Detail: FrameHE: Exporter
- FrameHE.to_arrow(*, include_index=True, include_index_name=True, include_columns=True, include_columns_name=False)
Return a
pyarrow.Tablefrom thisFrame.>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_arrow() pyarrow.Table __index0__: string a: int64 b: string ---- __index0__: [["p","q","r","s"]] a: [[10,2,8,3]] b: [["qrs ","XYZ","123"," wX "]]
- FrameHE.to_clipboard(*, delimiter='\t', include_index=True, include_index_name=True, include_columns=True, include_columns_name=False, encoding=None, line_terminator='\n', quote_char='"', quote_double=True, escape_char=None, quoting=0, store_filter=<static_frame.core.store_filter.StoreFilter object>)
Given a file path or file-like object, write the
Frameas delimited text. Thedelimiterdefaults to a tab.- Parameters:
path (A file) –
instance (PathLib) –
object. (or file-like) –
* –
delimiter – Character to be used for delimiterarating elements.
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index
name. Cannot be True ifinclude_columns_nameisTrue.include_columns – If
True, the columns will be written.include_columns_name – If including index, populate the column to the left of the columns with the columns
name. Cannot be True ifinclude_index_nameis True.encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the
delimiterorquote_char, or which contain new-line characters.quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When
True, the character is doubled. WhenFalse, theescape_charis used as a prefix to thequote_char. It defaults to True.escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A
StoreFilterinstance.
- FrameHE.to_csv(fp, *, include_index=True, include_index_name=True, include_columns=True, include_columns_name=False, encoding=None, line_terminator='\n', quoting=0, quote_char='"', quote_double=True, escape_char=None, store_filter=<static_frame.core.store_filter.StoreFilter object>)
Given a file path or file-like object, write the
Frameas delimited text. The delimiter is set to a comma.- Parameters:
path (A file) –
instance (PathLib) –
object. (or file-like) –
* –
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index
name. Cannot be True ifinclude_columns_nameisTrue.include_columns – If
True, the columns will be written.include_columns_name – If including index, populate the column to the left of the columns with the columns
name. Cannot be True ifinclude_index_nameis True.encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the
delimiterorquote_char, or which contain new-line characters.quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When
True, the character is doubled. WhenFalse, theescape_charis used as a prefix to thequote_char. It defaults to True.escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A
StoreFilterinstance.
>>> f1 = sf.FrameHE(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x') >>> f1 <FrameHE: x> <Index> a b <<U1> <Index> p 0 1 q 2 3 r 4 5 <<U1> <int64> <int64> >>> f1.to_csv('/tmp/f.csv') >>> from pathlib import Path >>> Path('/tmp/f.csv').read_text() __index0__,a,b p,0,1 q,2,3 r,4,5
- FrameHE.to_delimited(fp, *, delimiter, include_index=True, include_index_name=True, include_columns=True, include_columns_name=False, encoding=None, line_terminator='\n', quote_char='"', quote_double=True, escape_char=None, quoting=0, store_filter=<static_frame.core.store_filter.StoreFilter object>)
Given a file path or file-like object, write the
Frameas delimited text. Adelimitercharacter must be specified.- Parameters:
path (A file) –
instance (PathLib) –
object. (or file-like) –
* –
delimiter – Character to be used for delimiterarating elements.
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index
name. Cannot be True ifinclude_columns_nameisTrue.include_columns – If
True, the columns will be written.include_columns_name – If including index, populate the column to the left of the columns with the columns
name. Cannot be True ifinclude_index_nameis True.encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the
delimiterorquote_char, or which contain new-line characters.quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When
True, the character is doubled. WhenFalse, theescape_charis used as a prefix to thequote_char. It defaults to True.escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A
StoreFilterinstance.
>>> f1 = sf.FrameHE(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x') >>> f1 <FrameHE: x> <Index> a b <<U1> <Index> p 0 1 q 2 3 r 4 5 <<U1> <int64> <int64> >>> f1.to_delimited('/tmp/f.psv', delimiter='|') >>> from pathlib import Path >>> Path('/tmp/f.psv').read_text() __index0__|a|b p|0|1 q|2|3 r|4|5
- FrameHE.to_frame(*, name=<object object>)
Return
Frameinstance from thisFrame. If thisFrameis immutable the same instance will be returned.>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_frame() <Frame: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4>
- FrameHE.to_frame_go(*, name=<object object>)
Return a
FrameGOinstance from thisFrame.>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_frame_go() <FrameGO: x> <IndexGO> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4>
- FrameHE.to_frame_he(*, name=<object object>)
Return a
FrameHEinstance from thisFrame. If thisFrameis immutable the same instance will be returned.>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_frame_he() <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4>
- FrameHE.to_html(config=None, style_config=<static_frame.core.style_config.StyleConfig object>)
Return an HTML table representation of this
Frameusing standard TABLE, TR, and TD tags. This is not a complete HTML page.- Parameters:
config – Optional
DisplayConfiginstance.- Returns:
str
- FrameHE.to_html_datatables(fp=None, show=True, config=None)
Return a complete HTML representation of this
Frameusing the DataTables JS library for table naviagation and search. The page links to CDNs for JS resources, and thus will not fully render without an internet connection.- Parameters:
fp – optional file path to write; if not provided, a temporary file will be created. Note: the caller is responsible for deleting this file.
show – if True, the file will be opened with a webbrowser.
config – Optional
DisplayConfiginstance.
- Returns:
str, absolute file path to the file written.
- FrameHE.to_json_columns(indent=None)
Export a
Frameas a JSON string constructed as follows: A JSON object keyed by column labels, where values are columns represented by an object mapping of index labels to values.- Parameters:
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level.
>>> f = sf.FrameHE.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 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f.to_json_columns(indent=4) { "a": { "0": 10, "1": 2, "2": 8, "3": 3 }, "b": { "0": false, "1": true, "2": true, "3": false }, "c": { "0": "1517-01-01", "1": "1517-04-01", "2": "1517-12-31", "3": "1517-06-30" } }
- FrameHE.to_json_index(indent=None)
Export a
Frameas a JSON string constructed as follows: A JSON object keyed by index labels, where values are rows represented by an object mapping of column labels to values.- Parameters:
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level.
>>> f = sf.FrameHE.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 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f.to_json_index(indent=4) { "0": { "a": 10, "b": false, "c": "1517-01-01" }, "1": { "a": 2, "b": true, "c": "1517-04-01" }, "2": { "a": 8, "b": true, "c": "1517-12-31" }, "3": { "a": 3, "b": false, "c": "1517-06-30" } }
- FrameHE.to_json_records(indent=None)
Export a
Frameas a JSON string constructed as follows: A JSON array of row objects, where column labels are repeated for each row, and no index labels are included.- Parameters:
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level.
>>> f = sf.FrameHE.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 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f.to_json_records(indent=4) [ { "a": 10, "b": false, "c": "1517-01-01" }, { "a": 2, "b": true, "c": "1517-04-01" }, { "a": 8, "b": true, "c": "1517-12-31" }, { "a": 3, "b": false, "c": "1517-06-30" } ]
- FrameHE.to_json_split(indent=None)
Export a
Frameas a JSON string constructed as follows: A JSON object with a key for “columns”, “index”, and “data”, where data is given as an array of arrays of row values.- Parameters:
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level.
>>> f = sf.FrameHE.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 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f.to_json_split(indent=4) { "columns": [ "a", "b", "c" ], "index": [ 0, 1, 2, 3 ], "data": [ [ 10, false, "1517-01-01" ], [ 2, true, "1517-04-01" ], [ 8, true, "1517-12-31" ], [ 3, false, "1517-06-30" ] ] }
- FrameHE.to_json_typed(indent=None)
Export a
Frameas a JSON string constructed as follows: A JSON object with a key for “columns”, “index”, and “data”, where data is given as an array of arrays of column values; additionally, a key for “__meta__” defines an object with complete metadata and typing information.- Parameters:
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level.
>>> f = sf.FrameHE.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 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f.to_json_typed(indent=4) { "columns": [ "a", "b", "c" ], "index": null, "data": [ [ 10, 2, 8, 3 ], [ false, true, true, false ], [ "1517-01-01", "1517-04-01", "1517-12-31", "1517-06-30" ] ], "__meta__": { "__names__": [ "x", null, null ], "__dtypes__": [ "=i8", "|b1", "=M8[D]" ], "__dtypes_index__": [ "=i8" ], "__dtypes_columns__": [ "=U1" ], "__types__": [ "Index", "Index" ], "__depths__": [ 3, 1, 1 ] } }
- FrameHE.to_json_values(indent=None)
Export a
Frameas a JSON string constructed as follows: A JSON array of arrays of row values; no index or columns labels are included.- Parameters:
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level.
>>> f = sf.FrameHE.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 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f.to_json_values(indent=4) [ [ 10, false, "1517-01-01" ], [ 2, true, "1517-04-01" ], [ 8, true, "1517-12-31" ], [ 3, false, "1517-06-30" ] ]
- FrameHE.to_latex(config=None)
Display the Frame as a LaTeX formatted table.
>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_latex() \begin{table}[ht] \centering \begin{tabular}{c c c} \hline\hline & a & b \\ \hline p & 10 & qrs \\ q & 2 & XYZ \\ r & 8 & 123 \\ s & 3 & wX \\ \hline\end{tabular} \end{table}
- FrameHE.to_markdown(config=None)
Display the Frame as a Markdown formatted table.
>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_markdown() | |a |b | |--|---|----| |p |10 |qrs | |q |2 |XYZ | |r |8 |123 | |s |3 | wX |
- FrameHE.to_npy(fp, *, include_index=True, include_columns=True, consolidate_blocks=False)
Write a
Frameas a directory of npy file.>>> f1 = sf.FrameHE.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') >>> f1 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f1.to_npy('/tmp/f.npy') >>> sf.Frame.from_npy('/tmp/f.npy') <Frame: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> import shutil >>> shutil.rmtree('/tmp/f.npy')
- FrameHE.to_npz(fp, *, include_index=True, include_columns=True, consolidate_blocks=False)
Write a
Frameas an npz file.>>> f1 = sf.FrameHE.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') >>> f1 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f1.to_npz('/tmp/f.npz') >>> sf.Frame.from_npz('/tmp/f.npz') <Frame: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]>
- FrameHE.to_pairs(axis=0)
Return a tuple of major axis key, minor axis key vlaue pairs, where major axis is determined by the axis argument. Note that the returned object is eagerly constructed; use an iterator interface for lazy iteration.
>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_pairs() ((np.str_('a'), ((np.str_('p'), np.int64(10)), (np.str_('q'), np.int64(2)), (np.str_('r'), np.int64(8)), (np.str_('s'), np.int64(3)))), (np.str_('b'), ((np.str_('p'), np.str_('qrs ')), (np.str_('q'), np.str_('XYZ')), (np.str_('r'), np.str_('123')), (np.str_('s'), np.str_(' wX ')))))
- FrameHE.to_pandas()
Return a Pandas DataFrame.
>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_pandas() a b p 10 qrs q 2 XYZ r 8 123 s 3 wX
- FrameHE.to_parquet(fp, *, include_index=True, include_index_name=True, include_columns=True, include_columns_name=False)
Write an Arrow Parquet binary file.
>>> f1 = sf.FrameHE(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x') >>> f1 <FrameHE: x> <Index> a b <<U1> <Index> p 0 1 q 2 3 r 4 5 <<U1> <int64> <int64> >>> f1.to_parquet('/tmp/f.parquet')
- FrameHE.to_pickle(fp, *, protocol=None)
Write a
Frameas a Python pickle.The pickle module is not secure. Only unpickle data you trust.
- Parameters:
fp – file path to write.
protocol – Pickle protocol to use.
>>> f1 = sf.FrameHE.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') >>> f1 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f1.to_pickle('/tmp/f.pickle') >>> sf.Frame.from_pickle('/tmp/f.pickle') <Frame: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]>
- FrameHE.to_rst(config=None)
Display the Frame as an RST formatted table.
>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_rst() +--+---+----+ | |a |b | +==+===+====+ |p |10 |qrs | +--+---+----+ |q |2 |XYZ | +--+---+----+ |r |8 |123 | +--+---+----+ |s |3 | wX | +--+---+----+
- FrameHE.to_series(*, index_constructor=<class 'static_frame.core.index.Index'>, name=<object object>)
Return a
Seriesrepresentation of thisFrame, where the index is extended with columns to from tuple labels for each element in theFrame.- Parameters:
index_constructor – Index constructor of the tuples produced by combining index and columns into one label. Providing
IndexHierarchy.from_labelswill produce a hierarchical index.
>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_series() <Series: x> <Index> (np.str_('p'), np.str_('a')) 10 (np.str_('p'), np.str_('b')) qrs (np.str_('q'), np.str_('a')) 2 (np.str_('q'), np.str_('b')) XYZ (np.str_('r'), np.str_('a')) 8 (np.str_('r'), np.str_('b')) 123 (np.str_('s'), np.str_('a')) 3 (np.str_('s'), np.str_('b')) wX <object> <object>
- FrameHE.to_sql(connection, /, *, label=<object object>, include_index=True, schema='', placeholder='', dtype_to_type_decl=None)
Write Frame to the database provided by connection. Connections to SQLite, PostgreSQL, MySQL, and MariaDB are fully supported. The table name can be provided by label, otherwise Frame.name will be used. If the target table does not exist, it will be created using optimal mappings to NumPy dtypes. If the target table exists, records will be appended. Parameterized insert queries are always used. Records will never be deleted, nor tables dropped.
If using SQLAlchemy, pass the underlying DBAPI connection object (via the sqlalchemy.engine.Connection.connection attribute) as the conection.
- Parameters:
label – Provide a name for the table; if not provided, Frame.name will be used if not None, else an exception will be raised.
include_index – If True, the index will be included.
schema – If provided, this string will be used as a database schema label to prefix the table name in all SQL queries.
placeholder – String used as a placeholder in parameterized insert queries. Correct defaults are provided for SQLite, PostgreSQL, MySQL, and MariaDB.
dtype_to_type_decl – Mapping from NumPy dtype to a string to be used in type declaration when creating tables. Sensible defaults are provided for SQLite, PostgreSQL, MySQL, and MariaDB.
>>> import sqlite3 >>> conn = sqlite3.connect(':memory') >>> f1 = sf.FrameHE.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') >>> f1.to_sql(conn, include_index=False) >>> sf.Frame.from_sql("select * from x", connection=conn) <Frame> <Index> a b c <<U1> <Index> 0 10 0 1517-01-01 1 2 1 1517-04-01 2 8 1 1517-12-31 3 3 0 1517-06-30 <int64> <int64> <int64> <<U10>
- FrameHE.to_sqlite(fp, *, label=<object object>, include_index=True, include_columns=True)
Write the Frame as single-table SQLite file.
>>> f1 = sf.FrameHE.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') >>> f1 <FrameHE: x> <Index> a b c <<U1> <Index> 0 10 False 1517-01-01 1 2 True 1517-04-01 2 8 True 1517-12-31 3 3 False 1517-06-30 <int64> <int64> <bool> <datetime64[D]> >>> f1.to_sqlite('/tmp/f.db') >>> import sqlite3 >>> conn = sqlite3.connect('/tmp/f.db') >>> sf.Frame.from_sql("select * from x limit 2", connection=conn, index_depth=1) <Frame> <Index> a b c <<U1> <Index> 0 10 0 1517-01-01 1 2 1 1517-04-01 <int64> <int64> <int64> <<U10>
- FrameHE.to_tsv(fp, *, include_index=True, include_index_name=True, include_columns=True, include_columns_name=False, encoding=None, line_terminator='\n', quote_char='"', quote_double=True, escape_char=None, quoting=0, store_filter=<static_frame.core.store_filter.StoreFilter object>)
Given a file path or file-like object, write the
Frameas delimited text. The delimiter is set to a tab.- Parameters:
path (A file) –
instance (PathLib) –
object. (or file-like) –
* –
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index
name. Cannot be True ifinclude_columns_nameisTrue.include_columns – If
True, the columns will be written.include_columns_name – If including index, populate the column to the left of the columns with the columns
name. Cannot be True ifinclude_index_nameis True.encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the
delimiterorquote_char, or which contain new-line characters.quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When
True, the character is doubled. WhenFalse, theescape_charis used as a prefix to thequote_char. It defaults to True.escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A
StoreFilterinstance.
>>> f1 = sf.FrameHE(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x') >>> f1 <FrameHE: x> <Index> a b <<U1> <Index> p 0 1 q 2 3 r 4 5 <<U1> <int64> <int64> >>> f1.to_tsv('/tmp/f.tsv') >>> from pathlib import Path >>> Path('/tmp/f.tsv').read_text() __index0__ a b p 0 1 q 2 3 r 4 5
- FrameHE.to_visidata()
Open an interactive VisiData session.
- FrameHE.to_xarray()
Return an xarray Dataset.
In order to preserve columnar types, and following the precedent of Pandas, the
Frame, with a 1D index, is translated as a Dataset of 1D arrays, where each DataArray is a 1D array. If the index is anIndexHierarchy, each column is mapped into an ND array of shape equal to the unique values found at each depth of the index.>>> f = sf.FrameHE.from_fields(((10, 2, 8, 3), ('qrs ', 'XYZ', '123', ' wX ')), columns=('a', 'b'), index=('p', 'q', 'r', 's'), name='x') >>> f <FrameHE: x> <Index> a b <<U1> <Index> p 10 qrs q 2 XYZ r 8 123 s 3 wX <<U1> <int64> <<U4> >>> f.to_xarray() <xarray.Dataset> Size: 112B Dimensions: (__index0__: 4) Coordinates: * __index0__ (__index0__) <U1 16B 'p' 'q' 'r' 's' Data variables: a (__index0__) int64 32B 10 2 8 3 b (__index0__) <U4 64B 'qrs ' 'XYZ' '123' ' wX '
- FrameHE.to_xlsx(fp, *, label=<object object>, include_index=True, include_index_name=True, include_columns=True, include_columns_name=False, merge_hierarchical_labels=True, store_filter=<static_frame.core.store_filter.StoreFilter object>)
Write the Frame as single-sheet XLSX file.
>>> f1 = sf.FrameHE(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x') >>> f1 <FrameHE: x> <Index> a b <<U1> <Index> p 0 1 q 2 3 r 4 5 <<U1> <int64> <int64> >>> f1.to_xlsx('/tmp/f.xlsx')
FrameHE: 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