docs: adjust notebooks to new API (#770)

### Summary of Changes

The notebooks are now being executed again and work with the new API.

Author: lars-reimann

.github/README.md

@@ -12,8 +12,9 @@ common tasks on small to moderately sized datasets. As such, a major focus is to
 
 Instead of implementing DS methods from scratch, we use established DS libraries under the hood such as:
 
-* [pandas](https://pandas.pydata.org) for manipulation of tabular data,
-* [scikit-learn](https://scikit-learn.org) for machine learning, and
+* [polars](https://docs.pola.rs/) for manipulation of tabular data,
+* [scikit-learn](https://scikit-learn.org) for classical machine learning,
+* [torch](https://pytorch.org) for deep learning, and
 * [seaborn](https://seaborn.pydata.org) for visualization.
 
 For more specialized tasks, we recommend using these or other DS libraries directly.

docs/README.md

@@ -12,8 +12,9 @@ common tasks on small to moderately sized datasets. As such, a major focus is to
 
 Instead of implementing DS methods from scratch, we use established DS libraries under the hood such as:
 
-* [pandas](https://pandas.pydata.org) for manipulation of tabular data,
-* [scikit-learn](https://scikit-learn.org) for machine learning, and
+* [polars](https://docs.pola.rs/) for manipulation of tabular data,
+* [scikit-learn](https://scikit-learn.org) for classical machine learning,
+* [torch](https://pytorch.org) for deep learning, and
 * [seaborn](https://seaborn.pydata.org) for visualization.
 
 For more specialized tasks, we recommend using these or other DS libraries directly.

docs/tutorials/classification.ipynb

@@ -22,7 +22,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "from safeds.data.tabular.containers import Table\n",
     "\n",
@@ -33,7 +32,8 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -47,7 +47,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "train_table, testing_table = titanic.split_rows(0.6)\n",
     "\n",
@@ -56,7 +55,8 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -72,7 +72,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "from safeds.data.tabular.transformation import OneHotEncoder\n",
     "\n",
@@ -81,7 +80,8 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -94,12 +94,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": "transformed_table = encoder.transform(train_table)",
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -110,7 +110,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "extra_names = [\"id\", \"name\", \"ticket\", \"cabin\", \"port_embarked\", \"age\", \"fare\"]\n",
     "\n",
@@ -119,7 +118,8 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -130,7 +130,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "from safeds.ml.classical.classification import RandomForestClassifier\n",
     "\n",
@@ -140,7 +139,8 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -154,7 +154,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "encoder = OneHotEncoder().fit(test_table, [\"sex\"])\n",
     "transformed_test_table = encoder.transform(test_table)\n",
@@ -163,12 +162,13 @@
     "    transformed_test_table\n",
     ")\n",
     "#For visualisation purposes we only print out the first 15 rows.\n",
-    "prediction.to_table().slice_rows(start=0, end=15)"
+    "prediction.to_table().slice_rows(start=0, length=15)"
    ],
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -181,7 +181,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "source": [
     "encoder = OneHotEncoder().fit(test_table, [\"sex\"])\n",
     "testing_table = encoder.transform(testing_table)\n",
@@ -192,7 +191,8 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {