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""" Module for applying conditional formatting to DataFrames and Series. """ from collections import defaultdict from contextlib import contextmanager import copy from functools import partial from itertools import product from typing import ( Any, Callable, DefaultDict, Dict, List, Optional, Sequence, Tuple, Union, ) from uuid import uuid1 import numpy as np from pandas._config import get_option from pandas._libs import lib from pandas._typing import Axis, FrameOrSeries, FrameOrSeriesUnion, Label from pandas.compat._optional import import_optional_dependency from pandas.util._decorators import doc from pandas.core.dtypes.common import is_float import pandas as pd from pandas.api.types import is_dict_like, is_list_like import pandas.core.common as com from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice jinja2 = import_optional_dependency("jinja2", extra="DataFrame.style requires jinja2.") try: from matplotlib import colors import matplotlib.pyplot as plt has_mpl = True except ImportError: has_mpl = False no_mpl_message = "{0} requires matplotlib." @contextmanager def _mpl(func: Callable): if has_mpl: yield plt, colors else: raise ImportError(no_mpl_message.format(func.__name__)) class Styler: """ Helps style a DataFrame or Series according to the data with HTML and CSS. Parameters ---------- data : Series or DataFrame Data to be styled - either a Series or DataFrame. precision : int Precision to round floats to, defaults to pd.options.display.precision. table_styles : list-like, default None List of {selector: (attr, value)} dicts; see Notes. uuid : str, default None A unique identifier to avoid CSS collisions; generated automatically. caption : str, default None Caption to attach to the table. table_attributes : str, default None Items that show up in the opening ``<table>`` tag in addition to automatic (by default) id. cell_ids : bool, default True If True, each cell will have an ``id`` attribute in their HTML tag. The ``id`` takes the form ``T_<uuid>_row<num_row>_col<num_col>`` where ``<uuid>`` is the unique identifier, ``<num_row>`` is the row number and ``<num_col>`` is the column number. na_rep : str, optional Representation for missing values. If ``na_rep`` is None, no special formatting is applied. .. versionadded:: 1.0.0 Attributes ---------- env : Jinja2 jinja2.Environment template : Jinja2 Template loader : Jinja2 Loader See Also -------- DataFrame.style : Return a Styler object containing methods for building a styled HTML representation for the DataFrame. Notes ----- Most styling will be done by passing style functions into ``Styler.apply`` or ``Styler.applymap``. Style functions should return values with strings containing CSS ``'attr: value'`` that will be applied to the indicated cells. If using in the Jupyter notebook, Styler has defined a ``_repr_html_`` to automatically render itself. Otherwise call Styler.render to get the generated HTML. CSS classes are attached to the generated HTML * Index and Column names include ``index_name`` and ``level<k>`` where `k` is its level in a MultiIndex * Index label cells include * ``row_heading`` * ``row<n>`` where `n` is the numeric position of the row * ``level<k>`` where `k` is the level in a MultiIndex * Column label cells include * ``col_heading`` * ``col<n>`` where `n` is the numeric position of the column * ``level<k>`` where `k` is the level in a MultiIndex * Blank cells include ``blank`` * Data cells include ``data`` """ loader = jinja2.PackageLoader("pandas", "io/formats/templates") env = jinja2.Environment(loader=loader, trim_blocks=True) template = env.get_template("html.tpl") def __init__( self, data: FrameOrSeriesUnion, precision: Optional[int] = None, table_styles: Optional[List[Dict[str, List[Tuple[str, str]]]]] = None, uuid: Optional[str] = None, caption: Optional[str] = None, table_attributes: Optional[str] = None, cell_ids: bool = True, na_rep: Optional[str] = None, ): self.ctx: DefaultDict[Tuple[int, int], List[str]] = defaultdict(list) self._todo: List[Tuple[Callable, Tuple, Dict]] = [] if not isinstance(data, (pd.Series, pd.DataFrame)): raise TypeError("``data`` must be a Series or DataFrame") if data.ndim == 1: data = data.to_frame() if not data.index.is_unique or not data.columns.is_unique: raise ValueError("style is not supported for non-unique indices.") self.data = data self.index = data.index self.columns = data.columns self.uuid = uuid self.table_styles = table_styles self.caption = caption if precision is None: precision = get_option("display.precision") self.precision = precision self.table_attributes = table_attributes self.hidden_index = False self.hidden_columns: Sequence[int] = [] self.cell_ids = cell_ids self.na_rep = na_rep # display_funcs maps (row, col) -> formatting function def default_display_func(x): if self.na_rep is not None and pd.isna(x): return self.na_rep elif is_float(x): display_format = f"{x:.{self.precision}f}" return display_format else: return x self._display_funcs: DefaultDict[ Tuple[int, int], Callable[[Any], str] ] = defaultdict(lambda: default_display_func) def _repr_html_(self) -> str: """ Hooks into Jupyter notebook rich display system. """ return self.render() @doc(NDFrame.to_excel, klass="Styler") def to_excel( self, excel_writer, sheet_name: str = "Sheet1", na_rep: str = "", float_format: Optional[str] = None, columns: Optional[Sequence[Label]] = None, header: Union[Sequence[Label], bool] = True, index: bool = True, index_label: Optional[Union[Label, Sequence[Label]]] = None, startrow: int = 0, startcol: int = 0, engine: Optional[str] = None, merge_cells: bool = True, encoding: Optional[str] = None, inf_rep: str = "inf", verbose: bool = True, freeze_panes: Optional[Tuple[int, int]] = None, ) -> None: from pandas.io.formats.excel import ExcelFormatter formatter = ExcelFormatter( self, na_rep=na_rep, cols=columns, header=header, float_format=float_format, index=index, index_label=index_label, merge_cells=merge_cells, inf_rep=inf_rep, ) formatter.write( excel_writer, sheet_name=sheet_name, startrow=startrow, startcol=startcol, freeze_panes=freeze_panes, engine=engine, ) def _translate(self): """ Convert the DataFrame in `self.data` and the attrs from `_build_styles` into a dictionary of {head, body, uuid, cellstyle}. """ table_styles = self.table_styles or [] caption = self.caption ctx = self.ctx precision = self.precision hidden_index = self.hidden_index hidden_columns = self.hidden_columns uuid = self.uuid or str(uuid1()).replace("-", "_") ROW_HEADING_CLASS = "row_heading" COL_HEADING_CLASS = "col_heading" INDEX_NAME_CLASS = "index_name" DATA_CLASS = "data" BLANK_CLASS = "blank" BLANK_VALUE = "" def format_attr(pair): return f"{pair['key']}={pair['value']}" # for sparsifying a MultiIndex idx_lengths = _get_level_lengths(self.index) col_lengths = _get_level_lengths(self.columns, hidden_columns) cell_context = dict() n_rlvls = self.data.index.nlevels n_clvls = self.data.columns.nlevels rlabels = self.data.index.tolist() clabels = self.data.columns.tolist() if n_rlvls == 1: rlabels = [[x] for x in rlabels] if n_clvls == 1: clabels = [[x] for x in clabels] clabels = list(zip(*clabels)) cellstyle_map = defaultdict(list) head = [] for r in range(n_clvls): # Blank for Index columns... row_es = [ { "type": "th", "value": BLANK_VALUE, "display_value": BLANK_VALUE, "is_visible": not hidden_index, "class": " ".join([BLANK_CLASS]), } ] * (n_rlvls - 1) # ... except maybe the last for columns.names name = self.data.columns.names[r] cs = [ BLANK_CLASS if name is None else INDEX_NAME_CLASS, f"level{r}", ] name = BLANK_VALUE if name is None else name row_es.append( { "type": "th", "value": name, "display_value": name, "class": " ".join(cs), "is_visible": not hidden_index, } ) if clabels: for c, value in enumerate(clabels[r]): cs = [ COL_HEADING_CLASS, f"level{r}", f"col{c}", ] cs.extend( cell_context.get("col_headings", {}).get(r, {}).get(c, []) ) es = { "type": "th", "value": value, "display_value": value, "class": " ".join(cs), "is_visible": _is_visible(c, r, col_lengths), } colspan = col_lengths.get((r, c), 0) if colspan > 1: es["attributes"] = [ format_attr({"key": "colspan", "value": colspan}) ] row_es.append(es) head.append(row_es) if ( self.data.index.names and com.any_not_none(*self.data.index.names) and not hidden_index ): index_header_row = [] for c, name in enumerate(self.data.index.names): cs = [INDEX_NAME_CLASS, f"level{c}"] name = "" if name is None else name index_header_row.append( {"type": "th", "value": name, "class": " ".join(cs)} ) index_header_row.extend( [{"type": "th", "value": BLANK_VALUE, "class": " ".join([BLANK_CLASS])}] * (len(clabels[0]) - len(hidden_columns)) ) head.append(index_header_row) body = [] for r, idx in enumerate(self.data.index): row_es = [] for c, value in enumerate(rlabels[r]): rid = [ ROW_HEADING_CLASS, f"level{c}", f"row{r}", ] es = { "type": "th", "is_visible": (_is_visible(r, c, idx_lengths) and not hidden_index), "value": value, "display_value": value, "id": "_".join(rid[1:]), "class": " ".join(rid), } rowspan = idx_lengths.get((c, r), 0) if rowspan > 1: es["attributes"] = [ format_attr({"key": "rowspan", "value": rowspan}) ] row_es.append(es) for c, col in enumerate(self.data.columns): cs = [DATA_CLASS, f"row{r}", f"col{c}"] cs.extend(cell_context.get("data", {}).get(r, {}).get(c, [])) formatter = self._display_funcs[(r, c)] value = self.data.iloc[r, c] row_dict = { "type": "td", "value": value, "class": " ".join(cs), "display_value": formatter(value), "is_visible": (c not in hidden_columns), } # only add an id if the cell has a style props = [] if self.cell_ids or (r, c) in ctx: row_dict["id"] = "_".join(cs[1:]) for x in ctx[r, c]: # have to handle empty styles like [''] if x.count(":"): props.append(tuple(x.split(":"))) else: props.append(("", "")) row_es.append(row_dict) cellstyle_map[tuple(props)].append(f"row{r}_col{c}") body.append(row_es) cellstyle = [ {"props": list(props), "selectors": selectors} for props, selectors in cellstyle_map.items() ] table_attr = self.table_attributes use_mathjax = get_option("display.html.use_mathjax") if not use_mathjax: table_attr = table_attr or "" if 'class="' in table_attr: table_attr = table_attr.replace('class="', 'class="tex2jax_ignore ') else: table_attr += ' class="tex2jax_ignore"' return dict( head=head, cellstyle=cellstyle, body=body, uuid=uuid, precision=precision, table_styles=table_styles, caption=caption, table_attributes=table_attr, ) def format(self, formatter, subset=None, na_rep: Optional[str] = None) -> "Styler": """ Format the text display value of cells. Parameters ---------- formatter : str, callable, dict or None If ``formatter`` is None, the default formatter is used. subset : IndexSlice An argument to ``DataFrame.loc`` that restricts which elements ``formatter`` is applied to. na_rep : str, optional Representation for missing values. If ``na_rep`` is None, no special formatting is applied. .. versionadded:: 1.0.0 Returns ------- self : Styler Notes ----- ``formatter`` is either an ``a`` or a dict ``{column name: a}`` where ``a`` is one of - str: this will be wrapped in: ``a.format(x)`` - callable: called with the value of an individual cell The default display value for numeric values is the "general" (``g``) format with ``pd.options.display.precision`` precision. Examples -------- >>> df = pd.DataFrame(np.random.randn(4, 2), columns=['a', 'b']) >>> df.style.format("{:.2%}") >>> df['c'] = ['a', 'b', 'c', 'd'] >>> df.style.format({'c': str.upper}) """ if formatter is None: assert self._display_funcs.default_factory is not None formatter = self._display_funcs.default_factory() if subset is None: row_locs = range(len(self.data)) col_locs = range(len(self.data.columns)) else: subset = _non_reducing_slice(subset) if len(subset) == 1: subset = subset, self.data.columns sub_df = self.data.loc[subset] row_locs = self.data.index.get_indexer_for(sub_df.index) col_locs = self.data.columns.get_indexer_for(sub_df.columns) if is_dict_like(formatter): for col, col_formatter in formatter.items(): # formatter must be callable, so '{}' are converted to lambdas col_formatter = _maybe_wrap_formatter(col_formatter, na_rep) col_num = self.data.columns.get_indexer_for([col])[0] for row_num in row_locs: self._display_funcs[(row_num, col_num)] = col_formatter else: # single scalar to format all cells with formatter = _maybe_wrap_formatter(formatter, na_rep) locs = product(*(row_locs, col_locs)) for i, j in locs: self._display_funcs[(i, j)] = formatter return self def render(self, **kwargs) -> str: """ Render the built up styles to HTML. Parameters ---------- **kwargs Any additional keyword arguments are passed through to ``self.template.render``. This is useful when you need to provide additional variables for a custom template. Returns ------- rendered : str The rendered HTML. Notes ----- ``Styler`` objects have defined the ``_repr_html_`` method which automatically calls ``self.render()`` when it's the last item in a Notebook cell. When calling ``Styler.render()`` directly, wrap the result in ``IPython.display.HTML`` to view the rendered HTML in the notebook. Pandas uses the following keys in render. Arguments passed in ``**kwargs`` take precedence, so think carefully if you want to override them: * head * cellstyle * body * uuid * precision * table_styles * caption * table_attributes """ self._compute() # TODO: namespace all the pandas keys d = self._translate() # filter out empty styles, every cell will have a class # but the list of props may just be [['', '']]. # so we have the nested anys below trimmed = [x for x in d["cellstyle"] if any(any(y) for y in x["props"])] d["cellstyle"] = trimmed d.update(kwargs) return self.template.render(**d) def _update_ctx(self, attrs: DataFrame) -> None: """ Update the state of the Styler. Collects a mapping of {index_label: ['<property>: <value>']}. Parameters ---------- attrs : DataFrame should contain strings of '<property>: <value>;<prop2>: <val2>' Whitespace shouldn't matter and the final trailing ';' shouldn't matter. """ coli = {k: i for i, k in enumerate(self.columns)} rowi = {k: i for i, k in enumerate(self.index)} for jj in range(len(attrs.columns)): cn = attrs.columns[jj] j = coli[cn] for rn, c in attrs[[cn]].itertuples(): if not c: continue c = c.rstrip(";") if not c: continue i = rowi[rn] for pair in c.split(";"): self.ctx[(i, j)].append(pair) def _copy(self, deepcopy: bool = False) -> "Styler": styler = Styler( self.data, precision=self.precision, caption=self.caption, uuid=self.uuid, table_styles=self.table_styles, na_rep=self.na_rep, ) if deepcopy: styler.ctx = copy.deepcopy(self.ctx) styler._todo = copy.deepcopy(self._todo) else: styler.ctx = self.ctx styler._todo = self._todo return styler def __copy__(self) -> "Styler": """ Deep copy by default. """ return self._copy(deepcopy=False) def __deepcopy__(self, memo) -> "Styler": return self._copy(deepcopy=True) def clear(self) -> None: """ Reset the styler, removing any previously applied styles. Returns None. """ self.ctx.clear() self._todo = [] def _compute(self): """ Execute the style functions built up in `self._todo`. Relies on the conventions that all style functions go through .apply or .applymap. The append styles to apply as tuples of (application method, *args, **kwargs) """ r = self for func, args, kwargs in self._todo: r = func(self)(*args, **kwargs) return r def _apply( self, func: Callable[..., "Styler"], axis: Optional[Axis] = 0, subset=None, **kwargs, ) -> "Styler": subset = slice(None) if subset is None else subset subset = _non_reducing_slice(subset) data = self.data.loc[subset] if axis is not None: result = data.apply(func, axis=axis, result_type="expand", **kwargs) result.columns = data.columns else: result = func(data, **kwargs) if not isinstance(result, pd.DataFrame): raise TypeError( f"Function {repr(func)} must return a DataFrame when " f"passed to `Styler.apply` with axis=None" ) if not ( result.index.equals(data.index) and result.columns.equals(data.columns) ): raise ValueError( f"Result of {repr(func)} must have identical " f"index and columns as the input" ) result_shape = result.shape expected_shape = self.data.loc[subset].shape if result_shape != expected_shape: raise ValueError( f"Function {repr(func)} returned the wrong shape.\n" f"Result has shape: {result.shape}\n" f"Expected shape: {expected_shape}" ) self._update_ctx(result) return self def apply( self, func: Callable[..., "Styler"], axis: Optional[Axis] = 0, subset=None, **kwargs, ) -> "Styler": """ Apply a function column-wise, row-wise, or table-wise. Updates the HTML representation with the result. Parameters ---------- func : function ``func`` should take a Series or DataFrame (depending on ``axis``), and return an object with the same shape. Must return a DataFrame with identical index and column labels when ``axis=None``. axis : {0 or 'index', 1 or 'columns', None}, default 0 Apply to each column (``axis=0`` or ``'index'``), to each row (``axis=1`` or ``'columns'``), or to the entire DataFrame at once with ``axis=None``. subset : IndexSlice A valid indexer to limit ``data`` to *before* applying the function. Consider using a pandas.IndexSlice. **kwargs : dict Pass along to ``func``. Returns ------- self : Styler Notes ----- The output shape of ``func`` should match the input, i.e. if ``x`` is the input row, column, or table (depending on ``axis``), then ``func(x).shape == x.shape`` should be true. This is similar to ``DataFrame.apply``, except that ``axis=None`` applies the function to the entire DataFrame at once, rather than column-wise or row-wise. Examples -------- >>> def highlight_max(x): ... return ['background-color: yellow' if v == x.max() else '' for v in x] ... >>> df = pd.DataFrame(np.random.randn(5, 2)) >>> df.style.apply(highlight_max) """ self._todo.append( (lambda instance: getattr(instance, "_apply"), (func, axis, subset), kwargs) ) return self def _applymap(self, func: Callable, subset=None, **kwargs) -> "Styler": func = partial(func, **kwargs) # applymap doesn't take kwargs? if subset is None: subset = pd.IndexSlice[:] subset = _non_reducing_slice(subset) result = self.data.loc[subset].applymap(func) self._update_ctx(result) return self def applymap(self, func: Callable, subset=None, **kwargs) -> "Styler": """ Apply a function elementwise. Updates the HTML representation with the result. Parameters ---------- func : function ``func`` should take a scalar and return a scalar. subset : IndexSlice A valid indexer to limit ``data`` to *before* applying the function. Consider using a pandas.IndexSlice. **kwargs : dict Pass along to ``func``. Returns ------- self : Styler See Also -------- Styler.where """ self._todo.append( (lambda instance: getattr(instance, "_applymap"), (func, subset), kwargs) ) return self def where( self, cond: Callable, value: str, other: Optional[str] = None, subset=None, **kwargs, ) -> "Styler": """ Apply a function elementwise. Updates the HTML representation with a style which is selected in accordance with the return value of a function. Parameters ---------- cond : callable ``cond`` should take a scalar and return a boolean. value : str Applied when ``cond`` returns true. other : str Applied when ``cond`` returns false. subset : IndexSlice A valid indexer to limit ``data`` to *before* applying the function. Consider using a pandas.IndexSlice. **kwargs : dict Pass along to ``cond``. Returns ------- self : Styler See Also -------- Styler.applymap """ if other is None: other = "" return self.applymap( lambda val: value if cond(val) else other, subset=subset, **kwargs ) def set_precision(self, precision: int) -> "Styler": """ Set the precision used to render. Parameters ---------- precision : int Returns ------- self : Styler """ self.precision = precision return self def set_table_attributes(self, attributes: str) -> "Styler": """ Set the table attributes. These are the items that show up in the opening ``<table>`` tag in addition to to automatic (by default) id. Parameters ---------- attributes : str Returns ------- self : Styler Examples -------- >>> df = pd.DataFrame(np.random.randn(10, 4)) >>> df.style.set_table_attributes('class="pure-table"') # ... <table class="pure-table"> ... """ self.table_attributes = attributes return self def export(self) -> List[Tuple[Callable, Tuple, Dict]]: """ Export the styles to applied to the current Styler. Can be applied to a second style with ``Styler.use``. Returns ------- styles : list See Also -------- Styler.use """ return self._todo def use(self, styles: List[Tuple[Callable, Tuple, Dict]]) -> "Styler": """ Set the styles on the current Styler. Possibly uses styles from ``Styler.export``. Parameters ---------- styles : list List of style functions. Returns ------- self : Styler See Also -------- Styler.export """ self._todo.extend(styles) return self def set_uuid(self, uuid: str) -> "Styler": """ Set the uuid for a Styler. Parameters ---------- uuid : str Returns ------- self : Styler """ self.uuid = uuid return self def set_caption(self, caption: str) -> "Styler": """ Set the caption on a Styler. Parameters ---------- caption : str Returns ------- self : Styler """ self.caption = caption return self def set_table_styles(self, table_styles) -> "Styler": """ Set the table styles on a Styler. These are placed in a ``<style>`` tag before the generated HTML table. Parameters ---------- table_styles : list Each individual table_style should be a dictionary with ``selector`` and ``props`` keys. ``selector`` should be a CSS selector that the style will be applied to (automatically prefixed by the table's UUID) and ``props`` should be a list of tuples with ``(attribute, value)``. Returns ------- self : Styler Examples -------- >>> df = pd.DataFrame(np.random.randn(10, 4)) >>> df.style.set_table_styles( ... [{'selector': 'tr:hover', ... 'props': [('background-color', 'yellow')]}] ... ) """ self.table_styles = table_styles return self def set_na_rep(self, na_rep: str) -> "Styler": """ Set the missing data representation on a Styler. .. versionadded:: 1.0.0 Parameters ---------- na_rep : str Returns ------- self : Styler """ self.na_rep = na_rep return self def hide_index(self) -> "Styler": """ Hide any indices from rendering. .. versionadded:: 0.23.0 Returns ------- self : Styler """ self.hidden_index = True return self def hide_columns(self, subset) -> "Styler": """ Hide columns from rendering. .. versionadded:: 0.23.0 Parameters ---------- subset : IndexSlice An argument to ``DataFrame.loc`` that identifies which columns are hidden. Returns ------- self : Styler """ subset = _non_reducing_slice(subset) hidden_df = self.data.loc[subset] self.hidden_columns = self.columns.get_indexer_for(hidden_df.columns) return self # ----------------------------------------------------------------------- # A collection of "builtin" styles # ----------------------------------------------------------------------- @staticmethod def _highlight_null(v, null_color: str) -> str: return f"background-color: {null_color}" if pd.isna(v) else "" def highlight_null( self, null_color: str = "red", subset: Optional[Union[Label, Sequence[Label]]] = None, ) -> "Styler": """ Shade the background ``null_color`` for missing values. Parameters ---------- null_color : str, default 'red' subset : label or list of labels, default None A valid slice for ``data`` to limit the style application to. .. versionadded:: 1.1.0 Returns ------- self : Styler """ self.applymap(self._highlight_null, null_color=null_color, subset=subset) return self def background_gradient( self, cmap="PuBu", low: float = 0, high: float = 0, axis: Optional[Axis] = 0, subset=None, text_color_threshold: float = 0.408, vmin: Optional[float] = None, vmax: Optional[float] = None, ) -> "Styler": """ Color the background in a gradient style. The background color is determined according to the data in each column (optionally row). Requires matplotlib. Parameters ---------- cmap : str or colormap Matplotlib colormap. low : float Compress the range by the low. high : float Compress the range by the high. axis : {0 or 'index', 1 or 'columns', None}, default 0 Apply to each column (``axis=0`` or ``'index'``), to each row (``axis=1`` or ``'columns'``), or to the entire DataFrame at once with ``axis=None``. subset : IndexSlice A valid slice for ``data`` to limit the style application to. text_color_threshold : float or int Luminance threshold for determining text color. Facilitates text visibility across varying background colors. From 0 to 1. 0 = all text is dark colored, 1 = all text is light colored. .. versionadded:: 0.24.0 vmin : float, optional Minimum data value that corresponds to colormap minimum value. When None (default): the minimum value of the data will be used. .. versionadded:: 1.0.0 vmax : float, optional Maximum data value that corresponds to colormap maximum value. When None (default): the maximum value of the data will be used. .. versionadded:: 1.0.0 Returns ------- self : Styler Raises ------ ValueError If ``text_color_threshold`` is not a value from 0 to 1. Notes ----- Set ``text_color_threshold`` or tune ``low`` and ``high`` to keep the text legible by not using the entire range of the color map. The range of the data is extended by ``low * (x.max() - x.min())`` and ``high * (x.max() - x.min())`` before normalizing. """ subset = _maybe_numeric_slice(self.data, subset) subset = _non_reducing_slice(subset) self.apply( self._background_gradient, cmap=cmap, subset=subset, axis=axis, low=low, high=high, text_color_threshold=text_color_threshold, vmin=vmin, vmax=vmax, ) return self @staticmethod def _background_gradient( s, cmap="PuBu", low: float = 0, high: float = 0, text_color_threshold: float = 0.408, vmin: Optional[float] = None, vmax: Optional[float] = None, ): """ Color background in a range according to the data. """ if ( not isinstance(text_color_threshold, (float, int)) or not 0 <= text_color_threshold <= 1 ): msg = "`text_color_threshold` must be a value from 0 to 1." raise ValueError(msg) with _mpl(Styler.background_gradient) as (plt, colors): smin = np.nanmin(s.to_numpy()) if vmin is None else vmin smax = np.nanmax(s.to_numpy()) if vmax is None else vmax rng = smax - smin # extend lower / upper bounds, compresses color range norm = colors.Normalize(smin - (rng * low), smax + (rng * high)) # matplotlib colors.Normalize modifies inplace? # https://github.com/matplotlib/matplotlib/issues/5427 rgbas = plt.cm.get_cmap(cmap)(norm(s.to_numpy(dtype=float))) def relative_luminance(rgba) -> float: """ Calculate relative luminance of a color. The calculation adheres to the W3C standards (https://www.w3.org/WAI/GL/wiki/Relative_luminance) Parameters ---------- color : rgb or rgba tuple Returns ------- float The relative luminance as a value from 0 to 1 """ r, g, b = ( x / 12.92 if x <= 0.03928 else ((x + 0.055) / 1.055 ** 2.4) for x in rgba[:3] ) return 0.2126 * r + 0.7152 * g + 0.0722 * b def css(rgba) -> str: dark = relative_luminance(rgba) < text_color_threshold text_color = "#f1f1f1" if dark else "#000000" return f"background-color: {colors.rgb2hex(rgba)};color: {text_color};" if s.ndim == 1: return [css(rgba) for rgba in rgbas] else: return pd.DataFrame( [[css(rgba) for rgba in row] for row in rgbas], index=s.index, columns=s.columns, ) def set_properties(self, subset=None, **kwargs) -> "Styler": """ Method to set one or more non-data dependent properties or each cell. Parameters ---------- subset : IndexSlice A valid slice for ``data`` to limit the style application to. **kwargs : dict A dictionary of property, value pairs to be set for each cell. Returns ------- self : Styler Examples -------- >>> df = pd.DataFrame(np.random.randn(10, 4)) >>> df.style.set_properties(color="white", align="right") >>> df.style.set_properties(**{'background-color': 'yellow'}) """ values = ";".join(f"{p}: {v}" for p, v in kwargs.items()) f = lambda x: values return self.applymap(f, subset=subset) @staticmethod def _bar( s, align: str, colors: List[str], width: float = 100, vmin: Optional[float] = None, vmax: Optional[float] = None, ): """ Draw bar chart in dataframe cells. """ # Get input value range. smin = np.nanmin(s.to_numpy()) if vmin is None else vmin smax = np.nanmax(s.to_numpy()) if vmax is None else vmax if align == "mid": smin = min(0, smin) smax = max(0, smax) elif align == "zero": # For "zero" mode, we want the range to be symmetrical around zero. smax = max(abs(smin), abs(smax)) smin = -smax # Transform to percent-range of linear-gradient normed = width * (s.to_numpy(dtype=float) - smin) / (smax - smin + 1e-12) zero = -width * smin / (smax - smin + 1e-12) def css_bar(start: float, end: float, color: str) -> str: """ Generate CSS code to draw a bar from start to end. """ css = "width: 10em; height: 80%;" if end > start: css += "background: linear-gradient(90deg," if start > 0: css += f" transparent {start:.1f}%, {color} {start:.1f}%, " e = min(end, width) css += f"{color} {e:.1f}%, transparent {e:.1f}%)" return css def css(x): if pd.isna(x): return "" # avoid deprecated indexing `colors[x > zero]` color = colors[1] if x > zero else colors[0] if align == "left": return css_bar(0, x, color) else: return css_bar(min(x, zero), max(x, zero), color) if s.ndim == 1: return [css(x) for x in normed] else: return pd.DataFrame( [[css(x) for x in row] for row in normed], index=s.index, columns=s.columns, ) def bar( self, subset=None, axis: Optional[Axis] = 0, color="#d65f5f", width: float = 100, align: str = "left", vmin: Optional[float] = None, vmax: Optional[float] = None, ) -> "Styler": """ Draw bar chart in the cell backgrounds. Parameters ---------- subset : IndexSlice, optional A valid slice for `data` to limit the style application to. axis : {0 or 'index', 1 or 'columns', None}, default 0 Apply to each column (``axis=0`` or ``'index'``), to each row (``axis=1`` or ``'columns'``), or to the entire DataFrame at once with ``axis=None``. color : str or 2-tuple/list If a str is passed, the color is the same for both negative and positive numbers. If 2-tuple/list is used, the first element is the color_negative and the second is the color_positive (eg: ['#d65f5f', '#5fba7d']). width : float, default 100 A number between 0 or 100. The largest value will cover `width` percent of the cell's width. align : {'left', 'zero',' mid'}, default 'left' How to align the bars with the cells. - 'left' : the min value starts at the left of the cell. - 'zero' : a value of zero is located at the center of the cell. - 'mid' : the center of the cell is at (max-min)/2, or if values are all negative (positive) the zero is aligned at the right (left) of the cell. vmin : float, optional Minimum bar value, defining the left hand limit of the bar drawing range, lower values are clipped to `vmin`. When None (default): the minimum value of the data will be used. .. versionadded:: 0.24.0 vmax : float, optional Maximum bar value, defining the right hand limit of the bar drawing range, higher values are clipped to `vmax`. When None (default): the maximum value of the data will be used. .. versionadded:: 0.24.0 Returns ------- self : Styler """ if align not in ("left", "zero", "mid"): raise ValueError("`align` must be one of {'left', 'zero',' mid'}") if not (is_list_like(color)): color = [color, color] elif len(color) == 1: color = [color[0], color[0]] elif len(color) > 2: raise ValueError( "`color` must be string or a list-like " "of length 2: [`color_neg`, `color_pos`] " "(eg: color=['#d65f5f', '#5fba7d'])" ) subset = _maybe_numeric_slice(self.data, subset) subset = _non_reducing_slice(subset) self.apply( self._bar, subset=subset, axis=axis, align=align, colors=color, width=width, vmin=vmin, vmax=vmax, ) return self def highlight_max( self, subset=None, color: str = "yellow", axis: Optional[Axis] = 0 ) -> "Styler": """ Highlight the maximum by shading the background. Parameters ---------- subset : IndexSlice, default None A valid slice for ``data`` to limit the style application to. color : str, default 'yellow' axis : {0 or 'index', 1 or 'columns', None}, default 0 Apply to each column (``axis=0`` or ``'index'``), to each row (``axis=1`` or ``'columns'``), or to the entire DataFrame at once with ``axis=None``. Returns ------- self : Styler """ return self._highlight_handler(subset=subset, color=color, axis=axis, max_=True) def highlight_min( self, subset=None, color: str = "yellow", axis: Optional[Axis] = 0 ) -> "Styler": """ Highlight the minimum by shading the background. Parameters ---------- subset : IndexSlice, default None A valid slice for ``data`` to limit the style application to. color : str, default 'yellow' axis : {0 or 'index', 1 or 'columns', None}, default 0 Apply to each column (``axis=0`` or ``'index'``), to each row (``axis=1`` or ``'columns'``), or to the entire DataFrame at once with ``axis=None``. Returns ------- self : Styler """ return self._highlight_handler( subset=subset, color=color, axis=axis, max_=False ) def _highlight_handler( self, subset=None, color: str = "yellow", axis: Optional[Axis] = None, max_: bool = True, ) -> "Styler": subset = _non_reducing_slice(_maybe_numeric_slice(self.data, subset)) self.apply( self._highlight_extrema, color=color, axis=axis, subset=subset, max_=max_ ) return self @staticmethod def _highlight_extrema( data: FrameOrSeries, color: str = "yellow", max_: bool = True ): """ Highlight the min or max in a Series or DataFrame. """ attr = f"background-color: {color}" if max_: extrema = data == np.nanmax(data.to_numpy()) else: extrema = data == np.nanmin(data.to_numpy()) if data.ndim == 1: # Series from .apply return [attr if v else "" for v in extrema] else: # DataFrame from .tee return pd.DataFrame( np.where(extrema, attr, ""), index=data.index, columns=data.columns ) @classmethod def from_custom_template(cls, searchpath, name): """ Factory function for creating a subclass of ``Styler``. Uses a custom template and Jinja environment. Parameters ---------- searchpath : str or list Path or paths of directories containing the templates. name : str Name of your custom template to use for rendering. Returns ------- MyStyler : subclass of Styler Has the correct ``env`` and ``template`` class attributes set. """ loader = jinja2.ChoiceLoader([jinja2.FileSystemLoader(searchpath), cls.loader]) class MyStyler(cls): env = jinja2.Environment(loader=loader) template = env.get_template(name) return MyStyler def pipe(self, func: Callable, *args, **kwargs): """ Apply ``func(self, *args, **kwargs)``, and return the result. .. versionadded:: 0.24.0 Parameters ---------- func : function Function to apply to the Styler. Alternatively, a ``(callable, keyword)`` tuple where ``keyword`` is a string indicating the keyword of ``callable`` that expects the Styler. *args : optional Arguments passed to `func`. **kwargs : optional A dictionary of keyword arguments passed into ``func``. Returns ------- object : The value returned by ``func``. See Also -------- DataFrame.pipe : Analogous method for DataFrame. Styler.apply : Apply a function row-wise, column-wise, or table-wise to modify the dataframe's styling. Notes ----- Like :meth:`DataFrame.pipe`, this method can simplify the application of several user-defined functions to a styler. Instead of writing: .. code-block:: python f(g(df.style.set_precision(3), arg1=a), arg2=b, arg3=c) users can write: .. code-block:: python (df.style.set_precision(3) .pipe(g, arg1=a) .pipe(f, arg2=b, arg3=c)) In particular, this allows users to define functions that take a styler object, along with other parameters, and return the styler after making styling changes (such as calling :meth:`Styler.apply` or :meth:`Styler.set_properties`). Using ``.pipe``, these user-defined style "transformations" can be interleaved with calls to the built-in Styler interface. Examples -------- >>> def format_conversion(styler): ... return (styler.set_properties(**{'text-align': 'right'}) ... .format({'conversion': '{:.1%}'})) The user-defined ``format_conversion`` function above can be called within a sequence of other style modifications: >>> df = pd.DataFrame({'trial': list(range(5)), ... 'conversion': [0.75, 0.85, np.nan, 0.7, 0.72]}) >>> (df.style ... .highlight_min(subset=['conversion'], color='yellow') ... .pipe(format_conversion) ... .set_caption("Results with minimum conversion highlighted.")) """ return com.pipe(self, func, *args, **kwargs) def _is_visible(idx_row, idx_col, lengths) -> bool: """ Index -> {(idx_row, idx_col): bool}). """ return (idx_col, idx_row) in lengths def _get_level_lengths(index, hidden_elements=None): """ Given an index, find the level length for each element. Optional argument is a list of index positions which should not be visible. Result is a dictionary of (level, initial_position): span """ if isinstance(index, pd.MultiIndex): levels = index.format(sparsify=lib.no_default, adjoin=False) else: levels = index.format() if hidden_elements is None: hidden_elements = [] lengths = {} if index.nlevels == 1: for i, value in enumerate(levels): if i not in hidden_elements: lengths[(0, i)] = 1 return lengths for i, lvl in enumerate(levels): for j, row in enumerate(lvl): if not get_option("display.multi_sparse"): lengths[(i, j)] = 1 elif (row is not lib.no_default) and (j not in hidden_elements): last_label = j lengths[(i, last_label)] = 1 elif row is not lib.no_default: # even if its hidden, keep track of it in case # length >1 and later elements are visible last_label = j lengths[(i, last_label)] = 0 elif j not in hidden_elements: lengths[(i, last_label)] += 1 non_zero_lengths = { element: length for element, length in lengths.items() if length >= 1 } return non_zero_lengths def _maybe_wrap_formatter( formatter: Union[Callable, str], na_rep: Optional[str] ) -> Callable: if isinstance(formatter, str): formatter_func = lambda x: formatter.format(x) elif callable(formatter): formatter_func = formatter else: msg = f"Expected a template string or callable, got {formatter} instead" raise TypeError(msg) if na_rep is None: return formatter_func elif isinstance(na_rep, str): return lambda x: na_rep if pd.isna(x) else formatter_func(x) else: msg = f"Expected a string, got {na_rep} instead" raise TypeError(msg)

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