Merge pull request #74 from kbarbary/fix-pandas-dep-warning

fix deprecation warning for DataFrame.swapaxes

Author: EBjerrum

README.md

@@ -118,3 +118,4 @@ Scikit-Mol has been developed as a community effort with contributions from peop
 - [@mikemhenry](https://github.com/mikemhenry)
 - [@c-feldmann](https://github.com/c-feldmann)
 - Mieczyslaw Torchala [@mieczyslaw](https://github.com/mieczyslaw)
+- Kyle Barbary [@kbarbary](https://github.com/kbarbary)

docs/index.md

@@ -118,3 +118,4 @@ Scikit-Mol has been developed as a community effort with contributions from peop
 - [@mikemhenry](https://github.com/mikemhenry)
 - [@c-feldmann](https://github.com/c-feldmann)
 - Mieczyslaw Torchala [@mieczyslaw](https://github.com/mieczyslaw)
+- Kyle Barbary [@kbarbary](https://github.com/kbarbary)

scikit_mol/parallel.py

@@ -1,13 +1,14 @@
 from collections.abc import Sequence
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, Union
 
 import numpy as np
+import pandas as pd
 from joblib import Parallel, delayed, effective_n_jobs
 
 
 def parallelized_with_batches(
     fn: Callable[[Sequence[Any]], Any],
-    inputs_list: np.ndarray,
+    inputs_list: Union[np.ndarray, pd.DataFrame],
     n_jobs: Optional[int] = None,
     **job_kwargs: Any,
 ) -> Sequence[Optional[Any]]:
@@ -33,6 +34,11 @@ def parallelized_with_batches(
         n_jobs = len(inputs_list)
     pool = Parallel(n_jobs=n_jobs, **job_kwargs)
 
-    input_chunks = np.array_split(inputs_list, n_jobs)
+    if isinstance(inputs_list, (pd.DataFrame, pd.Series)):
+        indexes = np.array_split(range(len(inputs_list)), n_jobs)
+        input_chunks = [inputs_list.iloc[idx] for idx in indexes]
+    else:
+        input_chunks = np.array_split(inputs_list, n_jobs)
+
     results = pool(delayed(fn)(chunk) for chunk in input_chunks)
     return results