Remove calls to .format

src/scanpy/_settings.py

@@ -82,9 +82,7 @@
         possible_types_str = types.__name__
     else:
         type_names = [t.__name__ for t in types]
-        possible_types_str = "{} or {}".format(
-            ", ".join(type_names[:-1]), type_names[-1]
-        )
+        possible_types_str = f"{', '.join(type_names[:-1])} or {type_names[-1]}"
     raise TypeError(f"{varname} must be of type {possible_types_str}")
 
 

src/scanpy/external/exporting.py

@@ -345,8 +345,8 @@
 
 
 def _frac_to_hex(frac):
-    rgb = tuple(np.array(np.array(plt.cm.jet(frac)[:3]) * 255, dtype=int))
-    return "#{:02x}{:02x}{:02x}".format(*rgb)
+    r, g, b = tuple(np.array(np.array(plt.cm.jet(frac)[:3]) * 255, dtype=int))
+    return f"#{r:02x}{g:02x}{b:02x}"
 
 
 def _get_color_stats_genes(color_stats, E, gene_list):

src/scanpy/external/tl/_phenograph.py

@@ -244,8 +244,8 @@
     comm_key = (
         f"pheno_{clustering_algo}" if clustering_algo in ["louvain", "leiden"] else ""
     )
-    ig_key = "pheno_{}_ig".format("jaccard" if jaccard else "gaussian")
-    q_key = "pheno_{}_q".format("jaccard" if jaccard else "gaussian")
+    ig_key = f"pheno_{'jaccard' if jaccard else 'gaussian'}_ig"
+    q_key = f"pheno_{'jaccard' if jaccard else 'gaussian'}_q"
 
     communities, graph, Q = phenograph.cluster(
         data=data,

src/scanpy/plotting/_tools/paga.py

@@ -701,11 +701,11 @@
         and isinstance(node_labels, str)
         and node_labels != adata.uns["paga"]["groups"]
     ):
-        raise ValueError(
-            "Provide a list of group labels for the PAGA groups {}, not {}.".format(
-                adata.uns["paga"]["groups"], node_labels
-            )
+        msg = (
+            "Provide a list of group labels for the PAGA groups "
+            f"{adata.uns['paga']['groups']}, not {node_labels}."
         )
+        raise ValueError(msg)
     groups_key = adata.uns["paga"]["groups"]
     if node_labels is None:
         node_labels = adata.obs[groups_key].cat.categories

src/scanpy/preprocessing/_qc.py

@@ -193,28 +193,23 @@ def describe_var(
     var_metrics = pd.DataFrame(index=adata.var_names)
     if issparse(X):
         # Current memory bottleneck for csr matrices:
-        var_metrics["n_cells_by_{expr_type}"] = X.getnnz(axis=0)
-        var_metrics["mean_{expr_type}"] = mean_variance_axis(X, axis=0)[0]
+        var_metrics[f"n_cells_by_{expr_type}"] = X.getnnz(axis=0)
+        var_metrics[f"mean_{expr_type}"] = mean_variance_axis(X, axis=0)[0]
     else:
-        var_metrics["n_cells_by_{expr_type}"] = np.count_nonzero(X, axis=0)
-        var_metrics["mean_{expr_type}"] = X.mean(axis=0)
+        var_metrics[f"n_cells_by_{expr_type}"] = np.count_nonzero(X, axis=0)
+        var_metrics[f"mean_{expr_type}"] = X.mean(axis=0)
     if log1p:
-        var_metrics["log1p_mean_{expr_type}"] = np.log1p(
-            var_metrics["mean_{expr_type}"]
+        var_metrics[f"log1p_mean_{expr_type}"] = np.log1p(
+            var_metrics[f"mean_{expr_type}"]
         )
-    var_metrics["pct_dropout_by_{expr_type}"] = (
-        1 - var_metrics["n_cells_by_{expr_type}"] / X.shape[0]
+    var_metrics[f"pct_dropout_by_{expr_type}"] = (
+        1 - var_metrics[f"n_cells_by_{expr_type}"] / X.shape[0]
     ) * 100
-    var_metrics["total_{expr_type}"] = np.ravel(X.sum(axis=0))
+    var_metrics[f"total_{expr_type}"] = np.ravel(X.sum(axis=0))
     if log1p:
-        var_metrics["log1p_total_{expr_type}"] = np.log1p(
-            var_metrics["total_{expr_type}"]
+        var_metrics[f"log1p_total_{expr_type}"] = np.log1p(
+            var_metrics[f"total_{expr_type}"]
         )
-    # Relabel
-    new_colnames = []
-    for col in var_metrics.columns:
-        new_colnames.append(col.format(**locals()))
-    var_metrics.columns = new_colnames
     if inplace:
         adata.var[var_metrics.columns] = var_metrics
     else:

src/scanpy/tools/_rank_genes_groups.py

@@ -123,10 +123,11 @@ def __init__(
         )
 
         if len(invalid_groups_selected) > 0:
-            raise ValueError(
-                "Could not calculate statistics for groups {} since they only "
-                "contain one sample.".format(", ".join(invalid_groups_selected))
+            msg = (
+                f"Could not calculate statistics for groups {', '.join(invalid_groups_selected)} "
+                "since they only contain one sample."
             )
+            raise ValueError(msg)
 
         adata_comp = adata
         if layer is not None:

tests/notebooks/test_paga_paul15_subsampled.py

@@ -138,6 +138,6 @@ def test_paga_paul15_subsampled(image_comparer, plt):
             show=False,
         )
         # add a test for this at some point
-        # data.to_csv('./write/paga_path_{}.csv'.format(descr))
+        # data.to_csv(f"./write/paga_path_{descr}.csv")
 
     save_and_compare_images("paga_path")