Detail: Frame: Attribute#

Frame.STATIC = True#

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.STATIC
True

Frame.T#

Transpose. Return a Frame with index as columns and vice versa.

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.T
<Frame: x>
<Index>    0          1          2          3          <int64>
<Index>
a          10         2          8          3
b          False      True       True       False
c          1517-01-01 1517-04-01 1517-12-31 1517-06-30
<<U1>      <object>   <object>   <object>   <object>

Frame.columns#

The IndexBase instance assigned for column labels.

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.columns
<Index>
a
b
c
<<U1>

Frame.dtypes#

Return a Series of dytpes for each realizable column.

Returns:: static_frame.Series

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.dtypes
<Series: x>
<Index>
a           int64
b           bool
c           datetime64[D]
<<U1>       <object>

Frame.index#

The IndexBase instance assigned for row labels.

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.index
<Index>
0
1
2
3
<int64>

Frame.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

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.memory
        L    Lu    LM   LMu   LMD  LMDu  R     Ru    RM   RMu   RMD  RMDu
Name    42   B     42   B     42   B     42    B     42   B     42   B
Index   292  B     308  B     180  B     8.39  KB    340  B     212  B
Columns 856  B     888  B     632  B     8.95  KB    912  B     656  B
Blocks  748  B     796  B     412  B     748   B     796  B     412  B
Total   1.93 KB    2.02 KB    1.27 KB    10.04 KB    2.08 KB    1.33 KB

Frame.mloc#: The memory locations, represented as an array of integers, of the underlying NumPy arrays.

Frame.name#

A hashable label attached to this container.

Returns:: Hashable

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.name
x

Frame.nbytes#

Return the total bytes of the underlying NumPy array.

Returns:: int

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.nbytes
68

Frame.ndim#

Return the number of dimensions, which for a Frame is always 2.

Returns:: int

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.ndim
2

Frame.shape#

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

Returns:: tp.Tuple[int, int]

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.shape
(4, 3)

Frame.size#

Return the size of the underlying NumPy array.

Returns:: int

>>> 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')), columns=('a', 'b', 'c'), dtypes=dict(c=np.datetime64), name='x')
>>> f.size
12