diff --git a/exercises/Pandas_WeatherData/WeatherData_Analysis_tasks.ipynb b/exercises/Pandas_WeatherData/WeatherData_Analysis_tasks.ipynb
index 2c43dd1d9732148221b00cd35531906909c14e3c..698ce0d7c27e238d47de7a6d48550dbc0f7bd43f 100644
--- a/exercises/Pandas_WeatherData/WeatherData_Analysis_tasks.ipynb
+++ b/exercises/Pandas_WeatherData/WeatherData_Analysis_tasks.ipynb
@@ -107,14 +107,6 @@
     "After having imported the data gather some `info`rmation on the data (e.g. datatypes of columns or memory usage)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff8e949d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "06461f7c",
@@ -351,9 +343,7 @@
    "id": "ceaaacae",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "grouped_by_month = df_weather_tweaked.groupby(df_weather_tweaked.index.month)"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -369,30 +359,7 @@
    "id": "b9251d7e",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 6), sharex=\"col\")\n",
-    "\n",
-    "ax2.set_xticks( range(1, 13 ) )\n",
-    "ax2.set_xlabel(\"month\")\n",
-    "\n",
-    "ax1.set_ylabel(\"Temperature / degree Celsius\")\n",
-    "ax1.violinplot(\n",
-    "    [\n",
-    "        df[\"Temperature\"]\n",
-    "        for _, df in grouped_by_month \n",
-    "    ]\n",
-    ");\n",
-    "ax2.set_ylabel(\"Relative Humidity /% \")\n",
-    "ax2.violinplot(\n",
-    "    [\n",
-    "        df[\"Humidity\"]\n",
-    "        for _, df in grouped_by_month \n",
-    "    ]\n",
-    ");\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -442,22 +409,6 @@
    "metadata": {},
    "outputs": [],
    "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9e943ec",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c62bbfcd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/slides/Day2_PandasDataFrames.ipynb b/slides/Day2_PandasDataFrames.ipynb
index 38a5542c2c4bde2b18ecd6390dbaf26e9f5da85c..288ef4f76211849868b2ac8055e7f3dfe237e890 100644
--- a/slides/Day2_PandasDataFrames.ipynb
+++ b/slides/Day2_PandasDataFrames.ipynb
@@ -2213,11 +2213,7 @@
    "id": "53d45569",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "cols=[\"sepal length\",\"sepal width\",\"petal length\",\"petal width\"]\n",
-    "df_agg= df.loc[:,cols].agg([np.min, np.max, np.mean, np.std])\n",
-    "df_agg"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -2233,10 +2229,7 @@
    "id": "52a96d54",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "df.loc[:,cols] =((df.loc[:,cols]  - df_agg.loc['mean',:] )/ df_agg.loc['std',:])\n",
-    "df.describe()"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -2270,9 +2263,7 @@
    "id": "801dd946",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "grouped_by_species = df.groupby(by=[\"Name\"])"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -2280,12 +2271,7 @@
    "id": "90b8c49e",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "fig, axs = plt.subplots(1,3)\n",
-    "for ax, (name, group_data) in zip(axs,grouped_by_species):    \n",
-    "    group_data.boxplot(ax=ax)\n",
-    "    ax.set_title(name)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -2299,7 +2285,7 @@
  "metadata": {
   "celltoolbar": "Slideshow",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },