diff --git a/pyproject.toml b/pyproject.toml
index ec5c6e0541..355c4ff483 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -237,6 +237,7 @@ select = [
     "PLR0917", # Ban APIs with too many positional parameters
     "FBT",     # No positional boolean parameters
     "PT",      # Pytest style
+    "SIM",     # Simplify control flow
 ]
 ignore = [
     # line too long -> we accept long comment lines; black gets rid of long code lines
diff --git a/src/scanpy/_settings.py b/src/scanpy/_settings.py
index b090261d1f..63f91d2279 100644
--- a/src/scanpy/_settings.py
+++ b/src/scanpy/_settings.py
@@ -371,7 +371,7 @@ def logpath(self) -> Path | None:
     def logpath(self, logpath: Path | str | None):
         _type_check(logpath, "logfile", (str, Path))
         # set via “file object” branch of logfile.setter
-        self.logfile = Path(logpath).open("a")
+        self.logfile = Path(logpath).open("a")  # noqa: SIM115
         self._logpath = Path(logpath)
 
     @property
@@ -519,7 +519,7 @@ def __str__(self) -> str:
         return "\n".join(
             f"{k} = {v!r}"
             for k, v in inspect.getmembers(self)
-            if not k.startswith("_") and not k == "getdoc"
+            if not k.startswith("_") and k != "getdoc"
         )
 
 
diff --git a/src/scanpy/_utils/__init__.py b/src/scanpy/_utils/__init__.py
index 46d62bcde6..b8513d87ba 100644
--- a/src/scanpy/_utils/__init__.py
+++ b/src/scanpy/_utils/__init__.py
@@ -13,7 +13,7 @@
 import sys
 import warnings
 from collections import namedtuple
-from contextlib import contextmanager
+from contextlib import contextmanager, suppress
 from enum import Enum
 from functools import partial, singledispatch, wraps
 from operator import mul, truediv
@@ -281,10 +281,8 @@ def get_igraph_from_adjacency(adjacency, directed=None):
     g = ig.Graph(directed=directed)
     g.add_vertices(adjacency.shape[0])  # this adds adjacency.shape[0] vertices
     g.add_edges(list(zip(sources, targets)))
-    try:
+    with suppress(KeyError):
         g.es["weight"] = weights
-    except KeyError:
-        pass
     if g.vcount() != adjacency.shape[0]:
         logg.warning(
             f"The constructed graph has only {g.vcount()} nodes. "
@@ -613,11 +611,10 @@ def _(
     out: sparse.csr_matrix | sparse.csc_matrix | None = None,
 ) -> sparse.csr_matrix | sparse.csc_matrix:
     check_op(op)
-    if out is not None:
-        if X.data is not out.data:
-            raise ValueError(
-                "`out` argument provided but not equal to X.  This behavior is not supported for sparse matrix scaling."
-            )
+    if out is not None and X.data is not out.data:
+        raise ValueError(
+            "`out` argument provided but not equal to X.  This behavior is not supported for sparse matrix scaling."
+        )
     if not allow_divide_by_zero and op is truediv:
         scaling_array = scaling_array.copy() + (scaling_array == 0)
 
@@ -684,7 +681,7 @@ def _(
     column_scale = axis == 1
 
     if isinstance(scaling_array, DaskArray):
-        if (row_scale and not X.chunksize[0] == scaling_array.chunksize[0]) or (
+        if (row_scale and X.chunksize[0] != scaling_array.chunksize[0]) or (
             column_scale
             and (
                 (
diff --git a/src/scanpy/_utils/compute/is_constant.py b/src/scanpy/_utils/compute/is_constant.py
index 7dac03b40a..80f6581980 100644
--- a/src/scanpy/_utils/compute/is_constant.py
+++ b/src/scanpy/_utils/compute/is_constant.py
@@ -121,10 +121,7 @@ def _is_constant_csr_rows(
     for i in range(n):
         start = indptr[i]
         stop = indptr[i + 1]
-        if stop - start == shape[1]:
-            val = data[start]
-        else:
-            val = 0
+        val = data[start] if stop - start == shape[1] else 0
         for j in range(start, stop):
             if data[j] != val:
                 result[i] = False
diff --git a/src/scanpy/datasets/_datasets.py b/src/scanpy/datasets/_datasets.py
index ccbc9a3bb3..41b23160d6 100644
--- a/src/scanpy/datasets/_datasets.py
+++ b/src/scanpy/datasets/_datasets.py
@@ -219,7 +219,7 @@ def moignard15() -> AnnData:
     }
     # annotate each observation/cell
     adata.obs["exp_groups"] = [
-        next(gname for gname in groups.keys() if sname.startswith(gname))
+        next(gname for gname in groups if sname.startswith(gname))
         for sname in adata.obs_names
     ]
     # fix the order and colors of names in "groups"
diff --git a/src/scanpy/datasets/_ebi_expression_atlas.py b/src/scanpy/datasets/_ebi_expression_atlas.py
index 9f3bcb81ad..b7e1886e71 100644
--- a/src/scanpy/datasets/_ebi_expression_atlas.py
+++ b/src/scanpy/datasets/_ebi_expression_atlas.py
@@ -65,10 +65,7 @@ def read_mtx_from_stream(stream: BinaryIO) -> sparse.csr_matrix:
     n, m, _ = (int(x) for x in curline[:-1].split(b" "))
 
     max_int32 = np.iinfo(np.int32).max
-    if n > max_int32 or m > max_int32:
-        coord_dtype = np.int64
-    else:
-        coord_dtype = np.int32
+    coord_dtype = np.int64 if n > max_int32 or m > max_int32 else np.int32
 
     data = pd.read_csv(
         stream,
diff --git a/src/scanpy/external/pl.py b/src/scanpy/external/pl.py
index 662bc88eb3..a3d1767cee 100644
--- a/src/scanpy/external/pl.py
+++ b/src/scanpy/external/pl.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import contextlib
 from typing import TYPE_CHECKING
 
 import matplotlib.pyplot as plt
@@ -214,10 +215,8 @@ def sam(
         return axes
 
     if isinstance(c, str):
-        try:
+        with contextlib.suppress(KeyError):
             c = np.array(list(adata.obs[c]))
-        except KeyError:
-            pass
 
     if isinstance(c[0], (str, np.str_)) and isinstance(c, (np.ndarray, list)):
         import samalg.utilities as ut
diff --git a/src/scanpy/get/get.py b/src/scanpy/get/get.py
index c5e95de1ab..0c1272ae62 100644
--- a/src/scanpy/get/get.py
+++ b/src/scanpy/get/get.py
@@ -121,10 +121,7 @@ def _check_indices(
     use_raw: bool = False,
 ) -> tuple[list[str], list[str], list[str]]:
     """Common logic for checking indices for obs_df and var_df."""
-    if use_raw:
-        alt_repr = "adata.raw"
-    else:
-        alt_repr = "adata"
+    alt_repr = "adata.raw" if use_raw else "adata"
 
     alt_dim = ("obs", "var")[dim == "obs"]
 
@@ -288,10 +285,7 @@ def obs_df(
         var = adata.raw.var
     else:
         var = adata.var
-    if gene_symbols is not None:
-        alias_index = pd.Index(var[gene_symbols])
-    else:
-        alias_index = None
+    alias_index = pd.Index(var[gene_symbols]) if gene_symbols is not None else None
 
     obs_cols, var_idx_keys, var_symbols = _check_indices(
         adata.obs,
diff --git a/src/scanpy/neighbors/__init__.py b/src/scanpy/neighbors/__init__.py
index 0666a8b3ed..64b14cf112 100644
--- a/src/scanpy/neighbors/__init__.py
+++ b/src/scanpy/neighbors/__init__.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import contextlib
 from collections.abc import Mapping
 from textwrap import indent
 from types import MappingProxyType
@@ -469,10 +470,7 @@ def transitions(self) -> np.ndarray | csr_matrix:
         -----
         This has not been tested, in contrast to `transitions_sym`.
         """
-        if issparse(self.Z):
-            Zinv = self.Z.power(-1)
-        else:
-            Zinv = np.diag(1.0 / np.diag(self.Z))
+        Zinv = self.Z.power(-1) if issparse(self.Z) else np.diag(1.0 / np.diag(self.Z))
         return self.Z @ self.transitions_sym @ Zinv
 
     @property
@@ -591,10 +589,9 @@ def compute_neighbors(
 
             if isinstance(index, NNDescent):
                 # very cautious here
-                try:
+                # TODO catch the correct exception
+                with contextlib.suppress(Exception):
                     self._rp_forest = _make_forest_dict(index)
-                except Exception:  # TODO catch the correct exception
-                    pass
         start_connect = logg.debug("computed neighbors", time=start_neighbors)
 
         if method == "umap":
diff --git a/src/scanpy/plotting/_anndata.py b/src/scanpy/plotting/_anndata.py
index 5bd3de8188..5dfea0ae29 100755
--- a/src/scanpy/plotting/_anndata.py
+++ b/src/scanpy/plotting/_anndata.py
@@ -157,15 +157,15 @@ def scatter(
     if x is None or y is None:
         raise ValueError("Either provide a `basis` or `x` and `y`.")
     if (
-        (x in adata.obs.keys() or x in var_index)
-        and (y in adata.obs.keys() or y in var_index)
-        and (color is None or color in adata.obs.keys() or color in var_index)
+        (x in adata.obs.columns or x in var_index)
+        and (y in adata.obs.columns or y in var_index)
+        and (color is None or color in adata.obs.columns or color in var_index)
     ):
         return _scatter_obs(**args)
     if (
-        (x in adata.var.keys() or x in adata.obs.index)
-        and (y in adata.var.keys() or y in adata.obs.index)
-        and (color is None or color in adata.var.keys() or color in adata.obs.index)
+        (x in adata.var.columns or x in adata.obs.index)
+        and (y in adata.var.columns or y in adata.obs.index)
+        and (color is None or color in adata.var.columns or color in adata.obs.index)
     ):
         adata_T = adata.T
         axs = _scatter_obs(
@@ -217,14 +217,12 @@ def _scatter_obs(
     use_raw = _check_use_raw(adata, use_raw)
 
     # Process layers
-    if layers in ["X", None] or (
-        isinstance(layers, str) and layers in adata.layers.keys()
-    ):
+    if layers in ["X", None] or (isinstance(layers, str) and layers in adata.layers):
         layers = (layers, layers, layers)
     elif isinstance(layers, Collection) and len(layers) == 3:
         layers = tuple(layers)
         for layer in layers:
-            if layer not in adata.layers.keys() and layer not in ["X", None]:
+            if layer not in adata.layers and layer not in ["X", None]:
                 raise ValueError(
                     "`layers` should have elements that are "
                     "either None or in adata.layers.keys()."
@@ -256,7 +254,7 @@ def _scatter_obs(
     )
     if title is not None and isinstance(title, str):
         title = [title]
-    highlights = adata.uns["highlights"] if "highlights" in adata.uns else []
+    highlights = adata.uns.get("highlights", [])
     if basis is not None:
         try:
             # ignore the '0th' diffusion component
@@ -292,19 +290,14 @@ def _scatter_obs(
         n = Y.shape[0]
         size = 120000 / n
 
-    if legend_loc.startswith("on data") and legend_fontsize is None:
-        legend_fontsize = rcParams["legend.fontsize"]
-    elif legend_fontsize is None:
+    if legend_fontsize is None:
         legend_fontsize = rcParams["legend.fontsize"]
 
     palette_was_none = False
     if palette is None:
         palette_was_none = True
     if isinstance(palette, Sequence) and not isinstance(palette, str):
-        if not is_color_like(palette[0]):
-            palettes = palette
-        else:
-            palettes = [palette]
+        palettes = palette if not is_color_like(palette[0]) else [palette]
     else:
         palettes = [palette for _ in range(len(keys))]
     palettes = [_utils.default_palette(palette) for palette in palettes]
@@ -328,7 +321,7 @@ def _scatter_obs(
     else:
         component_name = None
     axis_labels = (x, y) if component_name is None else None
-    show_ticks = True if component_name is None else False
+    show_ticks = component_name is None
 
     # generate the colors
     color_ids: list[np.ndarray | ColorLike] = []
@@ -364,9 +357,8 @@ def _scatter_obs(
             categoricals.append(ikey)
         color_ids.append(c)
 
-    if right_margin is None and len(categoricals) > 0:
-        if legend_loc == "right margin":
-            right_margin = 0.5
+    if right_margin is None and len(categoricals) > 0 and legend_loc == "right margin":
+        right_margin = 0.5
     if title is None and keys[0] is not None:
         title = [
             key.replace("_", " ") if not is_color_like(key) else "" for key in keys
@@ -488,10 +480,7 @@ def add_centroid(centroids, name, Y, mask):
 
             all_pos = np.zeros((len(adata.obs[key].cat.categories), 2))
             for iname, name in enumerate(adata.obs[key].cat.categories):
-                if name in centroids:
-                    all_pos[iname] = centroids[name]
-                else:
-                    all_pos[iname] = [np.nan, np.nan]
+                all_pos[iname] = centroids.get(name, [np.nan, np.nan])
             if legend_loc == "on data export":
                 filename = settings.writedir / "pos.csv"
                 logg.warning(f"exporting label positions to {filename}")
@@ -1245,10 +1234,7 @@ def heatmap(
         groupby_width = 0.2 if categorical else 0
         if figsize is None:
             height = 6
-            if show_gene_labels:
-                heatmap_width = len(var_names) * 0.3
-            else:
-                heatmap_width = 8
+            heatmap_width = len(var_names) * 0.3 if show_gene_labels else 8
             width = heatmap_width + dendro_width + groupby_width
         else:
             width, height = figsize
@@ -1352,10 +1338,7 @@ def heatmap(
         dendro_height = 0.8 if dendrogram else 0
         groupby_height = 0.13 if categorical else 0
         if figsize is None:
-            if show_gene_labels:
-                heatmap_height = len(var_names) * 0.18
-            else:
-                heatmap_height = 4
+            heatmap_height = len(var_names) * 0.18 if show_gene_labels else 4
             width = 10
             height = heatmap_height + dendro_height + groupby_height
         else:
@@ -1440,10 +1423,7 @@ def heatmap(
             for idx, (label, pos) in enumerate(
                 zip(var_group_labels, var_group_positions)
             ):
-                if var_groups_subset_of_groupby:
-                    label_code = label2code[label]
-                else:
-                    label_code = idx
+                label_code = label2code[label] if var_groups_subset_of_groupby else idx
                 arr += [label_code] * (pos[1] + 1 - pos[0])
             gene_groups_ax.imshow(
                 np.array([arr]).T, aspect="auto", cmap=groupby_cmap, norm=norm
@@ -1892,10 +1872,7 @@ def correlation_matrix(
         labels = adata.obs[groupby].cat.categories
     num_rows = corr_matrix.shape[0]
     colorbar_height = 0.2
-    if dendrogram:
-        dendrogram_width = 1.8
-    else:
-        dendrogram_width = 0
+    dendrogram_width = 1.8 if dendrogram else 0
     if figsize is None:
         corr_matrix_height = num_rows * 0.6
         height = corr_matrix_height + colorbar_height
@@ -2052,7 +2029,7 @@ def _prepare_dataframe(
                     "groupby has to be a valid observation. "
                     f"Given {group}, is not in observations: {adata.obs_keys()}" + msg
                 )
-            if group in adata.obs.keys() and group == adata.obs.index.name:
+            if group in adata.obs.columns and group == adata.obs.index.name:
                 raise ValueError(
                     f"Given group {group} is both and index and a column level, "
                     "which is ambiguous."
@@ -2171,10 +2148,7 @@ def _plot_gene_groups_brackets(
     if orientation == "top":
         # rotate labels if any of them is longer than 4 characters
         if rotation is None and group_labels:
-            if max([len(x) for x in group_labels]) > 4:
-                rotation = 90
-            else:
-                rotation = 0
+            rotation = 90 if max([len(x) for x in group_labels]) > 4 else 0
         for idx in range(len(left)):
             verts.append((left[idx], 0))  # lower-left
             verts.append((left[idx], 0.6))  # upper-left
@@ -2600,11 +2574,8 @@ def _plot_categories_as_colorblocks(
         )
         if len(labels) > 1:
             groupby_ax.set_xticks(ticks)
-            if max([len(str(x)) for x in labels]) < 3:
-                # if the labels are small do not rotate them
-                rotation = 0
-            else:
-                rotation = 90
+            # if the labels are small do not rotate them
+            rotation = 0 if max(len(str(x)) for x in labels) < 3 else 90
             groupby_ax.set_xticklabels(labels, rotation=rotation)
 
         # remove x ticks
diff --git a/src/scanpy/plotting/_baseplot_class.py b/src/scanpy/plotting/_baseplot_class.py
index 6e5c8cd2c5..e68cc07727 100644
--- a/src/scanpy/plotting/_baseplot_class.py
+++ b/src/scanpy/plotting/_baseplot_class.py
@@ -137,9 +137,7 @@ def __init__(
         self.width, self.height = figsize if figsize is not None else (None, None)
 
         self.has_var_groups = (
-            True
-            if var_group_positions is not None and len(var_group_positions) > 0
-            else False
+            var_group_positions is not None and len(var_group_positions) > 0
         )
 
         self._update_var_groups()
@@ -160,18 +158,19 @@ def __init__(
                 "Plot would be very large."
             )
 
-        if categories_order is not None:
-            if set(self.obs_tidy.index.categories) != set(categories_order):
-                logg.error(
-                    "Please check that the categories given by "
-                    "the `order` parameter match the categories that "
-                    "want to be reordered.\n\n"
-                    "Mismatch: "
-                    f"{set(self.obs_tidy.index.categories).difference(categories_order)}\n\n"
-                    f"Given order categories: {categories_order}\n\n"
-                    f"{groupby} categories: {list(self.obs_tidy.index.categories)}\n"
-                )
-                return
+        if categories_order is not None and (
+            set(self.obs_tidy.index.categories) != set(categories_order)
+        ):
+            logg.error(
+                "Please check that the categories given by "
+                "the `order` parameter match the categories that "
+                "want to be reordered.\n\n"
+                "Mismatch: "
+                f"{set(self.obs_tidy.index.categories).difference(categories_order)}\n\n"
+                f"Given order categories: {categories_order}\n\n"
+                f"{groupby} categories: {list(self.obs_tidy.index.categories)}\n"
+            )
+            return
 
         self.adata = adata
         self.groupby = [groupby] if isinstance(groupby, str) else groupby
@@ -388,8 +387,8 @@ def add_totals(
             self.group_extra_size = 0
             return self
 
-        _sort = True if sort is not None else False
-        _ascending = True if sort == "ascending" else False
+        _sort = sort is not None
+        _ascending = sort == "ascending"
         counts_df = self.obs_tidy.index.value_counts(sort=_sort, ascending=_ascending)
 
         if _sort:
@@ -489,10 +488,7 @@ def _plot_totals(
         if self.categories_order is not None:
             counts_df = counts_df.loc[self.categories_order]
         if params["color"] is None:
-            if f"{self.groupby}_colors" in self.adata.uns:
-                color = self.adata.uns[f"{self.groupby}_colors"]
-            else:
-                color = "salmon"
+            color = self.adata.uns.get(f"{self.groupby}_colors", "salmon")
         else:
             color = params["color"]
 
@@ -1027,10 +1023,7 @@ def _plot_var_groups_brackets(
         if orientation == "top":
             # rotate labels if any of them is longer than 4 characters
             if rotation is None and group_labels:
-                if max([len(x) for x in group_labels]) > 4:
-                    rotation = 90
-                else:
-                    rotation = 0
+                rotation = 90 if max([len(x) for x in group_labels]) > 4 else 0
             for idx, (left_coor, right_coor) in enumerate(zip(left, right)):
                 verts.append((left_coor, 0))  # lower-left
                 verts.append((left_coor, 0.6))  # upper-left
diff --git a/src/scanpy/plotting/_tools/__init__.py b/src/scanpy/plotting/_tools/__init__.py
index eec202d0a5..837d3791e8 100644
--- a/src/scanpy/plotting/_tools/__init__.py
+++ b/src/scanpy/plotting/_tools/__init__.py
@@ -93,9 +93,7 @@ def pca_overview(adata: AnnData, **params):
     --------
     pp.pca
     """
-    show = params["show"] if "show" in params else None
-    if "show" in params:
-        del params["show"]
+    show = params.pop("show", None)
     pca(adata, **params, show=False)
     pca_loadings(adata, show=False)
     pca_variance_ratio(adata, show=show)
@@ -398,10 +396,7 @@ def rank_genes_groups(
     tl.rank_genes_groups
 
     """
-    if "n_panels_per_row" in kwds:
-        n_panels_per_row = kwds["n_panels_per_row"]
-    else:
-        n_panels_per_row = ncols
+    n_panels_per_row = kwds.get("n_panels_per_row", ncols)
     if n_genes < 1:
         raise NotImplementedError(
             "Specifying a negative number for n_genes has not been implemented for "
@@ -567,10 +562,7 @@ def _rank_genes_groups_plot(
             if len(genes_list) == 0:
                 logg.warning(f"No genes found for group {group}")
                 continue
-            if n_genes < 0:
-                genes_list = genes_list[n_genes:]
-            else:
-                genes_list = genes_list[:n_genes]
+            genes_list = genes_list[n_genes:] if n_genes < 0 else genes_list[:n_genes]
             var_names[group] = genes_list
             var_names_list.extend(genes_list)
 
@@ -1566,10 +1558,7 @@ def embedding_density(
 
     # turn group into a list if needed
     if group == "all":
-        if groupby is None:
-            group = None
-        else:
-            group = list(adata.obs[groupby].cat.categories)
+        group = None if groupby is None else list(adata.obs[groupby].cat.categories)
     elif isinstance(group, str):
         group = [group]
 
@@ -1633,10 +1622,7 @@ def embedding_density(
             adata.obs[density_col_name] = dens_values
             dot_sizes[group_mask] = np.ones(sum(group_mask)) * fg_dotsize
 
-            if title is None:
-                _title = group_name
-            else:
-                _title = title
+            _title = group_name if title is None else title
 
             ax = embedding(
                 adata,
@@ -1773,16 +1759,15 @@ def _get_values_to_plot(
                 df["names"] = df[gene_symbols]
             # check that all genes are present in the df as sc.tl.rank_genes_groups
             # can be called with only top genes
-            if not check_done:
-                if df.shape[0] < adata.shape[1]:
-                    message = (
-                        "Please run `sc.tl.rank_genes_groups` with "
-                        "'n_genes=adata.shape[1]' to save all gene "
-                        f"scores. Currently, only {df.shape[0]} "
-                        "are found"
-                    )
-                    logg.error(message)
-                    raise ValueError(message)
+            if not check_done and df.shape[0] < adata.shape[1]:
+                message = (
+                    "Please run `sc.tl.rank_genes_groups` with "
+                    "'n_genes=adata.shape[1]' to save all gene "
+                    f"scores. Currently, only {df.shape[0]} "
+                    "are found"
+                )
+                logg.error(message)
+                raise ValueError(message)
             df["group"] = group
             df_list.append(df)
 
diff --git a/src/scanpy/plotting/_tools/paga.py b/src/scanpy/plotting/_tools/paga.py
index f0d45e9a80..159be79913 100644
--- a/src/scanpy/plotting/_tools/paga.py
+++ b/src/scanpy/plotting/_tools/paga.py
@@ -1131,7 +1131,7 @@ def paga_path(
         groups_key = adata.uns["paga"]["groups"]
     groups_names = adata.obs[groups_key].cat.categories
 
-    if "dpt_pseudotime" not in adata.obs.keys():
+    if "dpt_pseudotime" not in adata.obs.columns:
         raise ValueError(
             "`pl.paga_path` requires computation of a pseudotime `tl.dpt` "
             "for ordering at single-cell resolution"
diff --git a/src/scanpy/plotting/_tools/scatterplots.py b/src/scanpy/plotting/_tools/scatterplots.py
index 769514a69e..5c15fa8df4 100644
--- a/src/scanpy/plotting/_tools/scatterplots.py
+++ b/src/scanpy/plotting/_tools/scatterplots.py
@@ -886,7 +886,7 @@ def pca(
         return embedding(
             adata, "pca", show=show, return_fig=return_fig, save=save, **kwargs
         )
-    if "pca" not in adata.obsm.keys() and "X_pca" not in adata.obsm.keys():
+    if "pca" not in adata.obsm and "X_pca" not in adata.obsm:
         raise KeyError(
             f"Could not find entry in `obsm` for 'pca'.\n"
             f"Available keys are: {list(adata.obsm.keys())}."
@@ -1011,10 +1011,7 @@ def spatial(
     crop_coord = _check_crop_coord(crop_coord, scale_factor)
     na_color = _check_na_color(na_color, img=img)
 
-    if bw:
-        cmap_img = "gray"
-    else:
-        cmap_img = None
+    cmap_img = "gray" if bw else None
     circle_radius = size * scale_factor * spot_size * 0.5
 
     axs = embedding(
@@ -1342,10 +1339,7 @@ def _check_spatial_data(
             library_id = list(spatial_mapping.keys())[0]
         else:
             library_id = None
-    if library_id is not None:
-        spatial_data = spatial_mapping[library_id]
-    else:
-        spatial_data = None
+    spatial_data = spatial_mapping[library_id] if library_id is not None else None
     return library_id, spatial_data
 
 
@@ -1387,10 +1381,7 @@ def _check_na_color(
     na_color: ColorLike | None, *, img: np.ndarray | None = None
 ) -> ColorLike:
     if na_color is None:
-        if img is not None:
-            na_color = (0.0, 0.0, 0.0, 0.0)
-        else:
-            na_color = "lightgray"
+        na_color = (0.0, 0.0, 0.0, 0.0) if img is not None else "lightgray"
     return na_color
 
 
diff --git a/src/scanpy/plotting/_utils.py b/src/scanpy/plotting/_utils.py
index ea6aa0cb10..13832658f5 100644
--- a/src/scanpy/plotting/_utils.py
+++ b/src/scanpy/plotting/_utils.py
@@ -1280,10 +1280,10 @@ def fix_kwds(kwds_dict, **kwargs):
 
 
 def _get_basis(adata: AnnData, basis: str):
-    if basis in adata.obsm.keys():
+    if basis in adata.obsm:
         basis_key = basis
 
-    elif f"X_{basis}" in adata.obsm.keys():
+    elif f"X_{basis}" in adata.obsm:
         basis_key = f"X_{basis}"
 
     return basis_key
diff --git a/src/scanpy/preprocessing/_combat.py b/src/scanpy/preprocessing/_combat.py
index b8487e4e7e..ef193d38b4 100644
--- a/src/scanpy/preprocessing/_combat.py
+++ b/src/scanpy/preprocessing/_combat.py
@@ -196,10 +196,7 @@ def combat(
             raise ValueError("Covariates must be unique")
 
     # only works on dense matrices so far
-    if issparse(adata.X):
-        X = adata.X.toarray().T
-    else:
-        X = adata.X.T
+    X = adata.X.toarray().T if issparse(adata.X) else adata.X.T
     data = pd.DataFrame(data=X, index=adata.var_names, columns=adata.obs_names)
 
     sanitize_anndata(adata)
diff --git a/src/scanpy/preprocessing/_deprecated/__init__.py b/src/scanpy/preprocessing/_deprecated/__init__.py
index bb944b874a..7cd7520171 100644
--- a/src/scanpy/preprocessing/_deprecated/__init__.py
+++ b/src/scanpy/preprocessing/_deprecated/__init__.py
@@ -7,7 +7,7 @@
 
 @legacy_api("max_fraction", "mult_with_mean")
 def normalize_per_cell_weinreb16_deprecated(
-    X: np.ndarray,
+    x: np.ndarray,
     *,
     max_fraction: float = 1,
     mult_with_mean: bool = False,
@@ -37,23 +37,23 @@ def normalize_per_cell_weinreb16_deprecated(
     if max_fraction < 0 or max_fraction > 1:
         raise ValueError("Choose max_fraction between 0 and 1.")
 
-    counts_per_cell = X.sum(1).A1 if issparse(X) else X.sum(1)
-    gene_subset = np.all(X <= counts_per_cell[:, None] * max_fraction, axis=0)
-    if issparse(X):
+    counts_per_cell = x.sum(1).A1 if issparse(x) else x.sum(1)
+    gene_subset = np.all(x <= counts_per_cell[:, None] * max_fraction, axis=0)
+    if issparse(x):
         gene_subset = gene_subset.A1
     tc_include = (
-        X[:, gene_subset].sum(1).A1 if issparse(X) else X[:, gene_subset].sum(1)
+        x[:, gene_subset].sum(1).A1 if issparse(x) else x[:, gene_subset].sum(1)
     )
 
-    X_norm = (
-        X.multiply(csr_matrix(1 / tc_include[:, None]))
-        if issparse(X)
-        else X / tc_include[:, None]
+    x_norm = (
+        x.multiply(csr_matrix(1 / tc_include[:, None]))
+        if issparse(x)
+        else x / tc_include[:, None]
     )
     if mult_with_mean:
-        X_norm *= np.mean(counts_per_cell)
+        x_norm *= np.mean(counts_per_cell)
 
-    return X_norm
+    return x_norm
 
 
 def zscore_deprecated(X: np.ndarray) -> np.ndarray:
diff --git a/src/scanpy/preprocessing/_normalization.py b/src/scanpy/preprocessing/_normalization.py
index c6fccfb70a..686c69b224 100644
--- a/src/scanpy/preprocessing/_normalization.py
+++ b/src/scanpy/preprocessing/_normalization.py
@@ -206,25 +206,25 @@ def normalize_total(
 
     view_to_actual(adata)
 
-    X = _get_obs_rep(adata, layer=layer)
+    x = _get_obs_rep(adata, layer=layer)
 
     gene_subset = None
     msg = "normalizing counts per cell"
 
-    counts_per_cell = axis_sum(X, axis=1)
+    counts_per_cell = axis_sum(x, axis=1)
     if exclude_highly_expressed:
         counts_per_cell = np.ravel(counts_per_cell)
 
         # at least one cell as more than max_fraction of counts per cell
 
-        gene_subset = axis_sum((X > counts_per_cell[:, None] * max_fraction), axis=0)
+        gene_subset = axis_sum((x > counts_per_cell[:, None] * max_fraction), axis=0)
         gene_subset = np.asarray(np.ravel(gene_subset) == 0)
 
         msg += (
             ". The following highly-expressed genes are not considered during "
             f"normalization factor computation:\n{adata.var_names[~gene_subset].tolist()}"
         )
-        counts_per_cell = axis_sum(X[:, gene_subset], axis=1)
+        counts_per_cell = axis_sum(x[:, gene_subset], axis=1)
 
     start = logg.info(msg)
     counts_per_cell = np.ravel(counts_per_cell)
@@ -237,12 +237,12 @@ def normalize_total(
         if key_added is not None:
             adata.obs[key_added] = counts_per_cell
         _set_obs_rep(
-            adata, _normalize_data(X, counts_per_cell, target_sum), layer=layer
+            adata, _normalize_data(x, counts_per_cell, target_sum), layer=layer
         )
     else:
         # not recarray because need to support sparse
         dat = dict(
-            X=_normalize_data(X, counts_per_cell, target_sum, copy=True),
+            X=_normalize_data(x, counts_per_cell, target_sum, copy=True),
             norm_factor=counts_per_cell,
         )
 
diff --git a/src/scanpy/preprocessing/_pca.py b/src/scanpy/preprocessing/_pca.py
index 5b5706c123..93432841f8 100644
--- a/src/scanpy/preprocessing/_pca.py
+++ b/src/scanpy/preprocessing/_pca.py
@@ -202,10 +202,7 @@ def pca(
 
     if n_comps is None:
         min_dim = min(adata_comp.n_vars, adata_comp.n_obs)
-        if settings.N_PCS >= min_dim:
-            n_comps = min_dim - 1
-        else:
-            n_comps = settings.N_PCS
+        n_comps = min_dim - 1 if min_dim <= settings.N_PCS else settings.N_PCS
 
     logg.info(f"    with n_comps={n_comps}")
 
@@ -395,7 +392,7 @@ def _handle_mask_var(
     if use_highly_variable or (
         use_highly_variable is None
         and mask_var is _empty
-        and "highly_variable" in adata.var.keys()
+        and "highly_variable" in adata.var.columns
     ):
         mask_var = "highly_variable"
 
diff --git a/src/scanpy/preprocessing/_scrublet/__init__.py b/src/scanpy/preprocessing/_scrublet/__init__.py
index 976dafe89f..d57eb81750 100644
--- a/src/scanpy/preprocessing/_scrublet/__init__.py
+++ b/src/scanpy/preprocessing/_scrublet/__init__.py
@@ -245,7 +245,7 @@ def _run_scrublet(ad_obs: AnnData, ad_sim: AnnData | None = None):
         return {"obs": ad_obs.obs, "uns": ad_obs.uns["scrublet"]}
 
     if batch_key is not None:
-        if batch_key not in adata.obs.keys():
+        if batch_key not in adata.obs.columns:
             msg = (
                 "`batch_key` must be a column of .obs in the input AnnData object,"
                 f"but {batch_key!r} is not in {adata.obs.keys()!r}."
diff --git a/src/scanpy/preprocessing/_simple.py b/src/scanpy/preprocessing/_simple.py
index 6cf42cccb7..073af955e9 100644
--- a/src/scanpy/preprocessing/_simple.py
+++ b/src/scanpy/preprocessing/_simple.py
@@ -376,10 +376,7 @@ def log1p_array(X: np.ndarray, *, base: Number | None = None, copy: bool = False
     # Can force arrays to be np.ndarrays, but would be useful to not
     # X = check_array(X, dtype=(np.float64, np.float32), ensure_2d=False, copy=copy)
     if copy:
-        if not np.issubdtype(X.dtype, np.floating):
-            X = X.astype(float)
-        else:
-            X = X.copy()
+        X = X.astype(float) if not np.issubdtype(X.dtype, np.floating) else X.copy()
     elif not (np.issubdtype(X.dtype, np.floating) or np.issubdtype(X.dtype, complex)):
         X = X.astype(float)
     np.log1p(X, out=X)
@@ -700,10 +697,7 @@ def regress_out(
     # regress on one or several ordinal variables
     else:
         # create data frame with selected keys (if given)
-        if keys:
-            regressors = adata.obs[keys]
-        else:
-            regressors = adata.obs.copy()
+        regressors = adata.obs[keys] if keys else adata.obs.copy()
 
         # add column of ones at index 0 (first column)
         regressors.insert(0, "ones", 1.0)
@@ -720,10 +714,7 @@ def regress_out(
     for idx, data_chunk in enumerate(chunk_list):
         # each task is a tuple of a data_chunk eg. (adata.X[:,0:100]) and
         # the regressors. This data will be passed to each of the jobs.
-        if variable_is_categorical:
-            regres = regressors_chunk[idx]
-        else:
-            regres = regressors
+        regres = regressors_chunk[idx] if variable_is_categorical else regressors
         tasks.append(tuple((data_chunk, regres, variable_is_categorical)))
 
     from joblib import Parallel, delayed
diff --git a/src/scanpy/readwrite.py b/src/scanpy/readwrite.py
index 90179daf46..9e0a298e18 100644
--- a/src/scanpy/readwrite.py
+++ b/src/scanpy/readwrite.py
@@ -703,13 +703,12 @@ def read_params(
 
     params = OrderedDict([])
     for line in filename.open():
-        if "=" in line:
-            if not as_header or line.startswith("#"):
-                line = line[1:] if line.startswith("#") else line
-                key, val = line.split("=")
-                key = key.strip()
-                val = val.strip()
-                params[key] = convert_string(val)
+        if "=" in line and (not as_header or line.startswith("#")):
+            line = line[1:] if line.startswith("#") else line
+            key, val = line.split("=")
+            key = key.strip()
+            val = val.strip()
+            params[key] = convert_string(val)
     return params
 
 
diff --git a/src/scanpy/tools/_dpt.py b/src/scanpy/tools/_dpt.py
index 8c5f7d857b..c0fa59262f 100644
--- a/src/scanpy/tools/_dpt.py
+++ b/src/scanpy/tools/_dpt.py
@@ -137,7 +137,7 @@ def dpt(
             "    adata.uns['iroot'] = root_cell_index\n"
             "    adata.var['xroot'] = adata[root_cell_name, :].X"
         )
-    if "X_diffmap" not in adata.obsm.keys():
+    if "X_diffmap" not in adata.obsm:
         logg.warning(
             "Trying to run `tl.dpt` without prior call of `tl.diffmap`. "
             "Falling back to `tl.diffmap` with default parameters."
diff --git a/src/scanpy/tools/_draw_graph.py b/src/scanpy/tools/_draw_graph.py
index b36638b5b5..4e8c91fb1f 100644
--- a/src/scanpy/tools/_draw_graph.py
+++ b/src/scanpy/tools/_draw_graph.py
@@ -130,7 +130,7 @@ def draw_graph(
     if adjacency is None:
         adjacency = _choose_graph(adata, obsp, neighbors_key)
     # init coordinates
-    if init_pos in adata.obsm.keys():
+    if init_pos in adata.obsm:
         init_coords = adata.obsm[init_pos]
     elif init_pos == "paga" or init_pos:
         init_coords = get_init_pos_from_paga(
diff --git a/src/scanpy/tools/_ingest.py b/src/scanpy/tools/_ingest.py
index 136f58af46..9ced418888 100644
--- a/src/scanpy/tools/_ingest.py
+++ b/src/scanpy/tools/_ingest.py
@@ -297,16 +297,12 @@ def _init_neighbors(self, adata, neighbors_key):
             self._use_rep = "X_pca"
             self._n_pcs = neighbors["params"]["n_pcs"]
             self._rep = adata.obsm["X_pca"][:, : self._n_pcs]
-        elif adata.n_vars > settings.N_PCS and "X_pca" in adata.obsm.keys():
+        elif adata.n_vars > settings.N_PCS and "X_pca" in adata.obsm:
             self._use_rep = "X_pca"
             self._rep = adata.obsm["X_pca"][:, : settings.N_PCS]
             self._n_pcs = self._rep.shape[1]
 
-        if "metric_kwds" in neighbors["params"]:
-            self._metric_kwds = neighbors["params"]["metric_kwds"]
-        else:
-            self._metric_kwds = {}
-
+        self._metric_kwds = neighbors["params"].get("metric_kwds", {})
         self._metric = neighbors["params"]["metric"]
 
         self._neigh_random_state = neighbors["params"].get("random_state", 0)
@@ -317,7 +313,7 @@ def _init_pca(self, adata):
         self._pca_use_hvg = adata.uns["pca"]["params"]["use_highly_variable"]
 
         mask = "highly_variable"
-        if self._pca_use_hvg and mask not in adata.var.keys():
+        if self._pca_use_hvg and mask not in adata.var.columns:
             msg = f"Did not find `adata.var[{mask!r}']`."
             raise ValueError(msg)
 
@@ -376,7 +372,7 @@ def _same_rep(self):
             return self._pca(self._n_pcs)
         if self._use_rep == "X":
             return adata.X
-        if self._use_rep in adata.obsm.keys():
+        if self._use_rep in adata.obsm:
             return adata.obsm[self._use_rep]
         return adata.X
 
diff --git a/src/scanpy/tools/_louvain.py b/src/scanpy/tools/_louvain.py
index e6259f035d..d3e616a850 100644
--- a/src/scanpy/tools/_louvain.py
+++ b/src/scanpy/tools/_louvain.py
@@ -163,10 +163,7 @@ def louvain(
         if not directed:
             logg.debug("    using the undirected graph")
         g = _utils.get_igraph_from_adjacency(adjacency, directed=directed)
-        if use_weights:
-            weights = np.array(g.es["weight"]).astype(np.float64)
-        else:
-            weights = None
+        weights = np.array(g.es["weight"]).astype(np.float64) if use_weights else None
         if flavor == "vtraag":
             import louvain
 
diff --git a/src/scanpy/tools/_marker_gene_overlap.py b/src/scanpy/tools/_marker_gene_overlap.py
index 1c286e9333..a1d71cf993 100644
--- a/src/scanpy/tools/_marker_gene_overlap.py
+++ b/src/scanpy/tools/_marker_gene_overlap.py
@@ -30,10 +30,7 @@ def _calc_overlap_count(markers1: dict, markers2: dict):
     overlaps = np.zeros((len(markers1), len(markers2)))
 
     for j, marker_group in enumerate(markers1):
-        tmp = [
-            len(markers2[i].intersection(markers1[marker_group]))
-            for i in markers2.keys()
-        ]
+        tmp = [len(markers2[i].intersection(markers1[marker_group])) for i in markers2]
         overlaps[j, :] = tmp
 
     return overlaps
@@ -51,7 +48,7 @@ def _calc_overlap_coef(markers1: dict, markers2: dict):
         tmp = [
             len(markers2[i].intersection(markers1[marker_group]))
             / max(min(len(markers2[i]), len(markers1[marker_group])), 1)
-            for i in markers2.keys()
+            for i in markers2
         ]
         overlap_coef[j, :] = tmp
 
@@ -70,7 +67,7 @@ def _calc_jaccard(markers1: dict, markers2: dict):
         tmp = [
             len(markers2[i].intersection(markers1[marker_group]))
             / len(markers2[i].union(markers1[marker_group]))
-            for i in markers2.keys()
+            for i in markers2
         ]
         jacc_results[j, :] = tmp
 
diff --git a/src/scanpy/tools/_paga.py b/src/scanpy/tools/_paga.py
index 98f0ac622b..98146b83e2 100644
--- a/src/scanpy/tools/_paga.py
+++ b/src/scanpy/tools/_paga.py
@@ -196,10 +196,7 @@ def _compute_connectivities_v1_2(self):
         inter_es = inter_es.tocoo()
         for i, j, v in zip(inter_es.row, inter_es.col, inter_es.data):
             expected_random_null = (es[i] * ns[j] + es[j] * ns[i]) / (n - 1)
-            if expected_random_null != 0:
-                scaled_value = v / expected_random_null
-            else:
-                scaled_value = 1
+            scaled_value = v / expected_random_null if expected_random_null != 0 else 1
             if scaled_value > 1:
                 scaled_value = 1
             connectivities[i, j] = scaled_value
@@ -229,10 +226,7 @@ def _compute_connectivities_v1_0(self):
         for i, j, v in zip(inter_es.row, inter_es.col, inter_es.data):
             # have n_neighbors**2 inside sqrt for backwards compat
             geom_mean_approx_knn = np.sqrt(n_neighbors_sq * ns[i] * ns[j])
-            if geom_mean_approx_knn != 0:
-                scaled_value = v / geom_mean_approx_knn
-            else:
-                scaled_value = 1
+            scaled_value = v / geom_mean_approx_knn if geom_mean_approx_knn != 0 else 1
             connectivities[i, j] = scaled_value
         # set attributes
         self.ns = ns
diff --git a/src/scanpy/tools/_rank_genes_groups.py b/src/scanpy/tools/_rank_genes_groups.py
index 3327fe7501..56b71d55eb 100644
--- a/src/scanpy/tools/_rank_genes_groups.py
+++ b/src/scanpy/tools/_rank_genes_groups.py
@@ -287,10 +287,7 @@ def wilcoxon(
             # initialize space for z-scores
             scores = np.zeros(n_genes)
             # initialize space for tie correction coefficients
-            if tie_correct:
-                T = np.zeros(n_genes)
-            else:
-                T = 1
+            T = np.zeros(n_genes) if tie_correct else 1
 
             for group_index, mask_obs in enumerate(self.groups_masks_obs):
                 if group_index == self.ireference:
@@ -346,10 +343,7 @@ def wilcoxon(
             for group_index, mask_obs in enumerate(self.groups_masks_obs):
                 n_active = np.count_nonzero(mask_obs)
 
-                if tie_correct:
-                    T_i = T[group_index]
-                else:
-                    T_i = 1
+                T_i = T[group_index] if tie_correct else 1
 
                 std_dev = np.sqrt(
                     T_i * n_active * (n_cells - n_active) * (n_cells + 1) / 12.0
@@ -733,10 +727,7 @@ def rank_genes_groups(
 
 
 def _calc_frac(X):
-    if issparse(X):
-        n_nonzero = X.getnnz(axis=0)
-    else:
-        n_nonzero = np.count_nonzero(X, axis=0)
+    n_nonzero = X.getnnz(axis=0) if issparse(X) else np.count_nonzero(X, axis=0)
     return n_nonzero / X.shape[0]
 
 
diff --git a/src/scanpy/tools/_sim.py b/src/scanpy/tools/_sim.py
index bf562b8408..7410442952 100644
--- a/src/scanpy/tools/_sim.py
+++ b/src/scanpy/tools/_sim.py
@@ -198,7 +198,7 @@ def sample_dynamic_data(**params):
                         X[::step],
                         dir=writedir,
                         noiseObs=noiseObs,
-                        append=(False if restart == 0 else True),
+                        append=restart != 0,
                         branching=branching,
                         nrRealizations=nrRealizations,
                     )
@@ -208,7 +208,7 @@ def sample_dynamic_data(**params):
                         noiseDyn * np.random.randn(500, 3),
                         dir=writedir,
                         noiseObs=noiseObs,
-                        append=(False if restart == 0 else True),
+                        append=restart != 0,
                         branching=branching,
                         nrRealizations=nrRealizations,
                     )
@@ -270,7 +270,7 @@ def sample_dynamic_data(**params):
                         X[::step],
                         dir=writedir,
                         noiseObs=noiseObs,
-                        append=(False if restart == 0 else True),
+                        append=restart != 0,
                         branching=branching,
                         nrRealizations=nrRealizations,
                     )
@@ -367,7 +367,7 @@ def write_data(
     # variable names
     if varNames:
         header += f'{"it":>2} '
-        for v in varNames.keys():
+        for v in varNames:
             header += f"{v:>7} "
     with (dir / f"sim_{id}.txt").open("ab" if append else "wb") as f:
         np.savetxt(
@@ -430,7 +430,7 @@ def __init__(
         # checks
         if initType not in ["branch", "random"]:
             raise RuntimeError("initType must be either: branch, random")
-        if model not in self.availModels.keys():
+        if model not in self.availModels:
             message = "model not among predefined models \n"  # noqa: F841  # TODO FIX
         # read from file
         from .. import sim_models
@@ -605,12 +605,12 @@ def set_coupl(self, Coupl=None):
         or via sampling.
         """
         self.varNames = {str(i): i for i in range(self.dim)}
-        if self.model not in self.availModels.keys() and Coupl is None:
+        if self.model not in self.availModels and Coupl is None:
             self.read_model()
         elif "var" in self.model.name:
             # vector auto regressive process
             self.Coupl = Coupl
-            self.boolRules = {s: "" for s in self.varNames.keys()}
+            self.boolRules = {s: "" for s in self.varNames}
             names = list(self.varNames.keys())
             for gp in range(self.dim):
                 pas = []
@@ -819,7 +819,7 @@ def parents_from_boolRule(self, rule):
         pa_old = []
         pa_delete = []
         for pa in rule_pa:
-            if pa not in self.varNames.keys():
+            if pa not in self.varNames:
                 settings.m(0, "list of available variables:")
                 settings.m(0, list(self.varNames.keys()))
                 message = (
@@ -842,12 +842,11 @@ def parents_from_boolRule(self, rule):
     def build_boolCoeff(self):
         """Compute coefficients for tuple space."""
         # coefficients for hill functions from boolean update rules
-        self.boolCoeff = {s: [] for s in self.varNames.keys()}
+        self.boolCoeff = {s: [] for s in self.varNames}
         # parents
-        self.pas = {s: [] for s in self.varNames.keys()}
+        self.pas = {s: [] for s in self.varNames}
         #
-        for key in self.boolRules.keys():
-            rule = self.boolRules[key]
+        for key, rule in self.boolRules.items():
             self.pas[key] = self.parents_from_boolRule(rule)
             pasIndices = [self.varNames[pa] for pa in self.pas[key]]
             # check whether there are coupling matrix entries for each parent
@@ -1150,11 +1149,10 @@ def sim_givenAdj(self, Adj: np.ndarray, model="line"):
         # if there is more than a child with a single parent
         # order these children (there are two in three dim)
         # by distance to the source/parent
-        if nrchildren_par[1] > 1:
-            if Adj[children_sorted[0], parents[0]] == 0:
-                help = children_sorted[0]
-                children_sorted[0] = children_sorted[1]
-                children_sorted[1] = help
+        if nrchildren_par[1] > 1 and Adj[children_sorted[0], parents[0]] == 0:
+            help = children_sorted[0]
+            children_sorted[0] = children_sorted[1]
+            children_sorted[1] = help
 
         for gp in children_sorted:
             for g in range(dim):
diff --git a/src/scanpy/tools/_top_genes.py b/src/scanpy/tools/_top_genes.py
index 00dc764d82..d66e9232f0 100644
--- a/src/scanpy/tools/_top_genes.py
+++ b/src/scanpy/tools/_top_genes.py
@@ -181,10 +181,7 @@ def ROC_AUC_analysis(
     y_true = mask
     for i, j in enumerate(name_list):
         vec = adata[:, [j]].X
-        if issparse(vec):
-            y_score = vec.todense()
-        else:
-            y_score = vec
+        y_score = vec.todense() if issparse(vec) else vec
 
         (
             fpr[name_list[i]],
diff --git a/src/scanpy/tools/_umap.py b/src/scanpy/tools/_umap.py
index c23b5551fa..4f225da2a1 100644
--- a/src/scanpy/tools/_umap.py
+++ b/src/scanpy/tools/_umap.py
@@ -187,7 +187,7 @@ def umap(
     if a is None or b is None:
         a, b = find_ab_params(spread, min_dist)
     adata.uns[key_uns] = dict(params=dict(a=a, b=b))
-    if isinstance(init_pos, str) and init_pos in adata.obsm.keys():
+    if isinstance(init_pos, str) and init_pos in adata.obsm:
         init_coords = adata.obsm[init_pos]
     elif isinstance(init_pos, str) and init_pos == "paga":
         init_coords = get_init_pos_from_paga(
diff --git a/src/scanpy/tools/_utils.py b/src/scanpy/tools/_utils.py
index b3ffa7d324..97e2de0df1 100644
--- a/src/scanpy/tools/_utils.py
+++ b/src/scanpy/tools/_utils.py
@@ -30,7 +30,7 @@ def _choose_representation(
         use_rep = "X"
     if use_rep is None:
         if adata.n_vars > settings.N_PCS:
-            if "X_pca" in adata.obsm.keys():
+            if "X_pca" in adata.obsm:
                 if n_pcs is not None and n_pcs > adata.obsm["X_pca"].shape[1]:
                     raise ValueError(
                         "`X_pca` does not have enough PCs. Rerun `sc.pp.pca` with adjusted `n_comps`."
@@ -50,7 +50,7 @@ def _choose_representation(
             logg.info("    using data matrix X directly")
             X = adata.X
     else:
-        if use_rep in adata.obsm.keys() and n_pcs is not None:
+        if use_rep in adata.obsm and n_pcs is not None:
             if n_pcs > adata.obsm[use_rep].shape[1]:
                 raise ValueError(
                     f"{use_rep} does not have enough Dimensions. Provide a "
@@ -58,7 +58,7 @@ def _choose_representation(
                     "`n_pcs` or lower `n_pcs` "
                 )
             X = adata.obsm[use_rep][:, :n_pcs]
-        elif use_rep in adata.obsm.keys() and n_pcs is None:
+        elif use_rep in adata.obsm and n_pcs is None:
             X = adata.obsm[use_rep]
         elif use_rep == "X":
             X = adata.X
diff --git a/tests/notebooks/test_paga_paul15_subsampled.py b/tests/notebooks/test_paga_paul15_subsampled.py
index 6d4ee886ba..9ce6ea8319 100644
--- a/tests/notebooks/test_paga_paul15_subsampled.py
+++ b/tests/notebooks/test_paga_paul15_subsampled.py
@@ -129,7 +129,7 @@ def test_paga_paul15_subsampled(image_comparer, plt):
             left_margin=0.15,
             n_avg=50,
             annotations=["distance"],
-            show_yticks=True if ipath == 0 else False,
+            show_yticks=ipath == 0,
             show_colorbar=False,
             color_map="Greys",
             color_maps_annotations={"distance": "viridis"},
diff --git a/tests/test_highly_variable_genes.py b/tests/test_highly_variable_genes.py
index f3b9298505..cdd5238c70 100644
--- a/tests/test_highly_variable_genes.py
+++ b/tests/test_highly_variable_genes.py
@@ -255,7 +255,7 @@ def test_pearson_residuals_general(
         "residual_variances",
         "highly_variable_rank",
     ]:
-        assert key in output_df.keys()
+        assert key in output_df.columns
 
     # check consistency with normalization method
     if subset:
@@ -324,7 +324,7 @@ def test_pearson_residuals_batch(pbmc3k_parametrized_small, subset, n_top_genes)
         "highly_variable_nbatches",
         "highly_variable_intersection",
     ]:
-        assert key in output_df.keys()
+        assert key in output_df.columns
 
     # general checks on ranks, hvg flag and residual variance
     _check_pearson_hvg_columns(output_df, n_top_genes)
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index 92eb61c252..b34db78780 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -1590,11 +1590,13 @@ def test_color_cycler(caplog):
     colors = sns.color_palette("deep")
     cyl = sns.rcmod.cycler("color", sns.color_palette("deep"))
 
-    with caplog.at_level(logging.WARNING):
-        with plt.rc_context({"axes.prop_cycle": cyl, "patch.facecolor": colors[0]}):
-            sc.pl.umap(pbmc, color="phase")
-            plt.show()
-            plt.close()
+    with (
+        caplog.at_level(logging.WARNING),
+        plt.rc_context({"axes.prop_cycle": cyl, "patch.facecolor": colors[0]}),
+    ):
+        sc.pl.umap(pbmc, color="phase")
+        plt.show()
+        plt.close()
 
     assert caplog.text == ""
 
diff --git a/tests/test_rank_genes_groups_logreg.py b/tests/test_rank_genes_groups_logreg.py
index 3cc294487e..618de375f7 100644
--- a/tests/test_rank_genes_groups_logreg.py
+++ b/tests/test_rank_genes_groups_logreg.py
@@ -40,7 +40,7 @@ def test_rank_genes_groups_with_renamed_categories_use_rep():
     assert adata.uns["rank_genes_groups"]["names"][0].tolist() == ("1", "3", "0")
 
     sc.tl.rank_genes_groups(adata, "blobs", method="logreg")
-    assert not adata.uns["rank_genes_groups"]["names"][0].tolist() == ("3", "1", "0")
+    assert adata.uns["rank_genes_groups"]["names"][0].tolist() != ("3", "1", "0")
 
 
 def test_rank_genes_groups_with_unsorted_groups():
diff --git a/tests/test_scaling.py b/tests/test_scaling.py
index fad2443dc1..0ad62bbc7d 100644
--- a/tests/test_scaling.py
+++ b/tests/test_scaling.py
@@ -120,7 +120,7 @@ def test_mask_string():
     adata.obs["some cells"] = np.array((0, 0, 1, 1, 1, 0, 0), dtype=bool)
     sc.pp.scale(adata, mask_obs="some cells")
     assert np.array_equal(adata.X, X_centered_for_mask)
-    assert "mean of some cells" in adata.var.keys()
+    assert "mean of some cells" in adata.var.columns
 
 
 @pytest.mark.parametrize("zero_center", [True, False])