cannot plot the figures

[yinawei]

The error occurred while calling "two_groups_unpaired.cohens_d.plot"

ImportError: cannot import name 'recode_for_categories' from 'pandas.core.arrays.categorical' (/Users/yinawei/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/arrays/categorical.py)

[josesho]

Hi @jelyness ,

Can you provide a copy-pastable example of the data and code you are using?

Also, can you run

dabest.__version__

and

pandas.__version__

and report what versions are printed?

Thanks!

[yinawei]

It works before. I think it could be the version issue.
$ dabest.version
'0.3.1'
$ pandas.version
'1.2.4'

I have the same issue while running your sample code:
import pandas as pd
import dabest
iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
iris_dabest = dabest.load(data=iris, x="species", y="petal_width",
idx=("setosa", "versicolor", "virginica"))
iris_dabest.mean_diff.plot();

---

ImportError Traceback (most recent call last)
in
10
11 # Produce a Cumming estimation plot.
---> 12 iris_dabest.mean_diff.plot();

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/dabest/_classes.py in plot(self, color_col, raw_marker_size, es_marker_size, swarm_label, contrast_label, swarm_ylim, contrast_ylim, custom_palette, swarm_desat, halfviolin_desat, halfviolin_alpha, float_contrast, show_pairs, group_summaries, group_summaries_offset, fig_size, dpi, ax, swarmplot_kwargs, violinplot_kwargs, slopegraph_kwargs, reflines_kwargs, group_summary_kwargs, legend_kwargs)
1691 del all_kwargs["self"]
1692
-> 1693 out = EffectSizeDataFramePlotter(self, **all_kwargs)
1694
1695 return out

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/dabest/plotter.py in EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs)
391 order=all_plot_groups, hue=color_col,
392 palette=plot_palette_raw, zorder=1,
--> 393 **swarmplot_kwargs)
394
395 # Plot the gapped line summaries, if this is not a Cumming plot.

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)
47 return inner_f
48

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/seaborn/categorical.py in swarmplot(x, y, hue, data, order, hue_order, dodge, orient, color, palette, size, edgecolor, linewidth, ax, **kwargs)
2990
2991 plotter = _SwarmPlotter(x, y, hue, data, order, hue_order,
-> 2992 dodge, orient, color, palette)
2993 if ax is None:
2994 ax = plt.gca()

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/seaborn/categorical.py in init(self, x, y, hue, data, order, hue_order, dodge, orient, color, palette)
1170 dodge, orient, color, palette):
1171 """Initialize the plotter."""
-> 1172 self.establish_variables(x, y, hue, data, orient, order, hue_order)
1173 self.establish_colors(color, palette, 1)
1174

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/seaborn/categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
205 # Group the numeric data
206 plot_data, value_label = self._group_longform(vals, groups,
--> 207 group_names)
208
209 # Now handle the hue levels for nested ordering

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/seaborn/categorical.py in _group_longform(self, vals, grouper, order)
251
252 # Group the val data
--> 253 grouped_vals = vals.groupby(grouper)
254 out_data = []
255 for g in order:

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/generic.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)
7892 if not isinstance(index, DatetimeIndex):
7893 raise TypeError("Index must be DatetimeIndex")
-> 7894
7895 indexer = index.indexer_at_time(time, asof=asof)
7896 return self._take_with_is_copy(indexer, axis=axis)

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in groupby(obj, by, **kwds)
2520 Cumulative max for each group.
2521
-> 2522 Returns
2523 -------
2524 Series or DataFrame

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in init(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)
389 Function to use for aggregating the data. If a function, must either
390 work when passed a {klass} or when passed to {klass}.apply.
--> 391
392 Accepted combinations are:
393

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in _get_grouper(obj, key, axis, level, sort, observed, mutated, validate)
650 multiple groupers
651
--> 652 Groupers are ultimately index mappings. They can originate as:
653 index mappings, keys to columns, functions, or Groupers
654

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in init(self, index, grouper, obj, name, level, sort, observed, in_axis)
311 # pandas\core\groupby\grouper.py:310: error: Value of type variable
312 # "FrameOrSeries" of "get_grouper" cannot be "Optional[Any]"
--> 313 # [type-var]
314 self.grouper, _, self.obj = get_grouper( # type: ignore[type-var]
315 self.obj,

~/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/groupby/categorical.py in
4
5 from pandas.core.algorithms import unique1d
----> 6 from pandas.core.arrays.categorical import (
7 Categorical,
8 CategoricalDtype,

ImportError: cannot import name 'recode_for_categories' from 'pandas.core.arrays.categorical' (/Users/yinawei/opt/miniconda3/envs/py3/lib/python3.7/site-packages/pandas/core/arrays/categorical.py)

[Jacobluke-]

Hi @yinawei , can you try to create another environment and install DABEST, the latest version of pandas to check if the problem remains?