pandas.io.formats.style.Styler.hide_index

Styler.hide_index(subset=None)[source]

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.

Changed in version 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