diff --git a/applications/pandas/pandas.ipynb b/applications/pandas/pandas.ipynb
index 69e390f8e11e98c96404072867ddc61d66d3054b..1245aaef9b4ee333e1bea90787b8a823193e0223 100644
--- a/applications/pandas/pandas.ipynb
+++ b/applications/pandas/pandas.ipynb
@@ -963,7 +963,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "titanic.groupby(['class', 'survived']).aggregate((np.median, mad))"
+    "titanic.groupby(['class', 'survived']).aggregate((np.median, 'mad'))"
    ]
   },
   {
@@ -984,7 +984,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_full = titanic.groupby(['class', 'survived']).aggregate((np.median, mad))"
+    "df_full = titanic.groupby(['class', 'survived']).aggregate((np.median, 'mad'))"
    ]
   },
   {
diff --git a/applications/pandas/pandas.md b/applications/pandas/pandas.md
index d7914fb0dfb4abd8569f3de92d9ed4162963d2dd..76dd30c74254105c8e62cb160bcf713a3822b89c 100644
--- a/applications/pandas/pandas.md
+++ b/applications/pandas/pandas.md
@@ -462,7 +462,7 @@ titanic.groupby(['class', pd.cut(titanic.age, bins=(0, 18, 50, np.inf))]).mean()
 We can use the `aggregate` method to apply a different function to each series
 
 ```
-titanic.groupby(['class', 'survived']).aggregate((np.median, mad))
+titanic.groupby(['class', 'survived']).aggregate((np.median, 'mad'))
 ```
 
 Note that both the index (on the left) and the column names (on the top) now
@@ -473,7 +473,7 @@ The short version is that columns can be selected using direct indexing (as
 discussed above)
 
 ```
-df_full = titanic.groupby(['class', 'survived']).aggregate((np.median, mad))
+df_full = titanic.groupby(['class', 'survived']).aggregate((np.median, 'mad'))
 ```
 
 ```