def is_categorical(data, c=None):
from pandas.api.types import is_categorical_dtype as cat
if c is None:
return cat(data) # if data is categorical/array
if not is_view(data): # if data is anndata view
strings_to_categoricals(data)
return isinstance(c, str) and c in data.obs.keys() and cat(data.obs[c])
def is_categorical(adata, c):
adata._sanitize(
) # Indentify array of categorical type and transform where applicable
return isinstance(c, str) and (c in adata.obs.keys() and cat(adata.obs[c])
or is_color_like(c))
def is_categorical(adata, c):
from pandas.api.types import is_categorical as cat
strings_to_categoricals(adata)
return isinstance(c, str) and c in adata.obs.keys() and cat(adata.obs[c])
def n_categories(adata, c):
from pandas.api.types import is_categorical as cat
return len(adata.obs[c].cat.categories) if (c in adata.obs.keys()
and cat(adata.obs[c])) else 0
def is_categorical(adata, c):
from pandas.api.types import is_categorical as cat
strings_to_categoricals(adata)
str_not_var = isinstance(c, str) and c not in adata.var_names
return str_not_var and (c in adata.obs.keys() and cat(adata.obs[c])
or is_color_like(c))