pandas.io.formats.style.Styler.hide

Styler.hide(subset=None, axis=0, level=None, names=False)

Hide the entire index / column headers, or specific rows / columns from display.

New in version 1.4.0.

Parameters

subsetlabel, array-like, IndexSlice, optional

A valid 1d input or single key along the axis within DataFrame.loc[<subset>, :] or DataFrame.loc[:, <subset>] depending upon axis, to limit data to select hidden rows / columns.

axis{“index”, 0, “columns”, 1}

Apply to the index or columns.

levelint, str, list

The level(s) to hide in a MultiIndex if hiding the entire index / column headers. Cannot be used simultaneously with subset.

namesbool

Whether to hide the level name(s) of the index / columns headers in the case it (or at least one the levels) remains visible.

Returns

selfStyler

Notes

This method has multiple functionality depending upon the combination of the subset, level and names arguments (see examples). The axis argument is used only to control whether the method is applied to row or column headers:

Argument combinations

subset	level	names	Effect
None	None	False	The axis-Index is hidden entirely.
None	None	True	Only the axis-Index names are hidden.
None	Int, Str, List	False	Specified axis-MultiIndex levels are hidden entirely.
None	Int, Str, List	True	Specified axis-MultiIndex levels are hidden entirely and the names of remaining axis-MultiIndex levels.
Subset	None	False	The specified data rows/columns are hidden, but the axis-Index itself, and names, remain unchanged.
Subset	None	True	The specified data rows/columns and axis-Index names are hidden, but the axis-Index itself remains unchanged.
Subset	Int, Str, List	Boolean	ValueError: cannot supply subset and level simultaneously.

Note this method only hides the identifed elements so can be chained to hide multiple elements in sequence.

Examples

Simple application hiding specific rows:

>>> df = pd.DataFrame([[1,2], [3,4], [5,6]], index=["a", "b", "c"])
>>> df.style.hide(["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()  
                 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 in a MultiIndex but retain the index:

>>> df.style.format("{:.1f}").hide(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 through chaining:

>>> df.style.format("{:.1f}").hide(subset=(slice(None), ["a", "c"])).hide()
...   
                 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

Hide a specific level:

>>> df.style.format("{:,.1f}").hide(level=1)  
                     x                    y
       a      b      c      a      b      c
x    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
y    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

Hiding just the index level names:

>>> df.index.names = ["lev0", "lev1"]
>>> df.style.format("{:,.1f}").hide(names=True)  
                         x                    y
           a      b      c      a      b      c
x   a    0.1    0.0    0.4    1.3    0.6   -1.4
    b    0.7    1.0    1.3    1.5   -0.0   -0.2
    c    1.4   -0.8    1.6   -0.2   -0.4   -0.3
y   a    0.4    1.0   -0.2   -0.8   -1.2    1.1
    b   -0.6    1.2    1.8    1.9    0.3    0.3
    c    0.8    0.5   -0.3    1.2    2.2   -0.8

Examples all produce equivalently transposed effects with axis="columns".

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