Fix example: pd.melt(var_name=scalar)

[bnavigator]

What this PR does / why we need it:

Pandas 2.2 is more strict than previous versions used to be. The var_name kwargs has always been a scalar, so give it one.

Before:

[   26s] =================================== FAILURES ===================================
[   26s] ________________________________ test_examples _________________________________
[   26s] 
[   26s]     def test_examples():
[   26s]         # reload configuration
[   26s]         importlib.reload(chartify)
[   26s]     
[   26s]         excluded_examples = ['chart_show']
[   26s]         public_examples = [
[   26s]             attr for attr in dir(chartify.examples) if
[   26s]             callable(getattr(chartify.examples, attr))
[   26s]             and not attr.startswith("_")
[   26s]             and attr not in excluded_examples
[   26s]         ]
[   26s]         # Disable chart rendering
[   26s]         chartify.examples._OUTPUT_FORMAT = None
[   26s]         for example in public_examples:
[   26s] >           getattr(chartify.examples, example)()
[   26s] 
[   26s] tests/test_examples.py:36: 
[   26s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
[   26s] chartify/examples.py:45: in wrapper
[   26s]     return f(*args, **kwargs)
[   26s] chartify/examples.py:1304: in style_color_palette_diverging
[   26s]     data = pd.melt(
[   26s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
[   26s] 
[   26s] frame =            time  Very Unlikely  ...       Likely  Very Likely
[   26s] 0    2015-01-01      13.887274  ...   705.859449   804.6...1  ...  1801.226623  1901.008443
[   26s] 1096 2018-01-01    1095.555889  ...  1812.849633  1897.883901
[   26s] 
[   26s] [1097 rows x 6 columns]
[   26s] id_vars = ['time']
[   26s] value_vars = ['Very Unlikely', 'Unlikely', 'Neutral', 'Likely', 'Very Likely']
[   26s] var_name = ['grouping'], value_name = 'y', col_level = None, ignore_index = True
[   26s] 
[   26s]     @Appender(_shared_docs["melt"] % {"caller": "pd.melt(df, ", "other": "DataFrame.melt"})
[   26s]     def melt(
[   26s]         frame: DataFrame,
[   26s]         id_vars=None,
[   26s]         value_vars=None,
[   26s]         var_name=None,
[   26s]         value_name: Hashable = "value",
[   26s]         col_level=None,
[   26s]         ignore_index: bool = True,
[   26s]     ) -> DataFrame:
[   26s]         if value_name in frame.columns:
[   26s]             raise ValueError(
[   26s]                 f"value_name ({value_name}) cannot match an element in "
[   26s]                 "the DataFrame columns."
[   26s]             )
[   26s]         id_vars = ensure_list_vars(id_vars, "id_vars", frame.columns)
[   26s]         value_vars_was_not_none = value_vars is not None
[   26s]         value_vars = ensure_list_vars(value_vars, "value_vars", frame.columns)
[   26s]     
[   26s]         if id_vars or value_vars:
[   26s]             if col_level is not None:
[   26s]                 level = frame.columns.get_level_values(col_level)
[   26s]             else:
[   26s]                 level = frame.columns
[   26s]             labels = id_vars + value_vars
[   26s]             idx = level.get_indexer_for(labels)
[   26s]             missing = idx == -1
[   26s]             if missing.any():
[   26s]                 missing_labels = [
[   26s]                     lab for lab, not_found in zip(labels, missing) if not_found
[   26s]                 ]
[   26s]                 raise KeyError(
[   26s]                     "The following id_vars or value_vars are not present in "
[   26s]                     f"the DataFrame: {missing_labels}"
[   26s]                 )
[   26s]             if value_vars_was_not_none:
[   26s]                 frame = frame.iloc[:, algos.unique(idx)]
[   26s]             else:
[   26s]                 frame = frame.copy()
[   26s]         else:
[   26s]             frame = frame.copy()
[   26s]     
[   26s]         if col_level is not None:  # allow list or other?
[   26s]             # frame is a copy
[   26s]             frame.columns = frame.columns.get_level_values(col_level)
[   26s]     
[   26s]         if var_name is None:
[   26s]             if isinstance(frame.columns, MultiIndex):
[   26s]                 if len(frame.columns.names) == len(set(frame.columns.names)):
[   26s]                     var_name = frame.columns.names
[   26s]                 else:
[   26s]                     var_name = [f"variable_{i}" for i in range(len(frame.columns.names))]
[   26s]             else:
[   26s]                 var_name = [
[   26s]                     frame.columns.name if frame.columns.name is not None else "variable"
[   26s]                 ]
[   26s]         elif is_list_like(var_name):
[   26s] >           raise ValueError(f"{var_name=} must be a scalar.")
[   26s] E           ValueError: var_name=['grouping'] must be a scalar.
[   26s] 
[   26s] /usr/lib64/python3.10/site-packages/pandas/core/reshape/melt.py:100: ValueError

After:
✔️ Test passes.

Release note:

NONE