Include equality of the container’s name (and all composed containers) in the comparison.
compare_dtype – Include equality of the container’s dtype (and all composed containers) in the comparison.
compare_class – Include equality of the container’s class (and all composed containers) in the comparison.
skipna – If True, comparisons between missing values are equal.
>>> ix1 = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix1 <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix2 = sf.IndexNanosecondGO(('2022-04-01', '2021-12-31', '2022-06-30')) >>> ix2 <IndexNanosecondGO> 2022-04-01T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]> >>> ix1.equals(ix2) False
- IndexNanosecondGO.extend(values)
Append multiple values :param values: can be a generator.
>>> ix1 = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix1 <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix2 = sf.IndexNanosecondGO(('2022-04-01', '2021-12-31', '2022-06-30')) >>> ix2 <IndexNanosecondGO> 2022-04-01T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]> >>> ix1.extend(ix2) >>> ix1 <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 2022-04-01T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.fillfalsy(value)
Return an
Index
with replacing falsy values with the supplied value.- Parameters:
value – Value to be used to replace missing values (NaN or None).
>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 NaT 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.fillfalsy('A') ValueError('Error parsing datetime string "A" at position 0')
- IndexNanosecondGO.fillna(value)
Return an
Index
with replacing null (NaN or None) with the supplied value.- Parameters:
value – Value to be used to replace missing values (NaN or None).
>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 NaT 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.fillna(0) <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1970-01-01T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.head(count=5)
Return a
Index
consisting only of the top elements as specified bycount
.- Parameters:
count – Number of elements to be returned from the top of the
Index
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.head(2) <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.iloc_searchsorted(values, *, side_left=True)
Given a sorted
Series
, return the iloc (integer) position(s) at which insertion invalues
would retain sort order.- Parameters:
values – a single value, or iterable of values.
side_left – If True, the index of the first suitable location found is given, else return the last such index. If matching an existing value, side_left==True will return that position, side_left==Right will return the next position (or the length).
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.iloc_searchsorted('c') ValueError('Error parsing datetime string "c" at position 0')
- IndexNanosecondGO.intersection(*others)
Perform intersection with one or many Index, container, or NumPy array. Identical comparisons retain order.
>>> ix1 = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix1 <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix2 = sf.IndexNanosecondGO(('2022-04-01', '2021-12-31', '2022-06-30')) >>> ix2 <IndexNanosecondGO> 2022-04-01T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]> >>> ix1.intersection(ix2) <IndexNanosecondGO> <datetime64[ns]>
- IndexNanosecondGO.isfalsy()
Return a same-shaped, Boolean
ndarray
indicating which values are falsy.>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix.isfalsy() [False True False]
- IndexNanosecondGO.isin(other)
Return a Boolean array showing True where a label is found in other. If other is a multidimensional array, it is flattened.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.isin(('1789-05-05',)) [False False False]
- IndexNanosecondGO.isna()
Return a same-shaped, Boolean
ndarray
indicating which values are NaN or None.>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix.isna() [False True False]
- IndexNanosecondGO.label_widths_at_depth(depth_level=0)
A generator of pairs, where each pair is the label and the contiguous count of that label found at the depth specified by
depth_level
.- Parameters:
depth_level – a depth level, starting from zero.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> tuple(ix.label_widths_at_depth(0)) ((numpy.datetime64('1789-05-05T00:00:00.000000000'), 1), (numpy.datetime64('1789-12-31T00:00:00.000000000'), 1), (numpy.datetime64('1799-11-09T00:00:00.000000000'), 1))
- IndexNanosecondGO.level_add(level, *, index_constructor=None)
Return an IndexHierarchy with an added root level.
- Parameters:
level – A hashable to used as the new root.
* –
index_constructor –
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.level_add('A') <IndexHierarchyGO> A 1789-05-05T00:00:... A 1789-12-31T00:00:... A 1799-11-09T00:00:... <<U1> <datetime64[ns]>
- IndexNanosecondGO.loc_searchsorted(values, *, side_left=True, fill_value=nan)
Given a sorted
Series
, return the loc (label) position(s) at which insertion invalues
would retain sort order.- Parameters:
values – a single value, or iterable of values.
side_left – If True, the index of the first suitable location found is given, else return the last such index. If matching an existing value, side_left==True will return that position, side_left==Right will return the next position (or the length).
fill_value – A value to be used to fill the label beyond the last label.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.loc_searchsorted('c') ValueError('Error parsing datetime string "c" at position 0')
- IndexNanosecondGO.loc_to_iloc(key)
Given a label (loc) style key (either a label, a list of labels, a slice, or a Boolean selection), return the index position (iloc) style key. Keys that are not found will raise a KeyError or a sf.LocInvalid error.
- Parameters:
key – a label key.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.loc_to_iloc('d') ValueError('Error parsing datetime string "d" at position 0') >>> ix.loc_to_iloc(['a', 'e']) ValueError('Cannot create a NumPy datetime other than NaT with generic units') >>> ix.loc_to_iloc(slice('c', None)) ValueError('Error parsing datetime string "c" at position 0')
- IndexNanosecondGO.max(axis=0, skipna=True, out=None)
Return the maximum along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.max() 1799-11-09T00:00:00.000000000
- IndexNanosecondGO.mean(axis=0, skipna=True, out=None)
Return the mean along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.mean() UFuncTypeError(<ufunc 'add'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.median(axis=0, skipna=True, out=None)
Return the median along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.median() UFuncTypeError(<ufunc 'add'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.min(axis=0, skipna=True, out=None)
Return the minimum along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.min() 1789-05-05T00:00:00.000000000
- IndexNanosecondGO.notfalsy()
Return a same-shaped, Boolean
ndarray
indicating which values are falsy.>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix.notfalsy() [ True False True]
- IndexNanosecondGO.notna()
Return a same-shaped, Boolean
ndarray
indicating which values are NaN or None.>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix.notna() [ True False True]
- IndexNanosecondGO.prod(axis=0, skipna=True, allna=1, out=None)
Return the product along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.prod() UFuncTypeError(<ufunc 'multiply'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.relabel(mapper)
Return a new Index with labels replaced by the callable or mapping; order will be retained. If a mapping is used, the mapping need not map all origin keys.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.relabel(lambda l: l.astype('<M8[ms]').astype(object).day) <IndexNanosecondGO> 1970-01-01T00:00:00.000000005 1970-01-01T00:00:00.000000031 1970-01-01T00:00:00.000000009 <datetime64[ns]>
- IndexNanosecondGO.rename(name)
Return a new Frame with an updated name attribute.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.rename('y') <IndexNanosecondGO: y> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.roll(shift)
Return an Index with values rotated forward and wrapped around (with a postive shift) or backward and wrapped around (with a negative shift).
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.roll(2) <IndexNanosecondGO> 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 1789-05-05T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.sample(count=1, *, seed=None)
Randomly (optionally made deterministic with a fixed seed) extract items from the container to return a subset of the container.
- Parameters:
select. (Number of elements to) –
selection. (Initial state of random) –
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.sample(2, seed=0) <IndexNanosecondGO> 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.sort(ascending=True, kind='mergesort', key=None)
Return a new Index with the labels sorted.
- Parameters:
ascending – If True, sort in ascending order; if False, sort in descending order.
kind – Name of the sort algorithm as passed to NumPy.
key – A function that is used to pre-process the selected columns or rows and derive new values to sort by.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.sort() <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.sort(ascending=False) <IndexNanosecondGO> 1799-11-09T00:00:00.000000000 1789-12-31T00:00:00.000000000 1789-05-05T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.std(axis=0, skipna=True, ddof=0, out=None)
Return the standard deviaton along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.std() UFuncTypeError(<ufunc 'add'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.sum(axis=0, skipna=True, allna=0, out=None)
Sum values along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.sum() UFuncTypeError(<ufunc 'add'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
- IndexNanosecondGO.tail(count=5)
Return a
Index
consisting only of the bottom elements as specified bycount
.- Parameters:
count – Number of elements to be returned from the bottom of the
Index
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.tail(2) <IndexNanosecondGO> 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.union(*others)
Perform union with another Index, container, or NumPy array. Identical comparisons retain order.
>>> ix1 = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix1 <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix2 = sf.IndexNanosecondGO(('2022-04-01', '2021-12-31', '2022-06-30')) >>> ix2 <IndexNanosecondGO> 2022-04-01T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]> >>> ix1.union(ix2) <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 2021-12-31T00:00:00.000000000 2022-04-01T00:00:00.000000000 2022-06-30T00:00:00.000000000 <datetime64[ns]>
- IndexNanosecondGO.unique(depth_level=0, order_by_occurrence=False)
Return a NumPy array of unique values.
- Parameters:
depth_level – defaults to 0 for for a 1D Index.
order_by_occurrence – for 1D indices, this argument is a no-op. Provided for compatibility with IndexHierarchy.
- Returns:
numpy.ndarray
>>> ix = sf.IndexNanosecondGO(('1789-05-05', 'NaT', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 NaT 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.unique() ['1789-05-05T00:00:00.000000000' 'NaT' '1799-11-09T00:00:00.000000000']
- IndexNanosecondGO.values_at_depth(depth_level=0)
Return an NP array for the depth_level specified.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.values_at_depth(0) ['1789-05-05T00:00:00.000000000' '1789-12-31T00:00:00.000000000' '1799-11-09T00:00:00.000000000']
- IndexNanosecondGO.var(axis=0, skipna=True, ddof=0, out=None)
Return the variance along the specified axis.
- Parameters:
axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.
>>> ix = sf.IndexNanosecondGO(('1789-05-05', '1789-12-31', '1799-11-09')) >>> ix <IndexNanosecondGO> 1789-05-05T00:00:00.000000000 1789-12-31T00:00:00.000000000 1799-11-09T00:00:00.000000000 <datetime64[ns]> >>> ix.var() UFuncTypeError(<ufunc 'add'>, (dtype('<M8[ns]'), dtype('<M8[ns]')))
IndexNanosecondGO: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Iterator | Operator Binary | Operator Unary | Accessor Values | Accessor Datetime | Accessor String | Accessor Regular Expression | Accessor Hashlib | Accessor Type Clinic