diff --git a/numpy.ipynb b/numpy.ipynb
index 2fbe7adb9372df0a585f6162fd1a8b3697e191dc..96acebad0c7b1e98ca1ffcdfc808153c855e3e2c 100644
--- a/numpy.ipynb
+++ b/numpy.ipynb
@@ -495,6 +495,98 @@
     "Es lohnt sich, einen kurzen Blick auf alle bereits definierten `ufunc`s zu werfen:\n",
     "[Available ufuncs](https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Was muss man kennen?\n",
+    "\n",
+    "Numpy und andere Bibliotheken des Scientific Computing in Python sind groß, man kann kaum alle Funktionen kennen.\n",
+    "In diesem Skript liefert die händisch bereinigte Ausgabe von `cat *.ipynb | egrep -o \"np\\.([^(]+)\" | sort | uniq` diese vollständige Liste. Diese muss nicht auswendig gelernt werden, gibt aber eine klare Abschätzung nach oben. Mehr muss man definitiv auf keinen Fall können! Außerdem sollte man bedenken, dass diese Liste für das gesamte Skript gilt, und wir vieles davon noch nicht kennen gelernt haben."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "np.abs\n",
+    "np.all\n",
+    "np.allclose\n",
+    "np.arange\n",
+    "np.argmin\n",
+    "np.array\n",
+    "np.atleast_1d\n",
+    "np.byte\n",
+    "np.column_stack\n",
+    "np.concatenate\n",
+    "np.copy\n",
+    "np.corrcoef\n",
+    "np.cos\n",
+    "np.cov\n",
+    "np.diff\n",
+    "np.dot\n",
+    "np.dstack\n",
+    "np.equal\n",
+    "np.exp\n",
+    "np.eye\n",
+    "np.float64\n",
+    "np.frombuffer\n",
+    "np.full\n",
+    "np.genfromtxt\n",
+    "np.heaviside\n",
+    "np.hstack\n",
+    "np.identity\n",
+    "np.isclose\n",
+    "np.isnan\n",
+    "np.linalg.eig\n",
+    "np.linalg.inv\n",
+    "np.linalg.norm\n",
+    "np.linalg.svd\n",
+    "np.linspace\n",
+    "np.log\n",
+    "np.matmul\n",
+    "np.max\n",
+    "np.maximum\n",
+    "np.may_share_memory\n",
+    "np.mean\n",
+    "np.median\n",
+    "np.meshgrid\n",
+    "np.mgrid\n",
+    "np.min\n",
+    "np.minimum\n",
+    "np.nansum\n",
+    "np.ones\n",
+    "np.ones_like\n",
+    "np.outer\n",
+    "np.pi\n",
+    "np.power\n",
+    "np.ptp\n",
+    "np.random.choice\n",
+    "np.random.multivariate_normal\n",
+    "np.random.permutation\n",
+    "np.random.rand\n",
+    "np.random.randint\n",
+    "np.random.randn\n",
+    "np.random.random\n",
+    "np.random.seed\n",
+    "np.random.uniform\n",
+    "np.round\n",
+    "np.sin\n",
+    "np.size\n",
+    "np.sort\n",
+    "np.sqrt\n",
+    "np.square\n",
+    "np.stack\n",
+    "np.std\n",
+    "np.sum\n",
+    "np.uint8\n",
+    "np.unique\n",
+    "np.var\n",
+    "np.vstack\n",
+    "np.zeros\n",
+    "np.zeros_like"
+   ]
   }
  ],
  "metadata": {