pandas.io.formats.style.Styler.relabel_index — pandas 2.2.3 documentation (original) (raw)
Styler.relabel_index(labels, axis=0, level=None)[source]#
Relabel the index, or column header, keys to display a set of specified values.
Added in version 1.5.0.
Parameters:
labelslist-like or Index
New labels to display. Must have same length as the underlying values not hidden.
axis{“index”, 0, “columns”, 1}
Apply to the index or columns.
levelint, str, list, optional
The level(s) over which to apply the new labels. If None will apply to all levels of an Index or MultiIndex which are not hidden.
Returns:
Styler
See also
Format the text display value of index or column headers.
Hide the index, column headers, or specified data from display.
Notes
As part of Styler, this method allows the display of an index to be completely user-specified without affecting the underlying DataFrame data, index, or column headers. This means that the flexibility of indexing is maintained whilst the final display is customisable.
Since Styler is designed to be progressively constructed with method chaining, this method is adapted to react to the currently specified hidden elements. This is useful because it means one does not have to specify all the new labels if the majority of an index, or column headers, have already been hidden. The following produce equivalent display (note the length of labels in each case).
relabel first, then hide
df = pd.DataFrame({"col": ["a", "b", "c"]}) df.style.relabel_index(["A", "B", "C"]).hide([0,1])
hide first, then relabel
df = pd.DataFrame({"col": ["a", "b", "c"]}) df.style.hide([0,1]).relabel_index(["C"])
This method should be used, rather than Styler.format_index(), in one of the following cases (see examples):
- A specified set of labels are required which are not a function of the underlying index keys.
- The function of the underlying index keys requires a counter variable, such as those available upon enumeration.
Examples
Basic use
df = pd.DataFrame({"col": ["a", "b", "c"]}) df.style.relabel_index(["A", "B", "C"])
col A a B b C c
Chaining with pre-hidden elements
df.style.hide([0,1]).relabel_index(["C"])
col C c
Using a MultiIndex
midx = pd.MultiIndex.from_product([[0, 1], [0, 1], [0, 1]]) df = pd.DataFrame({"col": list(range(8))}, index=midx) styler = df.style
col 0 0 0 0 1 1 1 0 2 1 3 1 0 0 4 1 5 1 0 6 1 7 styler.hide((midx.get_level_values(0)==0)|(midx.get_level_values(1)==0)) ...
styler.hide(level=[0,1])
styler.relabel_index(["binary6", "binary7"])
col binary6 6 binary7 7
We can also achieve the above by indexing first and then re-labeling
styler = df.loc[[(1,1,0), (1,1,1)]].style styler.hide(level=[0,1]).relabel_index(["binary6", "binary7"]) ...
col binary6 6 binary7 7
Defining a formatting function which uses an enumeration counter. Also note that the value of the index key is passed in the case of string labels so it can also be inserted into the label, using curly brackets (or double curly brackets if the string if pre-formatted),
df = pd.DataFrame({"samples": np.random.rand(10)}) styler = df.loc[np.random.randint(0,10,3)].style styler.relabel_index([f"sample{i+1} ({{}})" for i in range(3)]) ...
samples sample1 (5) 0.315811 sample2 (0) 0.495941 sample3 (2) 0.067946