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
isNone
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
- subsetlabel, 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
- selfStyler
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