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B �5�g� � @ s d Z ddlmZ ddlmZ ddlZddlmZ ddlZddl m Z m Z m Z m Z ddlZddlZddlmZ ddlmZmZmZmZmZmZ dd lmZ dd l m Z ddl Z dd l m Z m!Z! dd l"m#Z# dd l$m%Z% ddl&m' m(Z) ddl*m+Z+m,Z, ddl-m.Z. ddl/m0Z0 eddd�Z1ddl2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9 y ddl:m;Z; ddl<m=Z> dZ?W n e@k �r| dZ?dZAY nX edd�dd ��ZBG d d � d e5�ZCd d d!d"d#d$�d%d&�ZDd8d)d)d)d*d*d+d,d-�d.d/�ZEd9d"d d0d0d1d#d2�d3d4�ZFd"d d d#d5�d6d7�ZGdS ):zF Module for applying conditional formatting to DataFrames and Series. � )� annotations)�contextmanagerN)�partial)�Any�Callable�Hashable�Sequence)� get_option)�Axis�FilePathOrBuffer� FrameOrSeries�FrameOrSeriesUnion� IndexLabel�Scalar)�import_optional_dependency)�doc)� IndexSlice� RangeIndex)� is_list_like)�generic)� DataFrame�Series)�NDFrame)�save_to_buffer�jinja2z DataFrame.style requires jinja2.)�extra)� CSSProperties� CSSStyles�StylerRenderer�Subset�Tooltips�maybe_convert_css_to_tuples�non_reducing_slice)�colorsTFz{0} requires matplotlib.r )�funcc c s$ t rttfV ntt�| j���d S )N)�has_mpl�pltr# � ImportError�no_mpl_message�format�__name__)r$ � r+ �M/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/io/formats/style.py�_mplG s r- c s e Zd ZdZd�dddd d d d d d d d d d� � fdd� Zd d�dd�Zd�ddd d�dd�Zd�ddd d d�dd �Zee j d e j d d �d�d d d d#d$d d%d d d d d d d d&d'd(�d)d*��Z d�dddd+ddddd,d-d+dd+d.� d/d d d d d d ddd d d d d d0�d1d2�Z d�dddd+d+d3�d/d d d d d d4�d5d6�Zdd d7�d8d9�Zdd'd:�d;d<�Zd�d d d=�d>d?�Zd d�d@dA�Zd d�dBdC�Zd'd�dDdE�Zd�dFdGdHd dI�dJdK�Zd�dFdGdHd dI�dLdM�Zd�dNdHd dO�dPdQ�Zd�dNdHd dO�dRdS�Zd�dNd d dHd dT�dUdV�Zd dWdX�dYdZ�Zd d d[�d\d]�Zd^d�d_d`�Z d^d da�dbdc�Z d d dd�dedf�Z dgd dh�didj�Z d�dkddld dm�dndo�Z d�dpd d d dq�drds�Z!d dWdt�dudv�Z"d�dHd dw�dxdy�Z#d�dHd dw�dzd{�Z$ed|d}d~dd d��d�d�d�dGdHd�d�d�d�d d�� d�d���Z%ee%d}d|d�dd�d��d�d�d�dGdHd�d�d�d d��d�d���Z&d�dHd dw�d�d��Z'e(d�d d�d�d�d�d��d�d���Z)d�dHdGd�d d�d�d d��d�d��Z*d�d dHd d d��d�d��Z+d�dHd dGd d d��d�d��Z,d�dHd dGd d d��d�d��Z-d�dHd dGd�d�d d d d��d�d��Z.d�dHd dGd�d�d d d d d�� d�d��Z/e0d�d d d��d�d���Z1dNd��d�d��Z2� Z3S )��Stylera� 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, tuple, default None String caption to attach to the table. Tuple only used for LaTeX dual captions. 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 uuid_len : int, default 5 If ``uuid`` is not specified, the length of the ``uuid`` to randomly generate expressed in hex characters, in range [0, 32]. .. versionadded:: 1.2.0 decimal : str, default "." Character used as decimal separator for floats, complex and integers .. versionadded:: 1.3.0 thousands : str, optional, default None Character used as thousands separator for floats, complex and integers .. versionadded:: 1.3.0 escape : str, optional Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"`` in cell display string with HTML-safe sequences. Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``, ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with LaTeX-safe sequences. .. versionadded:: 1.3.0 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`` NT� �.r z int | NonezCSSStyles | Nonez str | Nonezstr | tuple | None�bool�int�str) �data� precision� table_styles�uuid�caption�table_attributes�cell_ids�na_rep�uuid_len�decimal� thousands�escapec s@ t � j||| ||||d� || _|| _| jd ||| | | d� d S )N)r4 r7 r< r6 r9 r8 r: )� formatterr5 r; r? r= r> )�super�__init__r5 r; r) ) �selfr4 r5 r6 r7 r8 r9 r: r; r< r= r> r? )� __class__r+ r, rB � s" zStyler.__init__)�returnc C s | � � S )zB Hooks into Jupyter notebook rich display system. )�render)rC r+ r+ r, � _repr_html_� s zStyler._repr_html_z bool | None)� sparse_index�sparse_columnsrE c K s0 |dkrt d�}|dkr t d�}| j||f|�S )am Render the ``Styler`` including all applied styles to HTML. Parameters ---------- sparse_index : bool, optional Whether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each row. Defaults to ``pandas.options.styler.sparse.index`` value. sparse_columns : bool, optional Whether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each row. Defaults to ``pandas.options.styler.sparse.columns`` value. **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 * table_styles * caption * table_attributes Nzstyler.sparse.indexzstyler.sparse.columns)r Z _render_html)rC rH rI �kwargsr+ r+ r, rF � s 2z Styler.renderr zCSSProperties | None)�ttips�props� css_classrE c C s\ | j std��|jjr |jjs&td��| jdkr8t� | _|| j_|rL|| j_ |rX|| j_ | S )a� Set the DataFrame of strings on ``Styler`` generating ``:hover`` tooltips. These string based tooltips are only applicable to ``<td>`` HTML elements, and cannot be used for column or index headers. .. versionadded:: 1.3.0 Parameters ---------- ttips : DataFrame DataFrame containing strings that will be translated to tooltips, mapped by identical column and index values that must exist on the underlying Styler data. None, NaN values, and empty strings will be ignored and not affect the rendered HTML. props : list-like or str, optional List of (attr, value) tuples or a valid CSS string. If ``None`` adopts the internal default values described in notes. css_class : str, optional Name of the tooltip class used in CSS, should conform to HTML standards. Only useful if integrating tooltips with external CSS. If ``None`` uses the internal default value 'pd-t'. Returns ------- self : Styler Notes ----- Tooltips are created by adding `<span class="pd-t"></span>` to each data cell and then manipulating the table level CSS to attach pseudo hover and pseudo after selectors to produce the required the results. The default properties for the tooltip CSS class are: - visibility: hidden - position: absolute - z-index: 1 - background-color: black - color: white - transform: translate(-20px, -20px) The property 'visibility: hidden;' is a key prerequisite to the hover functionality, and should always be included in any manual properties specification, using the ``props`` argument. Tooltips are not designed to be efficient, and can add large amounts of additional HTML for larger tables, since they also require that ``cell_ids`` is forced to `True`. Examples -------- Basic application >>> df = pd.DataFrame(data=[[0, 1], [2, 3]]) >>> ttips = pd.DataFrame( ... data=[["Min", ""], [np.nan, "Max"]], columns=df.columns, index=df.index ... ) >>> s = df.style.set_tooltips(ttips).render() Optionally controlling the tooltip visual display >>> df.style.set_tooltips(ttips, css_class='tt-add', props=[ ... ('visibility', 'hidden'), ... ('position', 'absolute'), ... ('z-index', 1)]) >>> df.style.set_tooltips(ttips, css_class='tt-add', ... props='visibility:hidden; position:absolute; z-index:1;') z1Tooltips can only render with 'cell_ids' is True.z=Tooltips render only if `ttips` has unique index and columns.N) r: �NotImplementedError�index� is_unique�columns�KeyError�tooltipsr Ztt_dataZclass_properties� class_name)rC rK rL rM r+ r+ r, � set_tooltips s K zStyler.set_tooltips�storage_options)�klassrV �Sheet1� r �infzSequence[Hashable] | NonezSequence[Hashable] | boolzIndexLabel | Noneztuple[int, int] | None�None)� sheet_namer; � float_formatrQ �headerrO � index_label�startrow�startcol�engine� merge_cells�encoding�inf_rep�verbose� freeze_panesrE c C s@ ddl m} || ||||||| |d� }|j||| | || d� d S )Nr )�ExcelFormatter)r; �colsr^ r] rO r_ rc re )r\ r` ra rg rb )Zpandas.io.formats.excelrh �write)rC Z excel_writerr\ r; r] rQ r^ rO r_ r` ra rb rc rd re rf rg rh r@ r+ r+ r, �to_excelq s$ zStyler.to_excelF�c�r) � column_format�position�position_float�hrules�labelr8 rH rI �multirow_align�multicol_align�siunitxrd � convert_cssz FilePathOrBuffer[str] | None)�bufrn ro rp rq rr r8 rH rI rs rt ru rd rv c C s | j dd�}| jdk r&dd� | jD �ng }|dk rP|jdd|� �d�gd d � n�d|krZn�| jj}tt| jj�d �| j_| j�� j�� }|| j_| j r�d n d | jj j }x>t | jj�D ].\}}|| j kr�|||kr�| s�dq�dnd 7 }q�W |jdd|� �d�gd d � |�r&|jdd|� �d�gd d � |�rb|dk�rFtd|� d���|jdd|� �d�gd d � |�r�|jddd�ddd�ddd�gd d � |�r�|jdd |�dd �� d �d�gd d � |�r�|�|� |dk�r�td �}| dk�r�td �} |j|| | | |d!�}t||| d"�S )#u4 Write Styler to a file, buffer or string in LaTeX format. .. versionadded:: 1.3.0 Parameters ---------- buf : str, Path, or StringIO-like, optional, default None Buffer to write to. If ``None``, the output is returned as a string. column_format : str, optional The LaTeX column specification placed in location: \\begin{tabular}{<column_format>} Defaults to 'l' for index and non-numeric data columns, and, for numeric data columns, to 'r' by default, or 'S' if ``siunitx`` is ``True``. position : str, optional The LaTeX positional argument (e.g. 'h!') for tables, placed in location: \\begin{table}[<position>] position_float : {"centering", "raggedleft", "raggedright"}, optional The LaTeX float command placed in location: \\begin{table}[<position>] \\<position_float> hrules : bool, default False Set to `True` to add \\toprule, \\midrule and \\bottomrule from the {booktabs} LaTeX package. label : str, optional The LaTeX label included as: \\label{<label>}. This is used with \\ref{<label>} in the main .tex file. caption : str, tuple, optional If string, the LaTeX table caption included as: \\caption{<caption>}. If tuple, i.e ("full caption", "short caption"), the caption included as: \\caption[<caption[1]>]{<caption[0]>}. sparse_index : bool, optional Whether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each row. Defaults to ``pandas.options.styler.sparse.index`` value. sparse_columns : bool, optional Whether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each row. Defaults to ``pandas.options.styler.sparse.columns`` value. multirow_align : {"c", "t", "b"} If sparsifying hierarchical MultiIndexes whether to align text centrally, at the top or bottom. multicol_align : {"r", "c", "l"} If sparsifying hierarchical MultiIndex columns whether to align text at the left, centrally, or at the right. siunitx : bool, default False Set to ``True`` to structure LaTeX compatible with the {siunitx} package. encoding : str, default "utf-8" Character encoding setting. convert_css : bool, default False Convert simple cell-styles from CSS to LaTeX format. Any CSS not found in conversion table is dropped. A style can be forced by adding option `--latex`. See notes. Returns ------- str or None If `buf` is None, returns the result as a string. Otherwise returns `None`. See Also -------- Styler.format: Format the text display value of cells. Notes ----- **Latex Packages** For the following features we recommend the following LaTeX inclusions: ===================== ========================================================== Feature Inclusion ===================== ========================================================== sparse columns none: included within default {tabular} environment sparse rows \\usepackage{multirow} hrules \\usepackage{booktabs} colors \\usepackage[table]{xcolor} siunitx \\usepackage{siunitx} bold (with siunitx) | \\usepackage{etoolbox} | \\robustify\\bfseries | \\sisetup{detect-all = true} *(within {document})* italic (with siunitx) | \\usepackage{etoolbox} | \\robustify\\itshape | \\sisetup{detect-all = true} *(within {document})* ===================== ========================================================== **Cell Styles** LaTeX styling can only be rendered if the accompanying styling functions have been constructed with appropriate LaTeX commands. All styling functionality is built around the concept of a CSS ``(<attribute>, <value>)`` pair (see `Table Visualization <../../user_guide/style.ipynb>`_), and this should be replaced by a LaTeX ``(<command>, <options>)`` approach. Each cell will be styled individually using nested LaTeX commands with their accompanied options. For example the following code will highlight and bold a cell in HTML-CSS: >>> df = pd.DataFrame([[1,2], [3,4]]) >>> s = df.style.highlight_max(axis=None, ... props='background-color:red; font-weight:bold;') >>> s.render() The equivalent using LaTeX only commands is the following: >>> s = df.style.highlight_max(axis=None, ... props='cellcolor:{red}; bfseries: ;') >>> s.to_latex() Internally these structured LaTeX ``(<command>, <options>)`` pairs are translated to the ``display_value`` with the default structure: ``\<command><options> <display_value>``. Where there are multiple commands the latter is nested recursively, so that the above example highlighed cell is rendered as ``\cellcolor{red} \bfseries 4``. Occasionally this format does not suit the applied command, or combination of LaTeX packages that is in use, so additional flags can be added to the ``<options>``, within the tuple, to result in different positions of required braces (the **default** being the same as ``--nowrap``): =================================== ============================================ Tuple Format Output Structure =================================== ============================================ (<command>,<options>) \\<command><options> <display_value> (<command>,<options> ``--nowrap``) \\<command><options> <display_value> (<command>,<options> ``--rwrap``) \\<command><options>{<display_value>} (<command>,<options> ``--wrap``) {\\<command><options> <display_value>} (<command>,<options> ``--lwrap``) {\\<command><options>} <display_value> (<command>,<options> ``--dwrap``) {\\<command><options>}{<display_value>} =================================== ============================================ For example the `textbf` command for font-weight should always be used with `--rwrap` so ``('textbf', '--rwrap')`` will render a working cell, wrapped with braces, as ``\textbf{<display_value>}``. A more comprehensive example is as follows: >>> df = pd.DataFrame([[1, 2.2, "dogs"], [3, 4.4, "cats"], [2, 6.6, "cows"]], ... index=["ix1", "ix2", "ix3"], ... columns=["Integers", "Floats", "Strings"]) >>> s = df.style.highlight_max( ... props='cellcolor:[HTML]{FFFF00}; color:{red};' ... 'textit:--rwrap; textbf:--rwrap;' ... ) >>> s.to_latex() .. figure:: ../../_static/style/latex_1.png **Table Styles** Internally Styler uses its ``table_styles`` object to parse the ``column_format``, ``position``, ``position_float``, and ``label`` input arguments. These arguments are added to table styles in the format: .. code-block:: python set_table_styles([ {"selector": "column_format", "props": f":{column_format};"}, {"selector": "position", "props": f":{position};"}, {"selector": "position_float", "props": f":{position_float};"}, {"selector": "label", "props": f":{{{label.replace(':','§')}}};"} ], overwrite=False) Exception is made for the ``hrules`` argument which, in fact, controls all three commands: ``toprule``, ``bottomrule`` and ``midrule`` simultaneously. Instead of setting ``hrules`` to ``True``, it is also possible to set each individual rule definition, by manually setting the ``table_styles``, for example below we set a regular ``toprule``, set an ``hline`` for ``bottomrule`` and exclude the ``midrule``: .. code-block:: python set_table_styles([ {'selector': 'toprule', 'props': ':toprule;'}, {'selector': 'bottomrule', 'props': ':hline;'}, ], overwrite=False) If other ``commands`` are added to table styles they will be detected, and positioned immediately above the '\\begin{tabular}' command. For example to add odd and even row coloring, from the {colortbl} package, in format ``\rowcolors{1}{pink}{red}``, use: .. code-block:: python set_table_styles([ {'selector': 'rowcolors', 'props': ':{1}{pink}{red};'} ], overwrite=False) A more comprehensive example using these arguments is as follows: >>> df.columns = pd.MultiIndex.from_tuples([ ... ("Numeric", "Integers"), ... ("Numeric", "Floats"), ... ("Non-Numeric", "Strings") ... ]) >>> df.index = pd.MultiIndex.from_tuples([ ... ("L0", "ix1"), ("L0", "ix2"), ("L1", "ix3") ... ]) >>> s = df.style.highlight_max( ... props='cellcolor:[HTML]{FFFF00}; color:{red}; itshape:; bfseries:;' ... ) >>> s.to_latex( ... column_format="rrrrr", position="h", position_float="centering", ... hrules=True, label="table:5", caption="Styled LaTeX Table", ... multirow_align="t", multicol_align="r" ... ) .. figure:: ../../_static/style/latex_2.png **Formatting** To format values :meth:`Styler.format` should be used prior to calling `Styler.to_latex`, as well as other methods such as :meth:`Styler.hide_index` or :meth:`Styler.hide_columns`, for example: >>> s.clear() >>> s.table_styles = [] >>> s.caption = None >>> s.format({ ... ("Numeric", "Integers"): '\${}', ... ("Numeric", "Floats"): '{:.3f}', ... ("Non-Numeric", "Strings"): str.upper ... }) >>> s.to_latex() \begin{tabular}{llrrl} {} & {} & \multicolumn{2}{r}{Numeric} & {Non-Numeric} \\ {} & {} & {Integers} & {Floats} & {Strings} \\ \multirow[c]{2}{*}{L0} & ix1 & \\$1 & 2.200 & DOGS \\ & ix2 & \$3 & 4.400 & CATS \\ L1 & ix3 & \$2 & 6.600 & COWS \\ \end{tabular} **CSS Conversion** This method can convert a Styler constructured with HTML-CSS to LaTeX using the following limited conversions. ================== ==================== ============= ========================== CSS Attribute CSS value LaTeX Command LaTeX Options ================== ==================== ============= ========================== font-weight | bold | bfseries | bolder | bfseries font-style | italic | itshape | oblique | slshape background-color | red cellcolor | {red}--lwrap | #fe01ea | [HTML]{FE01EA}--lwrap | #f0e | [HTML]{FF00EE}--lwrap | rgb(128,255,0) | [rgb]{0.5,1,0}--lwrap | rgba(128,0,0,0.5) | [rgb]{0.5,0,0}--lwrap | rgb(25%,255,50%) | [rgb]{0.25,1,0.5}--lwrap color | red color | {red} | #fe01ea | [HTML]{FE01EA} | #f0e | [HTML]{FF00EE} | rgb(128,255,0) | [rgb]{0.5,1,0} | rgba(128,0,0,0.5) | [rgb]{0.5,0,0} | rgb(25%,255,50%) | [rgb]{0.25,1,0.5} ================== ==================== ============= ========================== It is also possible to add user-defined LaTeX only styles to a HTML-CSS Styler using the ``--latex`` flag, and to add LaTeX parsing options that the converter will detect within a CSS-comment. >>> df = pd.DataFrame([[1]]) >>> df.style.set_properties( ... **{"font-weight": "bold /* --dwrap */", "Huge": "--latex--rwrap"} ... ).to_latex(convert_css=True) \begin{tabular}{lr} {} & {0} \\ 0 & {\bfseries}{\Huge{1}} \\ \end{tabular} T)�deepcopyNc S s g | ] }|d �qS )�selectorr+ )�.0�styler+ r+ r, � <listcomp>� s z#Styler.to_latex.<locals>.<listcomp>rn �:)ry rL F)� overwrite)�stoprY �lrm �Sro )Z raggedrightZ raggedleftZ centeringzR`position_float` should be one of 'raggedright', 'raggedleft', 'centering', got: '�'rp Ztoprulez:topruleZmidrulez:midruleZ bottomrulez :bottomrulerr z:{� §�}zstyler.sparse.indexzstyler.sparse.columns)rH rI rs rt rv )rw rd )�_copyr6 �set_table_stylesr4 rQ r �lenZ_get_numeric_dataZto_list� hide_index_rO �nlevels� enumerate�hidden_columns� ValueError�replace� set_captionr Z _render_latexr )rC rw rn ro rp rq rr r8 rH rI rs rt ru rd rv �objZtable_selectorsZ_original_columnsZ numeric_cols�ci�_�latexr+ r+ r, �to_latex� sr * zStyler.to_latex)� table_uuidr9 rd � doctype_html�exclude_styles)rw r� r9 rd r� r� c C sN |r| � |� |r | �|� | j||r*|nd|d�}t|||dk rF|ndd�S )aU Write Styler to a file, buffer or string in HTML-CSS format. .. versionadded:: 1.3.0 Parameters ---------- buf : str, Path, or StringIO-like, optional, default None Buffer to write to. If ``None``, the output is returned as a string. table_uuid : str, optional Id attribute assigned to the <table> HTML element in the format: ``<table id="T_<table_uuid>" ..>`` If not given uses Styler's initially assigned value. table_attributes : str, optional Attributes to assign within the `<table>` HTML element in the format: ``<table .. <table_attributes> >`` If not given defaults to Styler's preexisting value. encoding : str, optional Character encoding setting for file output, and HTML meta tags, defaults to "utf-8" if None. doctype_html : bool, default False Whether to output a fully structured HTML file including all HTML elements, or just the core ``<style>`` and ``<table>`` elements. exclude_styles : bool, default False Whether to include the ``<style>`` element and all associated element ``class`` and ``id`` identifiers, or solely the ``<table>`` element without styling identifiers. Returns ------- str or None If `buf` is None, returns the result as a string. Otherwise returns `None`. See Also -------- DataFrame.to_html: Write a DataFrame to a file, buffer or string in HTML format. zutf-8)r� rd r� N)rw rd )�set_uuid�set_table_attributesrF r )rC rw r� r9 rd r� r� �htmlr+ r+ r, �to_html s 3 zStyler.to_html)�classesrE c C s� |j jr|jjstd��|�| j�}xZt|�� �D ]J\}}x@t|dd� �D ],\}}t� |�sL|dksLt |�| j ||f< qLW q2W | S )a� Set the DataFrame of strings added to the ``class`` attribute of ``<td>`` HTML elements. Parameters ---------- classes : DataFrame DataFrame containing strings that will be translated to CSS classes, mapped by identical column and index key values that must exist on the underlying Styler data. None, NaN values, and empty strings will be ignored and not affect the rendered HTML. Returns ------- self : Styler See Also -------- Styler.set_table_styles: Set the table styles included within the ``<style>`` HTML element. Styler.set_table_attributes: Set the table attributes added to the ``<table>`` HTML element. Notes ----- Can be used in combination with ``Styler.set_table_styles`` to define an internal CSS solution without reference to external CSS files. Examples -------- >>> df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"]) >>> classes = pd.DataFrame([ ... ["min-val red", "", "blue"], ... ["red", None, "blue max-val"] ... ], index=df.index, columns=df.columns) >>> df.style.set_td_classes(classes) Using `MultiIndex` columns and a `classes` `DataFrame` as a subset of the underlying, >>> df = pd.DataFrame([[1,2],[3,4]], index=["a", "b"], ... columns=[["level0", "level0"], ["level1a", "level1b"]]) >>> classes = pd.DataFrame(["min-val"], index=["a"], ... columns=[["level0"],["level1a"]]) >>> df.style.set_td_classes(classes) Form of the output with new additional css classes, >>> df = pd.DataFrame([[1]]) >>> css = pd.DataFrame([["other-class"]]) >>> s = Styler(df, uuid="_", cell_ids=False).set_td_classes(css) >>> s.hide_index().render() '<style type="text/css"></style>' '<table id="T__">' ' <thead>' ' <tr><th class="col_heading level0 col0" >0</th></tr>' ' </thead>' ' <tbody>' ' <tr><td class="data row0 col0 other-class" >1</td></tr>' ' </tbody>' '</table>' z>Classes render only if `classes` has unique index and columns.� NrY ) rO rP rQ rR � reindex_liker4 r� � itertuples�pd�isnar3 � cell_context)rC r� rm Zrow_tuprl �valuer+ r+ r, �set_td_classesa s ? zStyler.set_td_classes)�attrsrE c C s� | j jr| jjstd��xf|jD ]\}xV||g �� D ]D\}}|sBq4t|�}| j �|�| j�|� }}| j||f �|� q4W q W dS )a� Update the state of the ``Styler`` for data cells. 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. zS`Styler.apply` and `.applymap` are not compatible with non-unique index or columns.N) rO rP rQ rR r� r! Zget_loc�ctx�extend)rC r� ZcnZrnrl Zcss_list�i�jr+ r+ r, � _update_ctx� s zStyler._update_ctx)rx rE c C s� t | j| jd�}dddddg}ddd d d d d dg}x |D ]}t||t| |�� q8W x0|D ](}t| |�}t|||rzt�|�n|� qXW |S )a� Copies a Styler, allowing for deepcopy or shallow copy Copying a Styler aims to recreate a new Styler object which contains the same data and styles as the original. Data dependent attributes [copied and NOT exported]: - formatting (._display_funcs) - hidden index values or column values (.hidden_rows, .hidden_columns) - tooltips - cell_context (cell css classes) - ctx (cell css styles) - caption Non-data dependent attributes [copied and exported]: - hidden index state and hidden columns state (.hide_index_, .hide_columns_) - table_attributes - table_styles - applied styles (_todo) )r< r� � hide_columns_r9 r: r8 Z_display_funcs� hidden_rowsr� r� r� �_todor6 rS )r. r4 r< �setattr�getattr�copyrx )rC rx ZstylerZshallow�deep�attr�valr+ r+ r, r� � s, z Styler._copyc C s | j dd�S )NF)rx )r� )rC r+ r+ r, �__copy__� s zStyler.__copy__c C s | j dd�S )NT)rx )r� )rC �memor+ r+ r, � __deepcopy__ s zStyler.__deepcopy__c C s4 | j �� d| _| j�� | j�� d| _g | _dS )zf Reset the ``Styler``, removing any previously applied styles. Returns None. NF)r� �clearrS r� r� r� r� )rC r+ r+ r, r� s z Styler.clearzCallable[..., Styler]z Axis | Nonez Subset | None)r$ �axis�subsetrE c K s8 |d krt d �n|}t|�}| jj| }|d k rR|j|f|dd�|��}|j|_n�||f|�}t|t�s�t|tj �s�t dt |�� d���|j |j ks�t dt |�� d|j � d|j � ���t||j|jd�}n0|j�|j�r�|j�|j�s�t dt |�� d ���|j |j k�r*t dt |�� d |j � d |j � ���| �|� | S ) N�expand)r� Z result_typez Function zP must return a DataFrame or ndarray when passed to `Styler.apply` with axis=Nonez6 returned ndarray with wrong shape. Result has shape: z Expected shape: )rO rQ z Result of z3 must have identical index and columns as the inputz- returned the wrong shape. Result has shape: z Expected shape: )�slicer" r4 �loc�applyrQ � isinstancer �np�ndarray� TypeError�repr�shaper� rO �equalsr� )rC r$ r� r� rJ r4 �resultr+ r+ r, �_apply s. z Styler._applyc K s | j �dd� |||f|f� | S )a� Apply a CSS-styling function column-wise, row-wise, or table-wise. Updates the HTML representation with the result. Parameters ---------- func : function ``func`` should take a Series if ``axis`` in [0,1] and return an object of same length, also with identical index if the object is a Series. ``func`` should take a DataFrame if ``axis`` is ``None`` and return either an ndarray with the same shape or a DataFrame with identical columns and index. .. versionchanged:: 1.3.0 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 : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. **kwargs : dict Pass along to ``func``. Returns ------- self : Styler See Also -------- Styler.applymap: Apply a CSS-styling function elementwise. Notes ----- The elements of the output of ``func`` should be CSS styles as strings, in the format 'attribute: value; attribute2: value2; ...' or, if nothing is to be applied to that element, an empty string or ``None``. 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, color): ... return np.where(x == np.nanmax(x.to_numpy()), f"color: {color};", None) >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"]) >>> df.style.apply(highlight_max, color='red') >>> df.style.apply(highlight_max, color='blue', axis=1) >>> df.style.apply(highlight_max, color='green', axis=None) Using ``subset`` to restrict application to a single column or multiple columns >>> df.style.apply(highlight_max, color='red', subset="A") >>> df.style.apply(highlight_max, color='red', subset=["A", "B"]) Using a 2d input to ``subset`` to select rows in addition to columns >>> df.style.apply(highlight_max, color='red', subset=([0,1,2], slice(None)) >>> df.style.apply(highlight_max, color='red', subset=(slice(0,5,2), "A") c S s t | d�S )Nr� )r� )�instancer+ r+ r, �<lambda>� � z Styler.apply.<locals>.<lambda>)r� �append)rC r$ r� r� rJ r+ r+ r, r� A s Gz Styler.applyr )r$ r� rE c K sH t |f|�}|d kr td d � }t|�}| jj| �|�}| �|� | S )N)r r r" r4 r� �applymapr� )rC r$ r� rJ r� r+ r+ r, � _applymap� s zStyler._applymapc K s | j �dd� ||f|f� | S )a Apply a CSS-styling function elementwise. Updates the HTML representation with the result. Parameters ---------- func : function ``func`` should take a scalar and return a scalar. subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. **kwargs : dict Pass along to ``func``. Returns ------- self : Styler See Also -------- Styler.apply: Apply a CSS-styling function column-wise, row-wise, or table-wise. Notes ----- The elements of the output of ``func`` should be CSS styles as strings, in the format 'attribute: value; attribute2: value2; ...' or, if nothing is to be applied to that element, an empty string or ``None``. Examples -------- >>> def color_negative(v, color): ... return f"color: {color};" if v < 0 else None >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"]) >>> df.style.applymap(color_negative, color='red') Using ``subset`` to restrict application to a single column or multiple columns >>> df.style.applymap(color_negative, color='red', subset="A") >>> df.style.applymap(color_negative, color='red', subset=["A", "B"]) Using a 2d input to ``subset`` to select rows in addition to columns >>> df.style.applymap(color_negative, color='red', subset=([0,1,2], slice(None)) >>> df.style.applymap(color_negative, color='red', subset=(slice(0,5,2), "A") c S s t | d�S )Nr� )r� )r� r+ r+ r, r� � r� z!Styler.applymap.<locals>.<lambda>)r� r� )rC r$ r� rJ r+ r+ r, r� � s 2zStyler.applymap)�condr� �otherr� rE c s8 t jdtdd� �dkr d�| j� ���fdd�|d�S ) a' Apply CSS-styles based on a conditional function elementwise. .. deprecated:: 1.3.0 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 optional keyword arguments, and return a boolean. value : str Applied when ``cond`` returns true. other : str Applied when ``cond`` returns false. subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. **kwargs : dict Pass along to ``cond``. Returns ------- self : Styler See Also -------- Styler.applymap: Apply a CSS-styling function elementwise. Styler.apply: Apply a CSS-styling function column-wise, row-wise, or table-wise. Notes ----- This method is deprecated. This method is a convenience wrapper for :meth:`Styler.applymap`, which we recommend using instead. The example: >>> df = pd.DataFrame([[1, 2], [3, 4]]) >>> def cond(v, limit=4): ... return v > 1 and v != limit >>> df.style.where(cond, value='color:green;', other='color:red;') should be refactored to: >>> def style_func(v, value, other, limit=4): ... cond = v > 1 and v != limit ... return value if cond else other >>> df.style.applymap(style_func, value='color:green;', other='color:red;') z:this method is deprecated in favour of `Styler.applymap()`� )� stacklevelNrY c s � | f��r�S �S )Nr+ )r� )r� rJ r� r� r+ r, r� r� z Styler.where.<locals>.<lambda>)r� )�warnings�warn� FutureWarningr� )rC r� r� r� r� rJ r+ )r� rJ r� r� r, �where� s >z Styler.wherer )r5 rE c C s& t jdtdd� || _| j|| jd�S )a$ Set the precision used to display values. .. deprecated:: 1.3.0 Parameters ---------- precision : int Returns ------- self : Styler Notes ----- This method is deprecated see `Styler.format`. zDthis method is deprecated in favour of `Styler.format(precision=..)`r� )r� )r5 r; )r� r� r� r5 r) r; )rC r5 r+ r+ r, � set_precision s zStyler.set_precision)� attributesrE c C s || _ | S )a� Set the table attributes added to the ``<table>`` HTML element. These are items in addition to automatic (by default) ``id`` attribute. Parameters ---------- attributes : str Returns ------- self : Styler See Also -------- Styler.set_table_styles: Set the table styles included within the ``<style>`` HTML element. Styler.set_td_classes: Set the DataFrame of strings added to the ``class`` attribute of ``<td>`` HTML elements. Examples -------- >>> df = pd.DataFrame(np.random.randn(10, 4)) >>> df.style.set_table_attributes('class="pure-table"') # ... <table class="pure-table"> ... )r9 )rC r� r+ r+ r, r� 5 s zStyler.set_table_attributesz"list[tuple[Callable, tuple, dict]]c C s | j S )a Export the styles applied to the current ``Styler``. Can be applied to a second Styler with ``Styler.use``. Returns ------- styles : list See Also -------- Styler.use: Set the styles on the current ``Styler``. )r� )rC r+ r+ r, �exportS s z Styler.export)�stylesrE c C s | j �|� | S )a} 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 : Export the styles to applied to the current ``Styler``. )r� r� )rC r� r+ r+ r, �usec s z Styler.use)r7 rE c C s || _ | S )a� Set the uuid applied to ``id`` attributes of HTML elements. Parameters ---------- uuid : str Returns ------- self : Styler Notes ----- Almost all HTML elements within the table, and including the ``<table>`` element are assigned ``id`` attributes. The format is ``T_uuid_<extra>`` where ``<extra>`` is typically a more specific identifier, such as ``row1_col2``. )r7 )rC r7 r+ r+ r, r� y s zStyler.set_uuidz str | tuple)r8 rE c C s || _ | S )a� Set the text added to a ``<caption>`` HTML element. Parameters ---------- caption : str, tuple For HTML output either the string input is used or the first element of the tuple. For LaTeX the string input provides a caption and the additional tuple input allows for full captions and short captions, in that order. Returns ------- self : Styler )r8 )rC r8 r+ r+ r, r� � s zStyler.set_captionr zlist[int] | None)r� � pixel_size�levelsrE c C s, |dkr$d| j j }}|s dn|}n,|dkrHd| j j }}|sBdn|}ntd��d}t|tj�s�|dkr�d |d d �g}| jjd d k r�|d |� d� |d d< |�d|d|� d|� d� d �� n d|d d �d|d d �g}�n:t t |j ��}|�r t |�n|}|dk�r�g }xHt |�D ]<\}} |�d| d � d�|d|| � d|� d� d �� �q W td d � | jjD ���s |�d|j d � d�|d|d | � d|� d� d �� npg }xjt |�D ]^\}} |d || � d |� d |� d� } |�d!| d � d"�| d# d �d$| � �| d% d �g� �q�W | j|d&d'�S )(a4 Add CSS to permanently display the index or column headers in a scrolling frame. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Whether to make the index or column headers sticky. pixel_size : int, optional Required to configure the width of index cells or the height of column header cells when sticking a MultiIndex (or with a named Index). Defaults to 75 and 25 respectively. levels : list of int If ``axis`` is a MultiIndex the specific levels to stick. If ``None`` will stick all levels. Returns ------- self : Styler Notes ----- This method uses the CSS 'position: sticky;' property to display. It is designed to work with visible axes, therefore both: - `styler.set_sticky(axis="index").hide_index()` - `styler.set_sticky(axis="columns").hide_columns()` may produce strange behaviour due to CSS controls with missing elements. )r rO r �K )r� rQ r� � z0`axis` must be one of {0, 1, 'index', 'columns'}z(position:sticky; background-color:white;zthead tr:nth-child(1) thztop:0px; z-index:2;)ry rL Nztop:0px; z-index:2; height:zpx;rL zthead tr:nth-child(2) thztop:zpx; z-index:2; height:zpx; zthead tr th:nth-child(1)z left:0px; z-index:3 !important;ztbody tr th:nth-child(1)zleft:0px; z-index:1;zthead tr:nth-child(z) thz px; height:zpx; z-index:2;c s s | ]}|d kV qd S )Nr+ )rz �namer+ r+ r, � <genexpr> s z$Styler.set_sticky.<locals>.<genexpr>zleft:zpx; min-width:zpx; max-width:zthead tr th:nth-child(�)zz-index:3 !important;ztbody tr th.levelz z-index:1;F)r~ )r4 rO rQ r� r� r� Z MultiIndex�namesr� �list�ranger� �sortedr� �allr� r� ) rC r� r� r� r� rL r� Z range_idxr� �levelZprops_r+ r+ r, � set_sticky� s\ # $$ zStyler.set_stickyz dict[Any, CSSStyles] | CSSStyles)r6 r� r~ rE c s~ t |t�rJ|dkr"| jjd �� n| jjd �� � �fdd�|�� D �}ndd� |D �}|st| jdk rt| j�|� n|| _| S )a� Set the table styles included within the ``<style>`` HTML element. This function can be used to style the entire table, columns, rows or specific HTML selectors. Parameters ---------- table_styles : list or dict If supplying a 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)``. If supplying a dict, the dict keys should correspond to column names or index values, depending upon the specified `axis` argument. These will be mapped to row or col CSS selectors. MultiIndex values as dict keys should be in their respective tuple form. The dict values should be a list as specified in the form with CSS selectors and props that will be applied to the specified row or column. .. versionchanged:: 1.2.0 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'``). Only used if `table_styles` is dict. .. versionadded:: 1.2.0 overwrite : bool, default True Styles are replaced if `True`, or extended if `False`. CSS rules are preserved so most recent styles set will dominate if selectors intersect. .. versionadded:: 1.2.0 Returns ------- self : Styler See Also -------- Styler.set_td_classes: Set the DataFrame of strings added to the ``class`` attribute of ``<td>`` HTML elements. Styler.set_table_attributes: Set the table attributes added to the ``<table>`` HTML element. Examples -------- >>> df = pd.DataFrame(np.random.randn(10, 4), ... columns=['A', 'B', 'C', 'D']) >>> df.style.set_table_styles( ... [{'selector': 'tr:hover', ... 'props': [('background-color', 'yellow')]}] ... ) Or with CSS strings >>> df.style.set_table_styles( ... [{'selector': 'tr:hover', ... 'props': 'background-color: yellow; font-size: 1em;']}] ... ) Adding column styling by name >>> df.style.set_table_styles({ ... 'A': [{'selector': '', ... 'props': [('color', 'red')]}], ... 'B': [{'selector': 'td', ... 'props': 'color: blue;']}] ... }, overwrite=False) Adding row styling >>> df.style.set_table_styles({ ... 0: [{'selector': 'td:hover', ... 'props': [('font-size', '25px')]}] ... }, axis=1, overwrite=False) )r rO z.colz.rowc sR g | ]J\}}�� |g�D ]4}|D ]*}t|d �� t|� t|d �d��q qqS )ry rL )ry rL )�get_indexer_forr3 r! )rz �keyr� �idx�s)�idfr� r+ r, r| � s z+Styler.set_table_styles.<locals>.<listcomp>c S s" g | ]}|d t |d �d��qS )ry rL )ry rL )r! )rz r� r+ r+ r, r| � s N)r� �dictr4 rQ rO �itemsr6 r� )rC r6 r� r~ r+ )r� r� r, r� ( s W zStyler.set_table_styles)r; rE c C s& t jdtdd� || _| j|| jd�S )aO Set the missing data representation on a ``Styler``. .. versionadded:: 1.0.0 .. deprecated:: 1.3.0 Parameters ---------- na_rep : str Returns ------- self : Styler Notes ----- This method is deprecated. See `Styler.format()` zAthis method is deprecated in favour of `Styler.format(na_rep=..)`r� )r� )r; r5 )r� r� r� r; r) r5 )rC r; r+ r+ r, � set_na_rep� s zStyler.set_na_rep)r� rE c C sL |dkrd| _ n8t|dd�f }t|�}| jj| }| j�|j�}|| _| S )a� Hide the entire index, or specific keys in the index from rendering. This method has dual functionality: - if ``subset`` is ``None`` then the entire index will be hidden whilst displaying all data-rows. - if a ``subset`` is given then those specific rows will be hidden whilst the index itself remains visible. .. versionchanged:: 1.3.0 Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 1d input or single key along the index axis within `DataFrame.loc[<subset>, :]`, to limit ``data`` to *before* applying the function. Returns ------- self : Styler See Also -------- Styler.hide_columns: Hide the entire column headers row, or specific columns. Examples -------- Simple application hiding specific rows: >>> df = pd.DataFrame([[1,2], [3,4], [5,6]], index=["a", "b", "c"]) >>> df.style.hide_index(["a", "b"]) 0 1 c 5 6 Hide the index and retain the data values: >>> midx = pd.MultiIndex.from_product([["x", "y"], ["a", "b", "c"]]) >>> df = pd.DataFrame(np.random.randn(6,6), index=midx, columns=midx) >>> df.style.format("{:.1f}").hide_index() x y a b c a b c 0.1 0.0 0.4 1.3 0.6 -1.4 0.7 1.0 1.3 1.5 -0.0 -0.2 1.4 -0.8 1.6 -0.2 -0.4 -0.3 0.4 1.0 -0.2 -0.8 -1.2 1.1 -0.6 1.2 1.8 1.9 0.3 0.3 0.8 0.5 -0.3 1.2 2.2 -0.8 Hide specific rows but retain the index: >>> df.style.format("{:.1f}").hide_index(subset=(slice(None), ["a", "c"])) x y a b c a b c x b 0.7 1.0 1.3 1.5 -0.0 -0.2 y b -0.6 1.2 1.8 1.9 0.3 0.3 Hide specific rows and the index: >>> df.style.format("{:.1f}").hide_index(subset=(slice(None), ["a", "c"])) ... .hide_index() x y a b c a b c 0.7 1.0 1.3 1.5 -0.0 -0.2 -0.6 1.2 1.8 1.9 0.3 0.3 NT)r� r r" r4 r� rO r� r� )rC r� �subset_�hideZhrowsr+ r+ r, � hide_index� s D zStyler.hide_indexc C sL |dkrd| _ n8tdd�|f }t|�}| jj| }| j�|j�}|| _| S )a� Hide the column headers or specific keys in the columns from rendering. This method has dual functionality: - if ``subset`` is ``None`` then the entire column headers row will be hidden whilst the data-values remain visible. - if a ``subset`` is given then those specific columns, including the data-values will be hidden, whilst the column headers row remains visible. .. versionchanged:: 1.3.0 Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 1d input or single key along the columns axis within `DataFrame.loc[:, <subset>]`, to limit ``data`` to *before* applying the function. Returns ------- self : Styler See Also -------- Styler.hide_index: Hide the entire index, or specific keys in the index. Examples -------- Simple application hiding specific columns: >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"]) >>> df.style.hide_columns(["a", "b"]) c 0 3 1 6 Hide column headers and retain the data values: >>> midx = pd.MultiIndex.from_product([["x", "y"], ["a", "b", "c"]]) >>> df = pd.DataFrame(np.random.randn(6,6), index=midx, columns=midx) >>> df.style.format("{:.1f}").hide_columns() x d 0.1 0.0 0.4 1.3 0.6 -1.4 e 0.7 1.0 1.3 1.5 -0.0 -0.2 f 1.4 -0.8 1.6 -0.2 -0.4 -0.3 y d 0.4 1.0 -0.2 -0.8 -1.2 1.1 e -0.6 1.2 1.8 1.9 0.3 0.3 f 0.8 0.5 -0.3 1.2 2.2 -0.8 Hide specific columns but retain the column headers: >>> df.style.format("{:.1f}").hide_columns(subset=(slice(None), ["a", "c"])) x y b b x a 0.0 0.6 b 1.0 -0.0 c -0.8 -0.4 y a 1.0 -1.2 b 1.2 0.3 c 0.5 2.2 Hide specific columns and the column headers: >>> df.style.format("{:.1f}").hide_columns(subset=(slice(None), ["a", "c"])) ... .hide_columns() x a 0.0 0.6 b 1.0 -0.0 c -0.8 -0.4 y a 1.0 -1.2 b 1.2 0.3 c 0.5 2.2 NT)r� r r" r4 r� rQ r� r� )rC r� r� r� Zhcolsr+ r+ r, � hide_columns s I zStyler.hide_columns� background�text�bgz${0 or 'index', 1 or 'columns', None})r� ZaltZ image_prefixr� Ztext_threshold�PuBu��&1� �?�floatz float | NonezSequence | None) �low�highr� r� �text_color_threshold�vmin�vmax�gmaprE c C sD |dkr"| dkr"| j jtjd�j}| jt||||||||| d� | S )ap Color the {name} in a gradient style. The {name} color is determined according to the data in each column, row or frame, or by a given gradient map. Requires matplotlib. Parameters ---------- cmap : str or colormap Matplotlib colormap. low : float Compress the color range at the low end. This is a multiple of the data range to extend below the minimum; good values usually in [0, 1], defaults to 0. high : float Compress the color range at the high end. This is a multiple of the data range to extend above the maximum; good values usually in [0, 1], defaults to 0. axis : {axis}, 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 : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. text_color_threshold : float or int {text_threshold} Luminance threshold for determining text color in [0, 1]. Facilitates text visibility across varying background colors. All text is dark if 0, and light if 1, defaults to 0.408. vmin : float, optional Minimum data value that corresponds to colormap minimum value. If not specified the minimum value of the data (or gmap) will be used. .. versionadded:: 1.0.0 vmax : float, optional Maximum data value that corresponds to colormap maximum value. If not specified the maximum value of the data (or gmap) will be used. .. versionadded:: 1.0.0 gmap : array-like, optional Gradient map for determining the {name} colors. If not supplied will use the underlying data from rows, columns or frame. If given as an ndarray or list-like must be an identical shape to the underlying data considering ``axis`` and ``subset``. If given as DataFrame or Series must have same index and column labels considering ``axis`` and ``subset``. If supplied, ``vmin`` and ``vmax`` should be given relative to this gradient map. .. versionadded:: 1.3.0 Returns ------- self : Styler See Also -------- Styler.{alt}_gradient: Color the {alt} in a gradient style. Notes ----- When using ``low`` and ``high`` the range of the gradient, given by the data if ``gmap`` is not given or by ``gmap``, is extended at the low end effectively by `map.min - low * map.range` and at the high end by `map.max + high * map.range` before the colors are normalized and determined. If combining with ``vmin`` and ``vmax`` the `map.min`, `map.max` and `map.range` are replaced by values according to the values derived from ``vmin`` and ``vmax``. This method will preselect numeric columns and ignore non-numeric columns unless a ``gmap`` is supplied in which case no preselection occurs. Examples -------- >>> df = pd.DataFrame(columns=["City", "Temp (c)", "Rain (mm)", "Wind (m/s)"], ... data=[["Stockholm", 21.6, 5.0, 3.2], ... ["Oslo", 22.4, 13.3, 3.1], ... ["Copenhagen", 24.5, 0.0, 6.7]]) Shading the values column-wise, with ``axis=0``, preselecting numeric columns >>> df.style.{name}_gradient(axis=0) .. figure:: ../../_static/style/{image_prefix}_ax0.png Shading all values collectively using ``axis=None`` >>> df.style.{name}_gradient(axis=None) .. figure:: ../../_static/style/{image_prefix}_axNone.png Compress the color map from the both ``low`` and ``high`` ends >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0) .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png Manually setting ``vmin`` and ``vmax`` gradient thresholds >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6) .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png Setting a ``gmap`` and applying to all columns with another ``cmap`` >>> df.style.{name}_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd') .. figure:: ../../_static/style/{image_prefix}_gmap.png Setting the gradient map for a dataframe (i.e. ``axis=None``), we need to explicitly state ``subset`` to match the ``gmap`` shape >>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]]) >>> df.style.{name}_gradient(axis=None, gmap=gmap, ... cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)'] ... ) .. figure:: ../../_static/style/{image_prefix}_axNone_gmap.png N)�include) �cmapr� r� r� r� r� r� r� r� )r4 � select_dtypesr� �numberrQ r� �_background_gradient) rC r r� r� r� r� r� r� r� r� r+ r+ r, �background_gradientb s zStyler.background_gradient�tgz>This argument is ignored (only used in `background_gradient`).)r� r� r� r� r� r� r� rE c C s@ |d kr"|d kr"| j jtjd�j}| jt||||||||dd� S )N)r� T) r r� r� r� r� r� r� r� � text_only)r4 r r� r rQ r� r ) rC r r� r� r� r� r� r� r� r+ r+ r, � text_gradient s zStyler.text_gradientc s. d� dd� |�� D ��� | j� fdd�|d�S )a� Set defined CSS-properties to each ``<td>`` HTML element within the given subset. Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. **kwargs : dict A dictionary of property, value pairs to be set for each cell. Returns ------- self : Styler Notes ----- This is a convenience methods which wraps the :meth:`Styler.applymap` calling a function returning the CSS-properties independently of the data. Examples -------- >>> df = pd.DataFrame(np.random.randn(10, 4)) >>> df.style.set_properties(color="white", align="right") >>> df.style.set_properties(**{'background-color': 'yellow'}) rY c s s" | ]\}}|� d |� d�V qdS )z: �;Nr+ )rz �p�vr+ r+ r, r� C s z(Styler.set_properties.<locals>.<genexpr>c s � S )Nr+ )�x)�valuesr+ r, r� D r� z'Styler.set_properties.<locals>.<lambda>)r� )�joinr� r� )rC r� rJ r+ )r r, �set_properties& s zStyler.set_properties�d z list[str])�alignr# �widthr� r� c s |dkrt �| �� �n|}|dkr0t �| �� �n|}� dkrRtd|�}td|�}n � dkrrtt|�t|��}| }�| jtd�| || d }� | || d �ddddd ��fd d � �� ���fd d ��| jdkr�fdd�|D �S t �fdd�|D �| j | j d�S dS )z4 Draw bar chart in dataframe cells. N�midr �zero)�dtypeg�-���q=r� r3 )�start�end�colorrE c sh d}|| krd|d7 }| dkr<|d| d�d|� d| d�d�7 }t |� �}||� d|d�d|d�d �7 }|S ) zL Generate CSS code to draw a bar from start to end. zwidth: 10em; height: 80%;z"background: linear-gradient(90deg,r z transparent z.1fz%, � z%, transparent z%))�min)r r r �css�e)r r+ r, �css_bar` s z Styler._bar.<locals>.css_barc sV t �| �rdS | �kr �d n�d }� dkr:�d| |�S �t| ��t| ��|�S d S )NrY r� r �left)r� r� r �max)r r )r r# r r r+ r, r m s zStyler._bar.<locals>.cssr� c s g | ] }� |��qS r+ r+ )rz r )r r+ r, r| z s z Styler._bar.<locals>.<listcomp>c s g | ]}� fd d�|D ��qS )c s g | ] }� |��qS r+ r+ )rz r )r r+ r, r| } s z*Styler._bar.<locals>.<listcomp>.<listcomp>r+ )rz �row)r r+ r, r| } s )rO rQ ) r� �nanmin�to_numpy�nanmaxr r �absr� �ndimr rO rQ ) r� r r# r r� r� �smin�smax�normedr+ )r r# r r r r r, �_barF s$ z Styler._bar�#d65f5fr )r� r� r r r� r� rE c C s� |dkrt d��t|�s"||g}n2t|�dkr@|d |d g}nt|�dkrTt d��|dkrn| jjtjd�j}| j| j |||||||d � | S ) a Draw bar chart in the cell backgrounds. Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. 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. 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. Returns ------- self : Styler )r r r z.`align` must be one of {'left', 'zero',' mid'}r� r r� zp`color` must be string or a list-like of length 2: [`color_neg`, `color_pos`] (eg: color=['#d65f5f', '#5fba7d'])N)r� )r� r� r r# r r� r� ) r� r r� r4 r r� r rQ r� r( )rC r� r� r r r r� r� r+ r+ r, �bar� s* 4 z Styler.bar�red)� null_colorr� rL rE c C s8 dddd�dd�}|dkr&d|� d �}| j |d||d �S ) a Highlight missing values with a style. Parameters ---------- null_color : str, default 'red' subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. .. versionadded:: 1.1.0 props : str, default None CSS properties to use for highlighting. If ``props`` is given, ``color`` is not used. .. versionadded:: 1.3.0 Returns ------- self : Styler See Also -------- Styler.highlight_max: Highlight the maximum with a style. Styler.highlight_min: Highlight the minimum with a style. Styler.highlight_between: Highlight a defined range with a style. Styler.highlight_quantile: Highlight values defined by a quantile with a style. r r3 z np.ndarray)r4 rL rE c S s t �t�| ��� |d�S )NrY )r� r� r� r� r! )r4 rL r+ r+ r, �f� s z Styler.highlight_null.<locals>.fNzbackground-color: r )r� r� rL )r� )rC r, r� rL r- r+ r+ r, �highlight_null� s % zStyler.highlight_null�yellow)r� r r� rL rE c C s. |dkrd|� d�}| j ttdd�|||d�S )a Highlight the maximum with a style. Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. color : str, default 'yellow' Background color to use for highlighting. 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``. props : str, default None CSS properties to use for highlighting. If ``props`` is given, ``color`` is not used. .. versionadded:: 1.3.0 Returns ------- self : Styler See Also -------- Styler.highlight_null: Highlight missing values with a style. Styler.highlight_min: Highlight the minimum with a style. Styler.highlight_between: Highlight a defined range with a style. Styler.highlight_quantile: Highlight values defined by a quantile with a style. Nzbackground-color: r r )�op)r� r� rL )r� r �_highlight_value)rC r� r r� rL r+ r+ r, � highlight_max s ( zStyler.highlight_maxc C s. |dkrd|� d�}| j ttdd�|||d�S )a Highlight the minimum with a style. Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. color : str, default 'yellow' Background color to use for highlighting. 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``. props : str, default None CSS properties to use for highlighting. If ``props`` is given, ``color`` is not used. .. versionadded:: 1.3.0 Returns ------- self : Styler See Also -------- Styler.highlight_null: Highlight missing values with a style. Styler.highlight_max: Highlight the maximum with a style. Styler.highlight_between: Highlight a defined range with a style. Styler.highlight_quantile: Highlight values defined by a quantile with a style. Nzbackground-color: r r )r0 )r� r� rL )r� r r1 )rC r� r r� rL r+ r+ r, � highlight_min7 s ( zStyler.highlight_min�bothzScalar | Sequence | None)r� r r� r �right� inclusiverL rE c C s, |dkrd|� d�}| j t||||||d�S )a� Highlight a defined range with a style. .. versionadded:: 1.3.0 Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. color : str, default 'yellow' Background color to use for highlighting. axis : {0 or 'index', 1 or 'columns', None}, default 0 If ``left`` or ``right`` given as sequence, axis along which to apply those boundaries. See examples. left : scalar or datetime-like, or sequence or array-like, default None Left bound for defining the range. right : scalar or datetime-like, or sequence or array-like, default None Right bound for defining the range. inclusive : {'both', 'neither', 'left', 'right'} Identify whether bounds are closed or open. props : str, default None CSS properties to use for highlighting. If ``props`` is given, ``color`` is not used. Returns ------- self : Styler See Also -------- Styler.highlight_null: Highlight missing values with a style. Styler.highlight_max: Highlight the maximum with a style. Styler.highlight_min: Highlight the minimum with a style. Styler.highlight_quantile: Highlight values defined by a quantile with a style. Notes ----- If ``left`` is ``None`` only the right bound is applied. If ``right`` is ``None`` only the left bound is applied. If both are ``None`` all values are highlighted. ``axis`` is only needed if ``left`` or ``right`` are provided as a sequence or an array-like object for aligning the shapes. If ``left`` and ``right`` are both scalars then all ``axis`` inputs will give the same result. This function only works with compatible ``dtypes``. For example a datetime-like region can only use equivalent datetime-like ``left`` and ``right`` arguments. Use ``subset`` to control regions which have multiple ``dtypes``. Examples -------- Basic usage >>> df = pd.DataFrame({ ... 'One': [1.2, 1.6, 1.5], ... 'Two': [2.9, 2.1, 2.5], ... 'Three': [3.1, 3.2, 3.8], ... }) >>> df.style.highlight_between(left=2.1, right=2.9) .. figure:: ../../_static/style/hbetw_basic.png Using a range input sequnce along an ``axis``, in this case setting a ``left`` and ``right`` for each column individually >>> df.style.highlight_between(left=[1.4, 2.4, 3.4], right=[1.6, 2.6, 3.6], ... axis=1, color="#fffd75") .. figure:: ../../_static/style/hbetw_seq.png Using ``axis=None`` and providing the ``left`` argument as an array that matches the input DataFrame, with a constant ``right`` >>> df.style.highlight_between(left=[[2,2,3],[2,2,3],[3,3,3]], right=3.5, ... axis=None, color="#fffd75") .. figure:: ../../_static/style/hbetw_axNone.png Using ``props`` instead of default background coloring >>> df.style.highlight_between(left=1.5, right=3.5, ... props='font-weight:bold;color:#e83e8c') .. figure:: ../../_static/style/hbetw_props.png Nzbackground-color: r )r� r� rL r r5 r6 )r� �_highlight_between)rC r� r r� r r5 r6 rL r+ r+ r, �highlight_betweenj s a zStyler.highlight_between� � �?�linear) r� r r� �q_left�q_right� interpolationr6 rL rE c C s� |dkrt d�n|} t| �} | jj| } ||g|d�} |dkrZ| jf |dd�| ��} d} n@|dkr~| jf |dd�| ��} d} n t| �� �� �jf | �} d} |dkr�d |� d �}| jt | ||| j d | j d |d �S ) uh Highlight values defined by a quantile with a style. .. versionadded:: 1.3.0 Parameters ---------- subset : label, array-like, IndexSlice, optional A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input or single key, to `DataFrame.loc[:, <subset>]` where the columns are prioritised, to limit ``data`` to *before* applying the function. color : str, default 'yellow' Background color to use for highlighting axis : {0 or 'index', 1 or 'columns', None}, default 0 Axis along which to determine and highlight quantiles. If ``None`` quantiles are measured over the entire DataFrame. See examples. q_left : float, default 0 Left bound, in [0, q_right), for the target quantile range. q_right : float, default 1 Right bound, in (q_left, 1], for the target quantile range. interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’} Argument passed to ``Series.quantile`` or ``DataFrame.quantile`` for quantile estimation. inclusive : {'both', 'neither', 'left', 'right'} Identify whether quantile bounds are closed or open. props : str, default None CSS properties to use for highlighting. If ``props`` is given, ``color`` is not used. Returns ------- self : Styler See Also -------- Styler.highlight_null: Highlight missing values with a style. Styler.highlight_max: Highlight the maximum with a style. Styler.highlight_min: Highlight the minimum with a style. Styler.highlight_between: Highlight a defined range with a style. Notes ----- This function does not work with ``str`` dtypes. Examples -------- Using ``axis=None`` and apply a quantile to all collective data >>> df = pd.DataFrame(np.arange(10).reshape(2,5) + 1) >>> df.style.highlight_quantile(axis=None, q_left=0.8, color="#fffd75") .. figure:: ../../_static/style/hq_axNone.png Or highlight quantiles row-wise or column-wise, in this case by row-wise >>> df.style.highlight_quantile(axis=1, q_left=0.8, color="#fffd75") .. figure:: ../../_static/style/hq_ax1.png Use ``props`` instead of default background coloring >>> df.style.highlight_quantile(axis=None, q_left=0.2, q_right=0.8, ... props='font-weight:bold;color:#e83e8c') .. figure:: ../../_static/style/hq_props.png N)�qr> )r rO F)r� Z numeric_onlyr� )r� rQ r zbackground-color: r )r� r� rL r r5 r6 ) r� r" r4 r� Zquantiler r! Zravelr� r7 Ziloc)rC r� r r� r< r= r> r6 rL r� r4 rJ r? Z axis_applyr+ r+ r, �highlight_quantile� s, M zStyler.highlight_quantile)� html_table� html_stylec s2 t �t �|�| jg��G � ��fdd�d| �}|S )a Factory function for creating a subclass of ``Styler``. Uses custom templates and Jinja environment. .. versionchanged:: 1.3.0 Parameters ---------- searchpath : str or list Path or paths of directories containing the templates. html_table : str Name of your custom template to replace the html_table template. .. versionadded:: 1.3.0 html_style : str Name of your custom template to replace the html_style template. .. versionadded:: 1.3.0 Returns ------- MyStyler : subclass of Styler Has the correct ``env``,``template_html``, ``template_html_table`` and ``template_html_style`` class attributes set. c s4 e Zd Zej�d�Z�r"e���Z� r0e�� �ZdS )z-Styler.from_custom_template.<locals>.MyStyler)�loaderN) r* � __module__� __qualname__r � Environment�envZ get_templateZtemplate_html_tableZtemplate_html_styler+ )rB rA rC r+ r, �MyStylere s rH )r Z ChoiceLoaderZFileSystemLoaderrC )�clsZ searchpathrA rB rH r+ )rB rA rC r, �from_custom_templateA s zStyler.from_custom_template)r$ c O s t j| |f|�|�S )a= Apply ``func(self, *args, **kwargs)``, and return the result. 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 CSS-styling function column-wise, row-wise, or table-wise. 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.")) )�com�pipe)rC r$ �argsrJ r+ r+ r, rL n s Cz Styler.pipe) NNNNNTNr/ r0 NN)NN)NN)rX rY NNTTNr r NTNrZ TN)N)N)F)r N)r N)N)N)NN)r NN)r T)N)N) r� r r r Nr� NNN)r� r r r NNNN)N)r NN)Nr r) r r NN)r+ NN)Nr/ r N)Nr/ r N)Nr/ r NNr4 N)Nr/ r r9 r: r; r4 N)NN)4r* rD rE �__doc__rB rG rF rU r r rk r Z _shared_docsr� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r r r � staticmethodr( r* r. r2 r3 r8 r@ � classmethodrJ rL � __classcell__r+ r+ )rD r, r. O s> ^ * 8 [ 0. q <L6 , I : G q PY " 7 L . 0 0 g a+r. z%FrameOrSeries | Sequence | np.ndarrayr3 z Any | Noner z np.ndarray)�arg�arg_namer r4 rE c C s� |r d|ini }t | t�r6t |t�r6td|� d���n�t | t�r\t |t�r\td|� d���nnt | ttf�r�| j|dd�jf |�} nHtj| f|�} t | tj�s�t �| j |j kr�td|� d| j � d |j � ���| S ) a� For the apply-type methods, ``axis=None`` creates ``data`` as DataFrame, and for ``axis=[1,0]`` it creates a Series. Where ``arg`` is expected as an element of some operator with ``data`` we must make sure that the two are compatible shapes, or raise. Parameters ---------- arg : sequence, Series or DataFrame the user input arg arg_name : string name of the arg for use in error messages dtype : numpy dtype, optional forced numpy dtype if given data : Series or DataFrame underling subset of Styler data on which operations are performed Returns ------- ndarray r r� zQ' is a Series but underlying data for operations is a DataFrame since 'axis=None'zT' is a DataFrame but underlying data for operations is a Series with 'axis in [0,1]'N)�methodz supplied 'z:' is not correct shape for data over selected 'axis': got z , expected ) r� r r r� r� r! r� Zasarrayr� �AssertionErrorr� )rR rS r r4 r+ r+ r, �_validate_apply_axis_arg� s rV r� ��&1� �?r� z float | Nonez,Sequence | np.ndarray | FrameOrSeries | Noner1 )r� r� r� r� r� r� r c s |dkr| j td�}nt|dt| �}ttj���\} � |dkrFt�|�n|} |dkr\t�|�n|} | | } � � | | | | | | �} | j � |�| |��}dd�dd��dd�� ��fd d � �| j d krڇ�fd d �|D �S t ��fdd �|D �| j| jd�S W dQ R X dS )zM Color background in a range according to the data or a gradient map N)r r� r� )rE c S s4 dd� | dd� D �\}}}d| d| d| S )a� Calculate relative luminance of a color. 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