diff --git a/src/pingouin/correlation.py b/src/pingouin/correlation.py
index 4d889d7a..582b52e4 100644
--- a/src/pingouin/correlation.py
+++ b/src/pingouin/correlation.py
@@ -830,7 +830,7 @@ def partial_corr(
         "pearson",
         "spearman",
     ], 'only "pearson" and "spearman" are supported for partial correlation.'
-    assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame."
+    assert hasattr(data, "__dataframe__"), "Data must be compatible with DataFrame"
     assert data.shape[0] > 2, "Data must have at least 3 samples."
     if covar is not None and (x_covar is not None or y_covar is not None):
         raise ValueError("Cannot specify both covar and {x,y}_covar.")
@@ -1189,7 +1189,7 @@ def rm_corr(data=None, x=None, y=None, subject=None):
     from pingouin import ancova, power_corr
 
     # Safety checks
-    assert isinstance(data, pd.DataFrame), "Data must be a DataFrame"
+    assert hasattr(data, "__dataframe__"), "Data must be compatible with DataFrame"
     assert x in data.columns, "The %s column is not in data." % x
     assert y in data.columns, "The %s column is not in data." % y
     assert data[x].dtype.kind in "bfiu", "%s must be numeric." % x
diff --git a/src/pingouin/distribution.py b/src/pingouin/distribution.py
index 1ddf0c58..9e8d128b 100644
--- a/src/pingouin/distribution.py
+++ b/src/pingouin/distribution.py
@@ -207,13 +207,16 @@ def normality(data, dv=None, group=None, method="shapiro", alpha=0.05):
     Pre   0.304021  0.858979    True
     Post  1.265656  0.531088    True
     """
-    assert isinstance(data, (pd.DataFrame, pd.Series, list, np.ndarray))
+    assert isinstance(data, (pd.DataFrame, pd.Series, list, np.ndarray)) or hasattr(
+        data, "__dataframe__"
+    )
     assert method in ["shapiro", "normaltest", "jarque_bera"]
     if isinstance(data, pd.Series):
         data = data.to_frame()
     col_names = ["W", "pval"]
     func = getattr(scipy.stats, method)
-    if isinstance(data, (list, np.ndarray)):
+    # Check if the data is 1D array-like
+    if isinstance(data, (list, np.ndarray)) or (hasattr(data, "__array__") and data.ndim == 1):
         data = np.asarray(data)
         assert data.ndim == 1, "Data must be 1D."
         assert data.size > 3, "Data must have more than 3 samples."
@@ -222,7 +225,7 @@ def normality(data, dv=None, group=None, method="shapiro", alpha=0.05):
         stats.columns = col_names
         stats["normal"] = np.where(stats["pval"] > alpha, True, False)
     else:
-        # Data is a Pandas DataFrame
+        # Any other kind of data is assumed to be DataFrame-like
         if dv is None and group is None:
             # Wide-format
             # Get numeric data only