pandas.io.formats.style.Styler.hide   Styler.hide(subset=None, axis=0, level=None, names=False)[source]
 
Hide the entire index / column headers, or specific rows / columns from display.  New in version 1.4.0.   Parameters 
 
subset:label, 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.  
level:int, str, list


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


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 
 
self:Styler

   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".