Detail: Frame: Accessor Reduce
Overview: Frame: Accessor Reduce
- Frame.reduce.from_func(func, *, fill_value).keys()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_func(func, *, fill_value=nan)
For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_func(lambda f: f.iloc[1:]).keys()) ('x',)
- Frame.reduce.from_func(func, *, fill_value).__iter__()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_func(func, *, fill_value=nan)
For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_func(lambda f: f.iloc[1:]).__iter__()) ('x',)
- Frame.reduce.from_func(func, *, fill_value).items()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_func(func, *, fill_value=nan)
For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_func(lambda f: f.iloc[1:]).items()) (('x', <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]>),)
- Frame.reduce.from_func(func, *, fill_value).values()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_func(func, *, fill_value=nan)
For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_func(lambda f: f.iloc[1:]).values()) (<Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]>,)
- Frame.reduce.from_func(func, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_func(func, *, fill_value=nan)
For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> f.reduce.from_func(lambda f: f.iloc[1:]).to_frame() <Frame> <Index> a b c <<U1> <IndexHierarchy> 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]>
- Frame.reduce.from_map_func(func, *, fill_value).keys()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_map_func(func, *, fill_value=nan)[source]
For each Frame, reduce by applying, for each column, a function that reduces to (0-dimensional) elements, where the column label and function are given as a mapping. Column labels are retained.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_map_func(np.min).keys()) ('x',)
- Frame.reduce.from_map_func(func, *, fill_value).__iter__()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_map_func(func, *, fill_value=nan)[source]
For each Frame, reduce by applying, for each column, a function that reduces to (0-dimensional) elements, where the column label and function are given as a mapping. Column labels are retained.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_map_func(np.min).__iter__()) ('x',)
- Frame.reduce.from_map_func(func, *, fill_value).items()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_map_func(func, *, fill_value=nan)[source]
For each Frame, reduce by applying, for each column, a function that reduces to (0-dimensional) elements, where the column label and function are given as a mapping. Column labels are retained.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_map_func(np.min).items()) (('x', <Series: x> <Index> a 2 b False c 1517-01-01 <<U1> <object>),)
- Frame.reduce.from_map_func(func, *, fill_value).values()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_map_func(func, *, fill_value=nan)[source]
For each Frame, reduce by applying, for each column, a function that reduces to (0-dimensional) elements, where the column label and function are given as a mapping. Column labels are retained.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_map_func(np.min).values()) (<Series: x> <Index> a 2 b False c 1517-01-01 <<U1> <object>,)
- Frame.reduce.from_map_func(func, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_map_func(func, *, fill_value=nan)[source]
For each Frame, reduce by applying, for each column, a function that reduces to (0-dimensional) elements, where the column label and function are given as a mapping. Column labels are retained.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> f.reduce.from_map_func(np.min).to_frame() <Frame> <Index> a b c <<U1> <Index> x 2 False 1517-01-01 <<U1> <int64> <bool> <datetime64[D]>
- Frame.reduce.from_label_map(func_map, *, fill_value).keys()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to each column, where the column label and function are given as a mapping. Column labels are retained.
- Parameters:
func_map – a mapping of column labels to functions.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_map({'b': np.min, 'a': np.max}).keys()) ('x',)
- Frame.reduce.from_label_map(func_map, *, fill_value).__iter__()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to each column, where the column label and function are given as a mapping. Column labels are retained.
- Parameters:
func_map – a mapping of column labels to functions.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_map({'b': np.min, 'a': np.max}).__iter__()) ('x',)
- Frame.reduce.from_label_map(func_map, *, fill_value).items()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to each column, where the column label and function are given as a mapping. Column labels are retained.
- Parameters:
func_map – a mapping of column labels to functions.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_map({'b': np.min, 'a': np.max}).items()) (('x', <Series: x> <Index> b False a 10 <<U1> <object>),)
- Frame.reduce.from_label_map(func_map, *, fill_value).values()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to each column, where the column label and function are given as a mapping. Column labels are retained.
- Parameters:
func_map – a mapping of column labels to functions.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_map({'b': np.min, 'a': np.max}).values()) (<Series: x> <Index> b False a 10 <<U1> <object>,)
- Frame.reduce.from_label_map(func_map, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to each column, where the column label and function are given as a mapping. Column labels are retained.
- Parameters:
func_map – a mapping of column labels to functions.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> f.reduce.from_label_map({'b': np.min, 'a': np.max}).to_frame() <Frame> <Index> b a <<U1> <Index> x False 10 <<U1> <bool> <int64>
- Frame.reduce.from_label_pair_map(func_map, *, fill_value).keys()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_pair_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to a column and assigning the result a new label. Functions are provided as values in a mapping, where the key is tuple of source label, destination label.
- Parameters:
func_map – a mapping of pairs of source label, destination label, to a function.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).keys()) ('x',)
- Frame.reduce.from_label_pair_map(func_map, *, fill_value).__iter__()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_pair_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to a column and assigning the result a new label. Functions are provided as values in a mapping, where the key is tuple of source label, destination label.
- Parameters:
func_map – a mapping of pairs of source label, destination label, to a function.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).__iter__()) ('x',)
- Frame.reduce.from_label_pair_map(func_map, *, fill_value).items()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_pair_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to a column and assigning the result a new label. Functions are provided as values in a mapping, where the key is tuple of source label, destination label.
- Parameters:
func_map – a mapping of pairs of source label, destination label, to a function.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).items()) (('x', <Series: x> <Index> b-min False b-max True <<U5> <bool>),)
- Frame.reduce.from_label_pair_map(func_map, *, fill_value).values()
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_pair_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to a column and assigning the result a new label. Functions are provided as values in a mapping, where the key is tuple of source label, destination label.
- Parameters:
func_map – a mapping of pairs of source label, destination label, to a function.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> tuple(f.reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).values()) (<Series: x> <Index> b-min False b-max True <<U5> <bool>,)
- Frame.reduce.from_label_pair_map(func_map, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)
- Frame.reduce
Return a
ReduceAlignedinterface, permitting function application per column or on entire containers.
- ReduceDispatchAligned.from_label_pair_map(func_map, *, fill_value=nan)[source]
For Frame, reduce by applying a function to a column and assigning the result a new label. Functions are provided as values in a mapping, where the key is tuple of source label, destination label.
- Parameters:
func_map – a mapping of pairs of source label, destination label, to a function.
>>> f = sf.Frame.from_fields(((10, 2, 8, 3), (False, True, True, False), ('1517-01-01', '1517-04-01', '1517-12-31', '1517-06-30')), index=sf.IndexHierarchy.from_product((0, 1), ('p', 'q')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x') >>> f <Frame: x> <Index> a b c <<U1> <IndexHierarchy> 0 p 10 False 1517-01-01 0 q 2 True 1517-04-01 1 p 8 True 1517-12-31 1 q 3 False 1517-06-30 <int64> <<U1> <int64> <bool> <datetime64[D]> >>> f.reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).to_frame() <Frame> <Index> b-min b-max <<U5> <Index> x False True <<U1> <bool> <bool>
Frame: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Assignment | Selector | Iterator | Operator Binary | Operator Unary | Accessor Values | Accessor Datetime | Accessor String | Accessor Transpose | Accessor Fill Value | Accessor Regular Expression | Accessor Hashlib | Accessor Type Clinic | Accessor Reduce