diff --git a/chart_utils.py b/chart_utils.py
index 6f2db0c..3f9b8ef 100644
--- a/chart_utils.py
+++ b/chart_utils.py
@@ -16,8 +16,10 @@ def set_yaxis_cash(plot):
     plot.yaxis[0].formatter = bm.NumeralTickFormatter(format="€0")
 
 
-def get_categorical_stats_plot(df, *, category):
-    df = get_categorical_stats(df, category, "Vuositulot")
+def get_categorical_stats_plot(df, *, category, na_as_category=None):
+    df = get_categorical_stats(
+        df, category, "Vuositulot", na_as_category=na_as_category
+    )
     df.reset_index(inplace=True)
     df[category] = df[category].astype("category")
     plot = bp.figure(
diff --git a/data_utils.py b/data_utils.py
index 9a4bf3b..b14ae62 100644
--- a/data_utils.py
+++ b/data_utils.py
@@ -1,13 +1,23 @@
+from typing import Optional
+
 import pandas as pd
 
 
 def get_categorical_stats(
-    df: pd.DataFrame, category_col: str, value_col: str
+    df: pd.DataFrame,
+    category_col: str,
+    value_col: str,
+    *,
+    na_as_category: Optional[str] = None,
 ) -> pd.DataFrame:
     # Drop records where value is not numeric before grouping...
     df = df.copy()
     df[value_col] = pd.to_numeric(df[value_col], errors="coerce")
     df = df[df[value_col].notna() & df[value_col] > 0]
+    if na_as_category:
+        df[category_col] = df[category_col].astype("string")
+        df.loc[df[category_col].isna(), category_col] = na_as_category
+        df[category_col] = df[category_col].astype("category")
     # ... then carry on.
     group = df[[category_col, value_col]].groupby(category_col)
-    return group[value_col].agg(["mean", "min", "max", "median"])
+    return group[value_col].agg(["mean", "min", "max", "median", "count"])