diff --git a/nbs/losses.ipynb b/nbs/losses.ipynb
index 6bc3be9..72adabd 100644
--- a/nbs/losses.ipynb
+++ b/nbs/losses.ipynb
@@ -652,7 +652,6 @@
     "            .sub(df[target_col], axis=0)\n",
     "            .abs()\n",
     "            .div(_zero_to_nan(df[target_col].abs()), axis=0)\n",
-    "            .fillna(0)\n",
     "            .groupby(df[id_col], observed=True).mean()\n",
     "        )\n",
     "        res.index.name = id_col\n",
@@ -662,7 +661,7 @@
     "            abs_err = pl.col(target_col).sub(pl.col(model)).abs()\n",
     "            abs_target = _zero_to_nan(pl.col(target_col))\n",
     "            ratio = abs_err.truediv(abs_target).alias(model)\n",
-    "            return ratio.fill_nan(0)\n",
+    "            return ratio.fill_nan(None)\n",
     "\n",
     "        res = _pl_agg_expr(df, models, id_col, gen_expr)\n",
     "    return res"
diff --git a/settings.ini b/settings.ini
index 0df391f..b91719a 100644
--- a/settings.ini
+++ b/settings.ini
@@ -29,7 +29,7 @@ user = Nixtla
 requirements = numpy packaging pandas>=1.1.1
 plotting_requirements = pandas[plot] plotly plotly-resampler
 scalers_requirements = numba scipy
-polars_requirements = polars
+polars_requirements = polars numpy<2
 dev_requirements = datasetsforecast==0.0.8 nbdev pyarrow
 readme_nb = index.ipynb
 allowed_metadata_keys = 
diff --git a/utilsforecast/losses.py b/utilsforecast/losses.py
index 2b80d7f..1c6ec4b 100644
--- a/utilsforecast/losses.py
+++ b/utilsforecast/losses.py
@@ -171,7 +171,6 @@ def mape(
             .sub(df[target_col], axis=0)
             .abs()
             .div(_zero_to_nan(df[target_col].abs()), axis=0)
-            .fillna(0)
             .groupby(df[id_col], observed=True)
             .mean()
         )
@@ -183,7 +182,7 @@ def gen_expr(model):
             abs_err = pl.col(target_col).sub(pl.col(model)).abs()
             abs_target = _zero_to_nan(pl.col(target_col))
             ratio = abs_err.truediv(abs_target).alias(model)
-            return ratio.fill_nan(0)
+            return ratio.fill_nan(None)
 
         res = _pl_agg_expr(df, models, id_col, gen_expr)
     return res