diff --git a/scvelo/core/_anndata.py b/scvelo/core/_anndata.py
index eac66cb6..2a05666f 100644
--- a/scvelo/core/_anndata.py
+++ b/scvelo/core/_anndata.py
@@ -1,4 +1,5 @@
 import re
+import warnings
 from typing import List, Literal, Optional, Union
 
 import numpy as np
@@ -178,6 +179,13 @@ def get_df(
     :class:`pd.DataFrame`
         A dataframe.
     """
+    warnings.warn(
+        "`get_df` is deprecated since scvelo==0.4.0 and will be removed in a future version "
+        "of scVelo. Please `AnnData::get_df` or Scanpy's `scanpy.get.obs_df` or `scanpy.get.var_df`.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
     if precision is not None:
         pd.set_option("display.precision", precision)
 
@@ -188,8 +196,6 @@ def get_df(
         keys, key_add = (
             keys.split("/") if isinstance(keys, str) and "/" in keys else (keys, None)
         )
-        keys = [keys] if isinstance(keys, str) else keys
-        key = keys[0]
 
         s_keys = ["obs", "var", "obsm", "varm", "uns", "layers"]
         d_keys = [
@@ -207,62 +213,68 @@ def get_df(
 
         if keys is None:
             df = data.to_df()
-        elif key in data.var_names:
-            df = obs_df(data, keys, layer=layer)
-        elif key in data.obs_names:
-            df = var_df(data, keys, layer=layer)
         else:
-            if keys_split is not None:
-                keys = [
-                    k
-                    for k in list(data.obs.keys()) + list(data.var.keys())
-                    if key in k and keys_split in k
-                ]
-                key = keys[0]
-            s_key = [s for (s, d_key) in zip(s_keys, d_keys) if key in d_key]
-            if len(s_key) == 0:
-                raise ValueError(f"'{key}' not found in any of {', '.join(s_keys)}.")
-            if len(s_key) > 1:
-                logg.warn(f"'{key}' found multiple times in {', '.join(s_key)}.")
-
-            s_key = s_key[-1]
-            df = getattr(data, s_key)[keys if len(keys) > 1 else key]
-            if key_add is not None:
-                df = df[key_add]
-            if index is None:
-                index = (
-                    data.var_names
-                    if s_key == "varm"
-                    else data.obs_names
-                    if s_key in {"obsm", "layers"}
-                    else None
-                )
-                if index is None and s_key == "uns" and hasattr(df, "shape"):
-                    key_cats = np.array(
-                        [
-                            key
-                            for key in data.obs.keys()
-                            if is_categorical_dtype(data.obs[key])
-                        ]
-                    )
-                    num_cats = [
-                        len(data.obs[key].cat.categories) == df.shape[0]
-                        for key in key_cats
+            keys = [keys] if isinstance(keys, str) else keys
+            key = keys[0]
+
+            if key in data.var_names:
+                df = obs_df(data, keys, layer=layer)
+            elif key in data.obs_names:
+                df = var_df(data, keys, layer=layer)
+            else:
+                if keys_split is not None:
+                    keys = [
+                        k
+                        for k in list(data.obs.keys()) + list(data.var.keys())
+                        if key in k and keys_split in k
                     ]
-                    if np.sum(num_cats) == 1:
-                        index = data.obs[key_cats[num_cats][0]].cat.categories
-                        if (
-                            columns is None
-                            and len(df.shape) > 1
-                            and df.shape[0] == df.shape[1]
-                        ):
-                            columns = index
-            elif isinstance(index, str) and index in data.obs.keys():
-                index = pd.Categorical(data.obs[index]).categories
-            if columns is None and s_key == "layers":
-                columns = data.var_names
-            elif isinstance(columns, str) and columns in data.obs.keys():
-                columns = pd.Categorical(data.obs[columns]).categories
+                    key = keys[0]
+                s_key = [s for (s, d_key) in zip(s_keys, d_keys) if key in d_key]
+                if len(s_key) == 0:
+                    raise ValueError(
+                        f"'{key}' not found in any of {', '.join(s_keys)}."
+                    )
+                if len(s_key) > 1:
+                    logg.warn(f"'{key}' found multiple times in {', '.join(s_key)}.")
+
+                s_key = s_key[-1]
+                df = getattr(data, s_key)[keys if len(keys) > 1 else key]
+                if key_add is not None:
+                    df = df[key_add]
+                if index is None:
+                    index = (
+                        data.var_names
+                        if s_key == "varm"
+                        else data.obs_names
+                        if s_key in {"obsm", "layers"}
+                        else None
+                    )
+                    if index is None and s_key == "uns" and hasattr(df, "shape"):
+                        key_cats = np.array(
+                            [
+                                key
+                                for key in data.obs.keys()
+                                if is_categorical_dtype(data.obs[key])
+                            ]
+                        )
+                        num_cats = [
+                            len(data.obs[key].cat.categories) == df.shape[0]
+                            for key in key_cats
+                        ]
+                        if np.sum(num_cats) == 1:
+                            index = data.obs[key_cats[num_cats][0]].cat.categories
+                            if (
+                                columns is None
+                                and len(df.shape) > 1
+                                and df.shape[0] == df.shape[1]
+                            ):
+                                columns = index
+                elif isinstance(index, str) and index in data.obs.keys():
+                    index = pd.Categorical(data.obs[index]).categories
+                if columns is None and s_key == "layers":
+                    columns = data.var_names
+                elif isinstance(columns, str) and columns in data.obs.keys():
+                    columns = pd.Categorical(data.obs[columns]).categories
     elif isinstance(data, pd.DataFrame):
         if isinstance(keys, str) and "*" in keys:
             keys, keys_split = keys.split("*")
diff --git a/tests/core/test_anndata.py b/tests/core/test_anndata.py
index 5a0abd16..c1c72e21 100644
--- a/tests/core/test_anndata.py
+++ b/tests/core/test_anndata.py
@@ -573,6 +573,20 @@ def test_data_as_array(
         else:
             assert (df.columns == ["col_1", "col_2"]).all()
 
+    @given(
+        adata=get_adata(
+            max_obs=5,
+            max_vars=5,
+            layer_keys=["layer_1", "layer_2"],
+        ),
+        modality=st.sampled_from([None, "X", "layer_1", "layer_2"]),
+    )
+    def test_default(self, adata: AnnData, modality: Optional[None]):
+        df = get_df(adata, layer=modality)
+
+        assert isinstance(df, pd.DataFrame)
+        np.testing.assert_equal(adata.to_df().values, df.values)
+
 
 class TestGetInitialSize(TestBase):
     @given(