pandas.io.formats.style.Styler.hide_columns

Styler.hide_columns(subset=None)[source]

Hide the column headers or specific keys in the columns from rendering.

This method has dual functionality:

• if subset is None then the entire column headers row will be hidden whilst the data-values remain visible.

• if a subset is given then those specific columns, including the data-values will be hidden, whilst the column headers row remains visible.

Changed in version 1.3.0.

Parameters

subset:label, array-like, IndexSlice, optional

A valid 1d input or single key along the columns axis within DataFrame.loc[:, <subset>], to limit data to before applying the function.

Returns

self:Styler

See also

Styler.hide_index

Hide the entire index, or specific keys in the index.

Examples

Simple application hiding specific columns:

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
>>> df.style.hide_columns(["a", "b"])
     c
0    3
1    6

Hide column headers 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_columns()
x   d    0.1    0.0    0.4    1.3    0.6   -1.4
    e    0.7    1.0    1.3    1.5   -0.0   -0.2
    f    1.4   -0.8    1.6   -0.2   -0.4   -0.3
y   d    0.4    1.0   -0.2   -0.8   -1.2    1.1
    e   -0.6    1.2    1.8    1.9    0.3    0.3
    f    0.8    0.5   -0.3    1.2    2.2   -0.8

Hide specific columns but retain the column headers:

>>> df.style.format("{:.1f}").hide_columns(subset=(slice(None), ["a", "c"]))
           x      y
           b      b
x   a    0.0    0.6
    b    1.0   -0.0
    c   -0.8   -0.4
y   a    1.0   -1.2
    b    1.2    0.3
    c    0.5    2.2

Hide specific columns and the column headers:

>>> df.style.format("{:.1f}").hide_columns(subset=(slice(None), ["a", "c"]))
...     .hide_columns()
x   a    0.0    0.6
    b    1.0   -0.0
    c   -0.8   -0.4
y   a    1.0   -1.2
    b    1.2    0.3
    c    0.5    2.2