pandas.io.formats.style.Styler.text_gradient#

Styler.text_gradient(cmap='PuBu', low=0, high=0, axis=0, subset=None, vmin=None, vmax=None, gmap=None)[source]#

Color the text in a gradient style.

The text color is determined according to the data in each column, row or frame, or by a given gradient map. Requires matplotlib.

Parameters:

cmapstr or colormap: Matplotlib colormap.
lowfloat: 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.
highfloat: 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{0, 1, “index”, “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.
subsetlabel, 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.
vminfloat, optional: Minimum data value that corresponds to colormap minimum value. If not specified the minimum value of the data (or gmap) will be used.
vmaxfloat, optional: Maximum data value that corresponds to colormap maximum value. If not specified the maximum value of the data (or gmap) will be used.
gmaparray-like, optional: Gradient map for determining the text 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.

New in version 1.3.0.

Returns:

Styler

See also

Styler.background_gradient: Color the background 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.text_gradient(axis=0)  

Shading all values collectively using axis=None

>>> df.style.text_gradient(axis=None)  

Compress the color map from the both low and high ends

>>> df.style.text_gradient(axis=None, low=0.75, high=1.0)  

Manually setting vmin and vmax gradient thresholds

>>> df.style.text_gradient(axis=None, vmin=6.7, vmax=21.6)  

Setting a gmap and applying to all columns with another cmap

>>> df.style.text_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd')
...  

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.text_gradient(axis=None, gmap=gmap,
...     cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)']
... )  