pandas.io.formats.style.Styler.pipe — pandas 2.2.3 documentation (original) (raw)

Styler.pipe(func, *args, **kwargs)[source]#

Apply func(self, *args, **kwargs), and return the result.

Parameters:

funcfunction

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.

*argsoptional

Arguments passed to func.

**kwargsoptional

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 DataFrame.pipe(), this method can simplify the application of several user-defined functions to a styler. Instead of writing:

f(g(df.style.format(precision=3), arg1=a), arg2=b, arg3=c)

users can write:

(df.style.format(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 Styler.apply() orStyler.set_properties()).

Examples

Common Use

A common usage pattern is to pre-define styling operations which can be easily applied to a generic styler in a single pipe call.

def some_highlights(styler, min_color="red", max_color="blue"): ... styler.highlight_min(color=min_color, axis=None) ... styler.highlight_max(color=max_color, axis=None) ... styler.highlight_null() ... return styler df = pd.DataFrame([[1, 2, 3, pd.NA], [pd.NA, 4, 5, 6]], dtype="Int64") df.style.pipe(some_highlights, min_color="green")

Since the method returns a Styler object it can be chained with other methods as if applying the underlying highlighters directly.

(df.style.format("{:.1f}") ... .pipe(some_highlights, min_color="green") ... .highlight_between(left=2, right=5))

Advanced Use

Sometimes it may be necessary to pre-define styling functions, but in the case where those functions rely on the styler, data or context. SinceStyler.use and Styler.export are designed to be non-data dependent, they cannot be used for this purpose. Additionally the Styler.applyand Styler.format type methods are not context aware, so a solution is to use pipe to dynamically wrap this functionality.

Suppose we want to code a generic styling function that highlights the final level of a MultiIndex. The number of levels in the Index is dynamic so we need the Styler context to define the level.

def highlight_last_level(styler): ... return styler.apply_index( ... lambda v: "background-color: pink; color: yellow", axis="columns", ... level=styler.columns.nlevels-1 ... )
df.columns = pd.MultiIndex.from_product([["A", "B"], ["X", "Y"]]) df.style.pipe(highlight_last_level)

Additionally suppose we want to highlight a column header if there is any missing data in that column. In this case we need the data object itself to determine the effect on the column headers.

def highlight_header_missing(styler, level): ... def dynamic_highlight(s): ... return np.where( ... styler.data.isna().any(), "background-color: red;", "" ... ) ... return styler.apply_index(dynamic_highlight, axis=1, level=level) df.style.pipe(highlight_header_missing, level=1)