wip REF: redirect df.to_html to Styler if new kwargs are used.

doc/source/user_guide/io.rst

@@ -21,7 +21,7 @@ The pandas I/O API is a set of top level ``reader`` functions accessed like
     text;`CSV <https://en.wikipedia.org/wiki/Comma-separated_values>`__;:ref:`read_csv<io.read_csv_table>`;:ref:`to_csv<io.store_in_csv>`
     text;Fixed-Width Text File;:ref:`read_fwf<io.fwf_reader>`
     text;`JSON <https://www.json.org/>`__;:ref:`read_json<io.json_reader>`;:ref:`to_json<io.json_writer>`
-    text;`HTML <https://en.wikipedia.org/wiki/HTML>`__;:ref:`read_html<io.read_html>`;:ref:`to_html<io.html>`
+    text;`HTML <https://en.wikipedia.org/wiki/HTML>`__;:ref:`read_html<io.read_html>`;:ref:`Styler.to_html<io.html>`
     text;`LaTeX <https://en.wikipedia.org/wiki/LaTeX>`__;;:ref:`Styler.to_latex<io.latex>`
     text;`XML <https://www.w3.org/standards/xml/core>`__;:ref:`read_xml<io.read_xml>`;:ref:`to_xml<io.xml>`
     text; Local clipboard;:ref:`read_clipboard<io.clipboard>`;:ref:`to_clipboard<io.clipboard>`
@@ -2682,8 +2682,8 @@ Read in pandas ``to_html`` output (with some loss of floating point precision):
 .. code-block:: python
 
    df = pd.DataFrame(np.random.randn(2, 2))
-   s = df.to_html(float_format="{0:.40g}".format)
-   dfin = pd.read_html(s, index_col=0)
+   s = df.style.format("{0:.40g}").to_html()
+   dfin = pd.read_html(s, index_col=0)[0]
 
 The ``lxml`` backend will raise an error on a failed parse if that is the only
 parser you provide. If you only have a single parser you can provide just a
@@ -2714,156 +2714,34 @@ succeeds, the function will return*.
 Writing to HTML files
 ''''''''''''''''''''''
 
-``DataFrame`` objects have an instance method ``to_html`` which renders the
-contents of the ``DataFrame`` as an HTML table. The function arguments are as
-in the method ``to_string`` described above.
-
 .. note::
 
-   Not all of the possible options for ``DataFrame.to_html`` are shown here for
-   brevity's sake. See :func:`~pandas.core.frame.DataFrame.to_html` for the
-   full set of options.
+   DataFrame *and* Styler objects currently have a ``to_html`` method. We recommend
+   using the :meth:`Styler.to_html <pandas.io.formats.style.Styler.to_html>` method
+   over :meth:`DataFrame.to_html` due to the former's greater flexibility with
+   conditional styling, and the latter's possible argument signature change and/or future deprecation.
 
-.. ipython:: python
-   :suppress:
+Review the documentation for :meth:`Styler.to_html <pandas.io.formats.style.Styler.to_html>`,
+which gives examples of conditional styling and explains the operation of its keyword
+arguments. The ``to_html`` methods render the contents of the ``DataFrame`` as an HTML table.
 
-   def write_html(df, filename, *args, **kwargs):
-       static = os.path.abspath(os.path.join("source", "_static"))
-       with open(os.path.join(static, filename + ".html"), "w") as f:
-           df.to_html(f, *args, **kwargs)
+For simple application the following pattern is sufficient:
 
 .. ipython:: python
 
    df = pd.DataFrame(np.random.randn(2, 2))
    df
-   print(df.to_html())  # raw html
-
-.. ipython:: python
-   :suppress:
-
-   write_html(df, "basic")
-
-HTML:
-
-.. raw:: html
-   :file: ../_static/basic.html
+   print(df.style.to_html())  # raw html
 
-The ``columns`` argument will limit the columns shown:
+To format values before output, chain the :meth:`Styler.format <pandas.io.formats.style.Styler.format>`
+and :meth:`Styler.format_index <pandas.io.formats.style.Styler.format_index>` methods.
 
 .. ipython:: python
 
-   print(df.to_html(columns=[0]))
-
-.. ipython:: python
-   :suppress:
-
-   write_html(df, "columns", columns=[0])
-
-HTML:
-
-.. raw:: html
-   :file: ../_static/columns.html
-
-``float_format`` takes a Python callable to control the precision of floating
-point values:
-
-.. ipython:: python
-
-   print(df.to_html(float_format="{0:.10f}".format))
-
-.. ipython:: python
-   :suppress:
-
-   write_html(df, "float_format", float_format="{0:.10f}".format)
-
-HTML:
-
-.. raw:: html
-   :file: ../_static/float_format.html
-
-``bold_rows`` will make the row labels bold by default, but you can turn that
-off:
-
-.. ipython:: python
-
-   print(df.to_html(bold_rows=False))
-
-.. ipython:: python
-   :suppress:
-
-   write_html(df, "nobold", bold_rows=False)
-
-.. raw:: html
-   :file: ../_static/nobold.html
-
-The ``classes`` argument provides the ability to give the resulting HTML
-table CSS classes. Note that these classes are *appended* to the existing
-``'dataframe'`` class.
-
-.. ipython:: python
-
-   print(df.to_html(classes=["awesome_table_class", "even_more_awesome_class"]))
-
-The ``render_links`` argument provides the ability to add hyperlinks to cells
-that contain URLs.
-
-.. ipython:: python
-
-   url_df = pd.DataFrame(
-       {
-           "name": ["Python", "pandas"],
-           "url": ["https://www.python.org/", "https://pandas.pydata.org"],
-       }
-   )
-   print(url_df.to_html(render_links=True))
-
-.. ipython:: python
-   :suppress:
-
-   write_html(url_df, "render_links", render_links=True)
-
-HTML:
-
-.. raw:: html
-   :file: ../_static/render_links.html
-
-Finally, the ``escape`` argument allows you to control whether the
-"<", ">" and "&" characters escaped in the resulting HTML (by default it is
-``True``). So to get the HTML without escaped characters pass ``escape=False``
-
-.. ipython:: python
-
-   df = pd.DataFrame({"a": list("&<>"), "b": np.random.randn(3)})
-
-
-.. ipython:: python
-   :suppress:
-
-   write_html(df, "escape")
-   write_html(df, "noescape", escape=False)
-
-Escaped:
-
-.. ipython:: python
-
-   print(df.to_html())
-
-.. raw:: html
-   :file: ../_static/escape.html
-
-Not escaped:
-
-.. ipython:: python
-
-   print(df.to_html(escape=False))
-
-.. raw:: html
-   :file: ../_static/noescape.html
-
-.. note::
+   print(df.style.format("€ {}").to_html())
 
-   Some browsers may not show a difference in the rendering of the previous two
-   HTML tables.
+Some browsers or browser applications may process and add css class styling by default to alter the appearance
+of HTML tables, such as Jupyter Notebook and Google Colab.
 
 
 .. _io.html.gotchas:

doc/source/user_guide/scale.rst

@@ -275,6 +275,7 @@ column names and dtypes. That's because Dask hasn't actually read the data yet.
 Rather than executing immediately, doing operations build up a **task graph**.
 
 .. ipython:: python
+   :okwarning:
 
    ddf
    ddf["name"]
@@ -333,6 +334,7 @@ known automatically. In this case, since we created the parquet files manually,
 we need to supply the divisions manually.
 
 .. ipython:: python
+   :okwarning:
 
    N = 12
    starts = [f"20{i:>02d}-01-01" for i in range(N)]

doc/source/whatsnew/v1.4.0.rst

@@ -621,7 +621,7 @@ Other Deprecations
 - Deprecated the behavior of :func:`to_datetime` with the string "now" with ``utc=False``; in a future version this will match ``Timestamp("now")``, which in turn matches :meth:`Timestamp.now` returning the local time (:issue:`18705`)
 - Deprecated :meth:`DateOffset.apply`, use ``offset + other`` instead (:issue:`44522`)
 - Deprecated parameter ``names`` in :meth:`Index.copy` (:issue:`44916`)
-- A deprecation warning is now shown for :meth:`DataFrame.to_latex` indicating the arguments signature may change and emulate more the arguments to :meth:`.Styler.to_latex` in future versions (:issue:`44411`)
+- A deprecation warning is now shown for both :meth:`DataFrame.to_html` and :meth:`DataFrame.to_latex` indicating the arguments signature may change and emulate more the arguments in :meth:`.Styler.to_html` and :meth:`.Styler.to_latex`, respectively, in future versions (:issue:`44411`, :issue:`44451`)
 - Deprecated behavior of :func:`concat` between objects with bool-dtype and numeric-dtypes; in a future version these will cast to object dtype instead of coercing bools to numeric values (:issue:`39817`)
 - Deprecated :meth:`Categorical.replace`, use :meth:`Series.replace` instead (:issue:`44929`)
 - Deprecated passing ``set`` or ``dict`` as indexer for :meth:`DataFrame.loc.__setitem__`, :meth:`DataFrame.loc.__getitem__`, :meth:`Series.loc.__setitem__`, :meth:`Series.loc.__getitem__`, :meth:`DataFrame.__getitem__`, :meth:`Series.__getitem__` and :meth:`Series.__setitem__` (:issue:`42825`)