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_write_utils.py
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_write_utils.py
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from __future__ import annotations
from collections.abc import Sequence
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Any, overload
from polars import functions as F
from polars.datatypes import (
Date,
Datetime,
Float64,
Int64,
List,
Object,
Struct,
Time,
)
from polars.datatypes.group import FLOAT_DTYPES, INTEGER_DTYPES
from polars.dependencies import json
from polars.exceptions import DuplicateError
from polars.selectors import _expand_selector_dicts, _expand_selectors, numeric
if TYPE_CHECKING:
from collections.abc import Iterable
from typing import Literal
from xlsxwriter import Workbook
from xlsxwriter.format import Format
from xlsxwriter.worksheet import Worksheet
from polars import DataFrame, Schema, Series
from polars._typing import (
ColumnFormatDict,
ColumnTotalsDefinition,
ConditionalFormatDict,
OneOrMoreDataTypes,
PolarsDataType,
RowTotalsDefinition,
)
from polars.expr import Expr
def _cluster(iterable: Iterable[Any], n: int = 2) -> Iterable[Any]:
return zip(*[iter(iterable)] * n)
_XL_DEFAULT_FLOAT_FORMAT_ = "#,##0.000;[Red]-#,##0.000"
_XL_DEFAULT_INTEGER_FORMAT_ = "#,##0;[Red]-#,##0"
_XL_DEFAULT_DTYPE_FORMATS_: dict[PolarsDataType, str] = {
Datetime: "yyyy-mm-dd hh:mm:ss",
Date: "yyyy-mm-dd;@",
Time: "hh:mm:ss;@",
}
class _XLFormatCache:
"""Create/cache only one Format object per distinct set of format options."""
def __init__(self, wb: Workbook) -> None:
self._cache: dict[str, Format] = {}
self.wb = wb
@staticmethod
def _key(fmt: dict[str, Any]) -> str:
return json.dumps(fmt, sort_keys=True, default=str)
def get(self, fmt: dict[str, Any] | Format) -> Format:
if not isinstance(fmt, dict):
wbfmt = fmt
else:
key = self._key(fmt)
wbfmt = self._cache.get(key)
if wbfmt is None:
wbfmt = self.wb.add_format(fmt)
self._cache[key] = wbfmt
return wbfmt
def _adjacent_cols(df: DataFrame, cols: Iterable[str], min_max: dict[str, Any]) -> bool:
"""Indicate if the given columns are all adjacent to one another."""
idxs = sorted(df.get_column_index(col) for col in cols)
if idxs != sorted(range(min(idxs), max(idxs) + 1)):
return False
else:
columns = df.columns
min_max["min"] = {"idx": idxs[0], "name": columns[idxs[0]]}
min_max["max"] = {"idx": idxs[-1], "name": columns[idxs[-1]]}
return True
def _all_integer_cols(cols: Iterable[str], schema: Schema) -> bool:
"""Indicate if the given columns are all integer-typed."""
return all(schema[col].is_integer() for col in cols)
def _unpack_multi_column_dict(
d: dict[str | Sequence[str], Any] | Any,
) -> dict[str, Any] | Any:
"""Unpack multi-col dictionary into equivalent single-col definitions."""
if not isinstance(d, dict):
return d
unpacked: dict[str, Any] = {}
for key, value in d.items():
if isinstance(key, str) or not isinstance(key, Sequence):
key = (key,)
for k in key:
unpacked[k] = value
return unpacked
def _xl_apply_conditional_formats(
df: DataFrame,
ws: Worksheet,
*,
conditional_formats: ConditionalFormatDict,
table_start: tuple[int, int],
include_header: bool,
format_cache: _XLFormatCache,
) -> None:
"""Take all conditional formatting options and apply them to the table/range."""
from xlsxwriter.format import Format
for cols, formats in _expand_selector_dicts(
df, conditional_formats, expand_keys=True, expand_values=False, tuple_keys=True
).items():
if not isinstance(cols, str) and len(cols) == 1:
cols = next(iter(cols))
if isinstance(formats, (str, dict)):
formats = [formats]
for fmt in formats:
if not isinstance(fmt, dict):
fmt = {"type": fmt}
if isinstance(cols, str):
col_range = _xl_column_range(
df, table_start, cols, include_header=include_header
)
else:
col_range = _xl_column_multi_range(
df, table_start, cols, include_header=include_header
)
if " " in col_range:
col = next(iter(cols))
fmt["multi_range"] = col_range
col_range = _xl_column_range(
df, table_start, col, include_header=include_header
)
if "format" in fmt:
f = fmt["format"]
fmt["format"] = (
f # already registered
if isinstance(f, Format)
else format_cache.get(
{"num_format": f} if isinstance(f, str) else f
)
)
ws.conditional_format(col_range, fmt)
@overload
def _xl_column_range(
df: DataFrame,
table_start: tuple[int, int],
col: str | tuple[int, int],
*,
include_header: bool,
as_range: Literal[True] = ...,
) -> str: ...
@overload
def _xl_column_range(
df: DataFrame,
table_start: tuple[int, int],
col: str | tuple[int, int],
*,
include_header: bool,
as_range: Literal[False],
) -> tuple[int, int, int, int]: ...
def _xl_column_range(
df: DataFrame,
table_start: tuple[int, int],
col: str | tuple[int, int],
*,
include_header: bool,
as_range: bool = True,
) -> tuple[int, int, int, int] | str:
"""Return the excel sheet range of a named column, accounting for all offsets."""
col_start = (
table_start[0] + int(include_header),
table_start[1] + (df.get_column_index(col) if isinstance(col, str) else col[0]),
)
col_finish = (
col_start[0] + len(df) - 1,
col_start[1] + (0 if isinstance(col, str) else (col[1] - col[0])),
)
if as_range:
return "".join(_xl_rowcols_to_range(*col_start, *col_finish))
else:
return col_start + col_finish
def _xl_column_multi_range(
df: DataFrame,
table_start: tuple[int, int],
cols: Iterable[str],
*,
include_header: bool,
) -> str:
"""Return column ranges as an xlsxwriter 'multi_range' string, or spanning range."""
m: dict[str, Any] = {}
if _adjacent_cols(df, cols, min_max=m):
return _xl_column_range(
df,
table_start,
(m["min"]["idx"], m["max"]["idx"]),
include_header=include_header,
)
return " ".join(
_xl_column_range(df, table_start, col, include_header=include_header)
for col in cols
)
def _xl_inject_dummy_table_columns(
df: DataFrame,
coldefs: dict[str, Any],
*,
dtype: dict[str, PolarsDataType] | PolarsDataType | None = None,
expr: Expr | None = None,
) -> DataFrame:
"""Insert dummy frame columns in order to create empty/named table columns."""
df_original_columns = set(df.columns)
df_select_cols = df.columns.copy()
cast_lookup = {}
for col, definition in coldefs.items():
if col in df_original_columns:
msg = f"cannot create a second {col!r} column"
raise DuplicateError(msg)
elif not isinstance(definition, dict):
df_select_cols.append(col)
else:
cast_lookup[col] = definition.get("return_dtype")
insert_before = definition.get("insert_before")
insert_after = definition.get("insert_after")
if insert_after is None and insert_before is None:
df_select_cols.append(col)
else:
insert_idx = (
df_select_cols.index(insert_after) + 1 # type: ignore[arg-type]
if insert_before is None
else df_select_cols.index(insert_before)
)
df_select_cols.insert(insert_idx, col)
expr = F.lit(None) if expr is None else expr
df = df.select(
(
col
if col in df_original_columns
else (
expr.cast(
cast_lookup.get( # type:ignore[arg-type]
col,
dtype.get(col, Float64) if isinstance(dtype, dict) else dtype,
)
)
if dtype or (cast_lookup.get(col) is not None)
else expr
).alias(col)
)
for col in df_select_cols
)
return df
def _xl_inject_sparklines(
ws: Worksheet,
df: DataFrame,
table_start: tuple[int, int],
col: str,
*,
include_header: bool,
params: Sequence[str] | dict[str, Any],
) -> None:
"""Inject sparklines into (previously-created) empty table columns."""
from xlsxwriter.utility import xl_rowcol_to_cell
m: dict[str, Any] = {}
data_cols = params.get("columns") if isinstance(params, dict) else params
if not data_cols:
msg = "supplying 'columns' param value is mandatory for sparklines"
raise ValueError(msg)
elif not _adjacent_cols(df, data_cols, min_max=m):
msg = "sparkline data range/cols must all be adjacent"
raise RuntimeError(msg)
spk_row, spk_col, _, _ = _xl_column_range(
df, table_start, col, include_header=include_header, as_range=False
)
data_start_col = table_start[1] + m["min"]["idx"]
data_end_col = table_start[1] + m["max"]["idx"]
if not isinstance(params, dict):
options = {}
else:
# strip polars-specific params before passing to xlsxwriter
options = {
name: val
for name, val in params.items()
if name not in ("columns", "insert_after", "insert_before")
}
if "negative_points" not in options:
options["negative_points"] = options.get("type") in ("column", "win_loss")
for _ in range(len(df)):
data_start = xl_rowcol_to_cell(spk_row, data_start_col)
data_end = xl_rowcol_to_cell(spk_row, data_end_col)
options["range"] = f"{data_start}:{data_end}"
ws.add_sparkline(spk_row, spk_col, options)
spk_row += 1
def _xl_rowcols_to_range(*row_col_pairs: int) -> list[str]:
"""Return list of "A1:B2" range refs from pairs of row/col indexes."""
from xlsxwriter.utility import xl_rowcol_to_cell
cell_refs = (xl_rowcol_to_cell(row, col) for row, col in _cluster(row_col_pairs))
return [f"{cell_start}:{cell_end}" for cell_start, cell_end in _cluster(cell_refs)]
def _xl_setup_table_columns(
df: DataFrame,
format_cache: _XLFormatCache,
column_totals: ColumnTotalsDefinition | None = None,
column_formats: ColumnFormatDict | None = None,
dtype_formats: dict[OneOrMoreDataTypes, str] | None = None,
header_format: dict[str, Any] | None = None,
sparklines: dict[str, Sequence[str] | dict[str, Any]] | None = None,
formulas: dict[str, str | dict[str, str]] | None = None,
row_totals: RowTotalsDefinition | None = None,
float_precision: int = 3,
table_style: dict[str, Any] | str | None = None,
) -> tuple[list[dict[str, Any]], dict[str | tuple[str, ...], str], DataFrame]:
"""Setup and unify all column-related formatting/defaults."""
# no excel support for compound types; cast to their simple string representation
def _map_str(s: Series) -> Series:
return s.__class__(s.name, [str(v) for v in s.to_list()])
cast_cols = [
F.col(col).map_batches(_map_str).alias(col)
for col, tp in df.schema.items()
if tp in (List, Struct, Object)
]
if cast_cols:
df = df.with_columns(cast_cols)
# expand/normalise column formats
column_formats = _unpack_multi_column_dict( # type: ignore[assignment]
_expand_selector_dicts(
df, column_formats, expand_keys=True, expand_values=False, tuple_keys=True
)
)
# normalise row totals
if not row_totals:
row_totals_dtype = None
row_total_funcs = {}
else:
schema = df.schema
numeric_cols = {col for col, tp in schema.items() if tp.is_numeric()}
if not isinstance(row_totals, dict):
row_totals_dtype = (
Int64 if _all_integer_cols(numeric_cols, schema) else Float64
)
sum_cols = (
numeric_cols
if row_totals is True
else (
{row_totals}
if isinstance(row_totals, str)
else set(_expand_selectors(df, row_totals))
)
)
n_ucase = sum((c[0] if c else "").isupper() for c in df.columns)
total = f"{'T' if (n_ucase > len(df.columns) // 2) else 't'}otal"
row_total_funcs = {total: _xl_table_formula(df, sum_cols, "sum")}
row_totals = [total]
else:
row_totals = _expand_selector_dicts(
df, row_totals, expand_keys=False, expand_values=True
)
row_totals_dtype = { # type: ignore[assignment]
nm: (
Int64
if _all_integer_cols(numeric_cols if cols is True else cols, schema)
else Float64
)
for nm, cols in row_totals.items()
}
row_total_funcs = {
name: _xl_table_formula(
df, (numeric_cols if cols is True else cols), "sum"
)
for name, cols in row_totals.items()
}
# expand/normalise column totals
if column_totals is True:
column_totals = {numeric(): "sum", **dict.fromkeys(row_totals or (), "sum")}
elif isinstance(column_totals, str):
fn = column_totals.lower()
column_totals = {numeric(): fn, **dict.fromkeys(row_totals or (), fn)}
column_totals = _unpack_multi_column_dict( # type: ignore[assignment]
_expand_selector_dicts(df, column_totals, expand_keys=True, expand_values=False)
if isinstance(column_totals, dict)
else _expand_selectors(df, column_totals)
)
column_total_funcs = (
dict.fromkeys(column_totals, "sum")
if isinstance(column_totals, Sequence)
else (column_totals.copy() if isinstance(column_totals, dict) else {})
)
# normalise formulas
column_formulas = {
col: {"formula": options} if isinstance(options, str) else options
for col, options in (formulas or {}).items()
}
# normalise formats
column_formats = dict(column_formats or {})
dtype_formats = dict(dtype_formats or {})
for tp in list(dtype_formats):
if isinstance(tp, (tuple, frozenset)):
dtype_formats.update(dict.fromkeys(tp, dtype_formats.pop(tp)))
for fmt in dtype_formats.values():
if not isinstance(fmt, str):
msg = f"invalid dtype_format value: {fmt!r} (expected format string, got {type(fmt).__name__!r})"
raise TypeError(msg)
# inject sparkline/row-total placeholder(s)
if sparklines:
df = _xl_inject_dummy_table_columns(df, sparklines)
if column_formulas:
df = _xl_inject_dummy_table_columns(df, column_formulas)
if row_totals:
df = _xl_inject_dummy_table_columns(df, row_total_funcs, dtype=row_totals_dtype)
# seed format cache with default fallback format
fmt_default = format_cache.get({"valign": "vcenter"})
if table_style is None:
# no table style; apply default black (+ve) & red (-ve) numeric formatting
int_base_fmt = _XL_DEFAULT_INTEGER_FORMAT_
flt_base_fmt = _XL_DEFAULT_FLOAT_FORMAT_
else:
# if we have a table style, defer the colours to that style
int_base_fmt = _XL_DEFAULT_INTEGER_FORMAT_.split(";", 1)[0]
flt_base_fmt = _XL_DEFAULT_FLOAT_FORMAT_.split(";", 1)[0]
for tp in INTEGER_DTYPES:
_XL_DEFAULT_DTYPE_FORMATS_[tp] = int_base_fmt
zeros = "0" * float_precision
fmt_float = int_base_fmt if not zeros else flt_base_fmt.replace(".000", f".{zeros}")
# assign default dtype formats
for tp, fmt in _XL_DEFAULT_DTYPE_FORMATS_.items():
dtype_formats.setdefault(tp, fmt)
for tp in FLOAT_DTYPES:
dtype_formats.setdefault(tp, fmt_float)
for tp, fmt in dtype_formats.items():
dtype_formats[tp] = fmt
# associate formats/functions with specific columns
for col, tp in df.schema.items():
base_type = tp.base_type()
if base_type in dtype_formats:
fmt = dtype_formats.get(tp, dtype_formats[base_type])
column_formats.setdefault(col, fmt)
if col not in column_formats:
column_formats[col] = fmt_default
# ensure externally supplied formats are made available
for col, fmt in column_formats.items(): # type: ignore[assignment]
if isinstance(fmt, str):
column_formats[col] = format_cache.get(
{"num_format": fmt, "valign": "vcenter"}
)
elif isinstance(fmt, dict):
if "num_format" not in fmt:
tp = df.schema.get(col)
if tp in dtype_formats:
fmt["num_format"] = dtype_formats[tp]
if "valign" not in fmt:
fmt["valign"] = "vcenter"
column_formats[col] = format_cache.get(fmt)
# optional custom header format
col_header_format = format_cache.get(header_format) if header_format else None
# assemble table columns
table_columns = [
{
k: v
for k, v in {
"header": col,
"format": column_formats[col],
"header_format": col_header_format,
"total_function": column_total_funcs.get(col),
"formula": (
row_total_funcs.get(col)
or column_formulas.get(col, {}).get("formula")
),
}.items()
if v is not None
}
for col in df.columns
]
return table_columns, column_formats, df # type: ignore[return-value]
def _xl_setup_table_options(
table_style: dict[str, Any] | str | None,
) -> tuple[dict[str, Any] | str | None, dict[str, Any]]:
"""Setup table options, distinguishing style name from other formatting."""
if isinstance(table_style, dict):
valid_options = (
"style",
"banded_columns",
"banded_rows",
"first_column",
"last_column",
)
for key in table_style:
if key not in valid_options:
msg = f"invalid table style key: {key!r}"
raise ValueError(msg)
table_options = table_style.copy()
table_style = table_options.pop("style", None)
else:
table_options = {}
return table_style, table_options
def _xl_worksheet_in_workbook(
wb: Workbook, ws: Worksheet, *, return_worksheet: bool = False
) -> bool | Worksheet:
if any(ws is sheet for sheet in wb.worksheets()):
return ws if return_worksheet else True
msg = f"the given workbook object {wb.filename!r} is not the parent of worksheet {ws.name!r}"
raise ValueError(msg)
def _xl_setup_workbook(
workbook: Workbook | BytesIO | Path | str | None,
worksheet: str | Worksheet | None = None,
) -> tuple[Workbook, Worksheet, bool]:
"""Establish the target excel workbook and worksheet."""
from xlsxwriter import Workbook
from xlsxwriter.worksheet import Worksheet
if isinstance(workbook, Workbook):
wb, can_close = workbook, False
ws = (
worksheet
if (
isinstance(worksheet, Worksheet)
and _xl_worksheet_in_workbook(wb, worksheet)
)
else wb.get_worksheet_by_name(name=worksheet)
)
elif isinstance(worksheet, Worksheet):
msg = f"worksheet object requires the parent workbook object; found workbook={workbook!r}"
raise TypeError(msg)
else:
workbook_options = {
"nan_inf_to_errors": True,
"strings_to_formulas": False,
"default_date_format": _XL_DEFAULT_DTYPE_FORMATS_[Date],
}
if isinstance(workbook, BytesIO):
wb, ws, can_close = Workbook(workbook, workbook_options), None, True
else:
file = Path("dataframe.xlsx" if workbook is None else workbook)
wb = Workbook(
(file if file.suffix else file.with_suffix(".xlsx"))
.expanduser()
.resolve(strict=False),
workbook_options,
)
ws, can_close = None, True
if ws is None:
if isinstance(worksheet, Worksheet):
ws = _xl_worksheet_in_workbook(wb, worksheet, return_worksheet=True)
else:
ws = wb.add_worksheet(name=worksheet)
return wb, ws, can_close
def _xl_table_formula(df: DataFrame, cols: Iterable[str], func: str) -> str:
"""Return a formula using structured references to columns in a named table."""
m: dict[str, Any] = {}
if isinstance(cols, str):
cols = [cols]
if _adjacent_cols(df, cols, min_max=m):
return f"={func.upper()}([@[{m['min']['name']}]:[{m['max']['name']}]])"
else:
colrefs = ",".join(f"[@[{c}]]" for c in cols)
return f"={func.upper()}({colrefs})"
def _xl_unique_table_name(wb: Workbook) -> str:
"""Establish a unique (per-workbook) table object name."""
table_prefix = "Frame"
polars_tables: set[str] = set()
for ws in wb.worksheets():
polars_tables.update(
tbl["name"] for tbl in ws.tables if tbl["name"].startswith(table_prefix)
)
n = len(polars_tables)
table_name = f"{table_prefix}{n}"
while table_name in polars_tables:
n += 1
table_name = f"{table_prefix}{n}"
return table_name