Merge pull request #749 from theislab/fix/cat_dtype

update check for categorical dtype

src/moscot/plotting/_utils.py

@@ -18,7 +18,6 @@
 
 import numpy as np
 import pandas as pd
-from pandas.api.types import is_categorical_dtype
 from sklearn.preprocessing import MinMaxScaler
 
 import matplotlib as mpl
@@ -523,7 +522,7 @@ def _color_transition(c1: str, c2: str, num: int, alpha: float) -> List[str]:
 def _create_col_colors(adata: AnnData, obs_col: str, subset: Union[str, List[str]]) -> Optional[mpl.colors.Colormap]:
     if isinstance(subset, list):
         subset = subset[0]
-    if not is_categorical_dtype(adata.obs[obs_col]):
+    if not isinstance(adata.obs[obs_col].dtype, pd.CategoricalDtype):
         raise TypeError(f"`adata.obs[{obs_col!r}] must be of categorical type.")
 
     for i, cat in enumerate(adata.obs[obs_col].cat.categories):

src/moscot/problems/time/_mixins.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 import pandas as pd
-from pandas.api.types import infer_dtype, is_categorical_dtype, is_numeric_dtype
+from pandas.api.types import infer_dtype, is_numeric_dtype
 
 from anndata import AnnData
 
@@ -1034,7 +1034,7 @@ def temporal_key(self: TemporalMixinProtocol[K, B], key: Optional[str]) -> None:
             raise KeyError(f"Unable to find temporal key in `adata.obs[{key!r}]`.")
         self.adata.obs[key] = self.adata.obs[key].astype("category")
         col = self.adata.obs[key]
-        if not (is_categorical_dtype(col) and is_numeric_dtype(col.cat.categories)):
+        if not (isinstance(col.dtype, pd.CategoricalDtype) and is_numeric_dtype(col.cat.categories)):
             raise TypeError(
                 f"Expected `adata.obs[{key!r}]` to be categorical with numeric categories, "
                 f"found `{infer_dtype(col)}`."