Fix pandas.__getitem__ warnings

theislab · Nov 2, 2023 · fcc8114 · fcc8114
1 parent 0f6fe72
commit fcc8114
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Showing 4 changed files with 6 additions and 6 deletions.
diff --git a/src/cellrank/_utils/_utils.py b/src/cellrank/_utils/_utils.py
@@ -1158,7 +1158,7 @@ def _series_from_one_hot_matrix(
     target_series = pd.Series(index=index, dtype="category")
     for vec, name in zip(membership.T, names):
         target_series = target_series.cat.add_categories(name)
-        target_series[np.where(vec)[0]] = name
+        target_series.iloc[np.where(vec)[0]] = name
 
     return target_series
 

diff --git a/src/cellrank/estimators/terminal_states/_gpcca.py b/src/cellrank/estimators/terminal_states/_gpcca.py
@@ -367,7 +367,7 @@ def predict_initial_states(self, n_states: int = 1, n_cells: int = 30, allow_ove
         if stat_dist is None:
             raise RuntimeError("No coarse-grained stationary distribution found.")
 
-        states = list(stat_dist[np.argsort(stat_dist)][:n_states].index)
+        states = list(stat_dist.iloc[np.argsort(stat_dist)][:n_states].index)
         return self.set_initial_states(states, n_cells=n_cells, allow_overlap=allow_overlap)
 
     @d.dedent

diff --git a/src/cellrank/pl/_heatmap.py b/src/cellrank/pl/_heatmap.py
@@ -178,7 +178,7 @@ def find_nearest(array: np.ndarray, value: float) -> int:
             return int(ix)
 
         series = series.sort_values(ascending=True)
-        return list(series[[find_nearest(series.values, v) for v in values]].index)
+        return list(series.iloc[[find_nearest(series.values, v) for v in values]].index)
 
     def subset_lineage(lname: str, rng: np.ndarray) -> np.ndarray:
         time_series = adata.obs[time_key]

diff --git a/tests/_helpers.py b/tests/_helpers.py
@@ -56,7 +56,7 @@ def _rpy2_mgcv_not_installed() -> bool:
 
 def bias_knn(
     conn: sp.csr_matrix,
-    pseudotime: np.ndarray,
+    pseudotime: pd.Series,
     n_neighbors: int,
     k: int = 3,
     frac_to_keep: Optional[float] = None,
@@ -72,7 +72,7 @@ def bias_knn(
         # get indices, values and current pseudo t
         row_data = conn[i, :].data
         row_ixs = conn[i, :].indices
-        current_t = pseudotime[i]
+        current_t = pseudotime.iloc[i]
 
         if frac_to_keep is not None:
             k_thresh = max(0, min(30, int(np.floor(len(row_data) * frac_to_keep))))
@@ -83,7 +83,7 @@ def bias_knn(
         cand_ixs = sorted_ixs[k_thresh:]
 
         # compare pseudotimes and set indices to zero
-        cand_t = pseudotime[cand_ixs]
+        cand_t = pseudotime.iloc[cand_ixs]
         rem_ixs = cand_ixs[cand_t < current_t]
         conn_biased[i, rem_ixs] = 0