diff --git a/chart_utils.py b/chart_utils.py
index 3f9b8ef..ba3d9f4 100644
--- a/chart_utils.py
+++ b/chart_utils.py
@@ -4,6 +4,8 @@ from pandas import DataFrame
 
 from data_utils import get_categorical_stats
 
+CAT_Q_RADIUS = 0.1
+
 gender_colormap = factor_cmap("Sukupuoli", ["#4834d4", "#eb4d4b"], ["mies", "nainen"])
 
 
@@ -26,7 +28,35 @@ def get_categorical_stats_plot(df, *, category, na_as_category=None):
         title=f"{category}/tulot", x_range=list(df[category].cat.categories)
     )
     set_yaxis_cash(plot)
-    plot.vbar(df[category], 0.4, df["max"], df["min"], color="#a4b0be")
+    plot.vbar(
+        df[category],
+        CAT_Q_RADIUS * 2.5,
+        df["max"],
+        df["min"],
+        color="#a4b0be",
+        fill_alpha=0.7,
+    )
+    plot.circle(
+        df[category],
+        df["q25"],
+        radius=CAT_Q_RADIUS,
+        legend_label="q25",
+        color="#f368e0",
+    )
+    plot.circle(
+        df[category],
+        df["q75"],
+        radius=CAT_Q_RADIUS,
+        legend_label="q75",
+        color="#00d2d3",
+    )
+    plot.circle(
+        df[category],
+        df["q90"],
+        radius=CAT_Q_RADIUS,
+        legend_label="q90",
+        color="#ff9f43",
+    )
     plot.line(
         df[category], df["median"], legend_label="median", color="#1289A7", line_width=4
     )
diff --git a/data_utils.py b/data_utils.py
index b14ae62..f831525 100644
--- a/data_utils.py
+++ b/data_utils.py
@@ -3,6 +3,22 @@ from typing import Optional
 import pandas as pd
 
 
+def q25(x):
+    return x.quantile(0.25)
+
+
+def q50(x):
+    return x.quantile(0.5)
+
+
+def q75(x):
+    return x.quantile(0.75)
+
+
+def q90(x):
+    return x.quantile(0.9)
+
+
 def get_categorical_stats(
     df: pd.DataFrame,
     category_col: str,
@@ -20,4 +36,6 @@ def get_categorical_stats(
         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", "count"])
+    return group[value_col].agg(
+        ["mean", "min", "max", "median", "count", q25, q50, q75, q90]
+    )