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 - subsetis- Nonethen the entire column headers row will be hidden whilst the data-values remain visible.
- if a - subsetis 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
- subsetlabel, array-like, IndexSlice, optional
- A valid 1d input or single key along the columns axis within DataFrame.loc[:, <subset>], to limit - datato before applying the function.
 
- Returns
- selfStyler
 
 - 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