Run black on all the notebooks

- we use black's default settings
- some cells are NOT kept in black's format to:
  - increase readability
  - or show Python's flexibility with regard to style

00_python_in_a_nutshell/00_content_arithmetic.ipynb

@@ -123,7 +123,7 @@
     }
    ],
    "source": [
-    "2 ** 3"
+    "2**3"
    ]
   },
   {
@@ -143,7 +143,7 @@
     }
    ],
    "source": [
-    "2 * 2 ** 3"
+    "2 * 2**3"
    ]
   },
   {

00_python_in_a_nutshell/05_content_functions.ipynb

@@ -385,7 +385,7 @@
    ],
    "source": [
     "for number in numbers:\n",
-    "    square = number ** 2\n",
+    "    square = number**2\n",
     "    print(\"The square of\", number, \"is\", square)"
    ]
   },

01_scientific_stack/00_content_numpy.ipynb

@@ -282,10 +282,12 @@
     }
    ],
    "source": [
-    "m1 = np.array([\n",
-    "    [1, 2, 3, 4, 5],\n",
-    "    [6, 7, 8, 9, 10],\n",
-    "])\n",
+    "m1 = np.array(\n",
+    "    [\n",
+    "        [1, 2, 3, 4, 5],\n",
+    "        [6, 7, 8, 9, 10],\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "m1"
    ]

01_scientific_stack/02_content_pandas.ipynb

@@ -1852,10 +1852,7 @@
     }
    ],
    "source": [
-    "df.loc[\n",
-    "    200300:200800,\n",
-    "    [\"o_street\", \"o_zip\", \"o_city\", \"o_latitude\", \"o_longitude\"]\n",
-    "]"
+    "df.loc[200300:200800, [\"o_street\", \"o_zip\", \"o_city\", \"o_latitude\", \"o_longitude\"]]"
    ]
   },
   {
@@ -1982,11 +1979,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = df.astype({\n",
-    "    \"pickup_at\": \"datetime64[ns]\",\n",
-    "    \"delivery_at\": \"datetime64[ns]\",\n",
-    "    \"cancelled\": bool,\n",
-    "})"
+    "df = df.astype(\n",
+    "    {\n",
+    "        \"pickup_at\": \"datetime64[ns]\",\n",
+    "        \"delivery_at\": \"datetime64[ns]\",\n",
+    "        \"cancelled\": bool,\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -2686,7 +2685,7 @@
    "source": [
     "df.loc[\n",
     "    max_a_table,\n",
-    "    [\"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"d_latitude\", \"d_longitude\"]\n",
+    "    [\"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"d_latitude\", \"d_longitude\"],\n",
     "].head()"
    ]
   },
@@ -2821,12 +2820,10 @@
     "        max_a_table\n",
     "        &\n",
     "        (\n",
-    "            (df[\"d_latitude\"] > 44.85)\n",
-    "            |\n",
-    "            (df[\"d_longitude\"] < -0.59)\n",
-    "        )        \n",
+    "            (df[\"d_latitude\"] > 44.85) | (df[\"d_longitude\"] < -0.59)\n",
+    "        )\n",
     "    ),\n",
-    "    [\"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"d_latitude\", \"d_longitude\"]\n",
+    "    [\"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"d_latitude\", \"d_longitude\"],\n",
     "].head()"
    ]
   },
@@ -2933,12 +2930,8 @@
    ],
    "source": [
     "df.loc[\n",
-    "    (\n",
-    "        max_a_table\n",
-    "        &\n",
-    "        df[\"customer_id\"].isin([6037, 79900, 80095])\n",
-    "    ),\n",
-    "    [\"placed_at\", \"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"total\"]\n",
+    "    (max_a_table & df[\"customer_id\"].isin([6037, 79900, 80095])),\n",
+    "    [\"placed_at\", \"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"total\"],\n",
     "].head()"
    ]
   },
@@ -3067,12 +3060,8 @@
    ],
    "source": [
     "df.loc[\n",
-    "    (\n",
-    "        max_a_table\n",
-    "        &\n",
-    "        ~df[\"customer_id\"].isin([6037, 79900, 80095])\n",
-    "    ),\n",
-    "    [\"placed_at\", \"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"total\"]\n",
+    "    (max_a_table & ~df[\"customer_id\"].isin([6037, 79900, 80095])),\n",
+    "    [\"placed_at\", \"customer_id\", \"d_street\", \"d_zip\", \"d_city\", \"total\"],\n",
     "].head()"
    ]
   },
@@ -3166,10 +3155,7 @@
     }
    ],
    "source": [
-    "df.loc[\n",
-    "    max_a_table,\n",
-    "    \"customer_id\"\n",
-    "].unique()"
+    "df.loc[max_a_table, \"customer_id\"].unique()"
    ]
   },
   {
@@ -3289,10 +3275,7 @@
     }
    ],
    "source": [
-    "df.loc[\n",
-    "    max_a_table,\n",
-    "    \"total\"\n",
-    "].sum() / 100"
+    "df.loc[max_a_table, \"total\"].sum() / 100"
    ]
   },
   {
@@ -3352,10 +3335,7 @@
     }
    ],
    "source": [
-    "df.loc[\n",
-    "    max_a_table,\n",
-    "    \"total\"\n",
-    "].min() / 100"
+    "df.loc[max_a_table, \"total\"].min() / 100"
    ]
   },
   {
@@ -3375,10 +3355,7 @@
     }
    ],
    "source": [
-    "df.loc[\n",
-    "    max_a_table,\n",
-    "    \"total\"\n",
-    "].max() / 100"
+    "df.loc[max_a_table, \"total\"].max() / 100"
    ]
   },
   {
@@ -3438,10 +3415,7 @@
     }
    ],
    "source": [
-    "df.loc[\n",
-    "    max_a_table,\n",
-    "    \"total\"\n",
-    "].mean().round() / 100"
+    "df.loc[max_a_table, \"total\"].mean().round() / 100"
    ]
   }
  ],

01_scientific_stack/03_exercises_simple_queries.ipynb

@@ -56,7 +56,7 @@
     "    \"orders.csv\",\n",
     "    index_col=\"order_id\",\n",
     "    dtype={\"cancelled\": bool},\n",
-    "    parse_dates=[\"placed_at\", \"pickup_at\", \"delivery_at\"]\n",
+    "    parse_dates=[\"placed_at\", \"pickup_at\", \"delivery_at\"],\n",
     ")"
    ]
   },

03_classification/00_content.ipynb

@@ -518,17 +518,17 @@
    ],
    "source": [
     "feature_index = 2\n",
-    "colors = ['blue', 'red', 'green']\n",
+    "colors = [\"blue\", \"red\", \"green\"]\n",
     "\n",
     "for label, color in zip(range(len(iris.target_names)), colors):\n",
     "    plt.hist(\n",
-    "        iris.data[iris.target==label, feature_index], \n",
+    "        iris.data[iris.target == label, feature_index],\n",
     "        label=iris.target_names[label],\n",
     "        color=color,\n",
     "    )\n",
     "\n",
     "plt.xlabel(iris.feature_names[feature_index])\n",
-    "plt.legend(loc='upper right')\n",
+    "plt.legend(loc=\"upper right\")\n",
     "plt.show()"
    ]
   },
@@ -559,19 +559,19 @@
     "first_feature_index = 1\n",
     "second_feature_index = 0\n",
     "\n",
-    "colors = ['blue', 'red', 'green']\n",
+    "colors = [\"blue\", \"red\", \"green\"]\n",
     "\n",
     "for label, color in zip(range(len(iris.target_names)), colors):\n",
     "    plt.scatter(\n",
-    "        iris.data[iris.target==label, first_feature_index], \n",
-    "        iris.data[iris.target==label, second_feature_index],\n",
+    "        iris.data[iris.target == label, first_feature_index],\n",
+    "        iris.data[iris.target == label, second_feature_index],\n",
     "        label=iris.target_names[label],\n",
     "        c=color,\n",
     "    )\n",
     "\n",
     "plt.xlabel(iris.feature_names[first_feature_index])\n",
     "plt.ylabel(iris.feature_names[second_feature_index])\n",
-    "plt.legend(loc='upper left')\n",
+    "plt.legend(loc=\"upper left\")\n",
     "plt.show()"
    ]
   },
@@ -772,7 +772,9 @@
     }
    ],
    "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=42, stratify=y)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, train_size=0.7, test_size=0.3, random_state=42, stratify=y\n",
+    ")\n",
     "\n",
     "y_test"
    ]
@@ -1040,12 +1042,12 @@
     "    X_test[incorrect_idx, first_feature_index],\n",
     "    X_test[incorrect_idx, second_feature_index],\n",
     "    color=\"darkred\",\n",
-    "    label='misclassified',\n",
+    "    label=\"misclassified\",\n",
     ")\n",
     "\n",
-    "plt.xlabel('sepal width [cm]')\n",
-    "plt.ylabel('petal length [cm]')\n",
-    "plt.legend(loc='best')\n",
+    "plt.xlabel(\"sepal width [cm]\")\n",
+    "plt.ylabel(\"petal length [cm]\")\n",
+    "plt.legend(loc=\"best\")\n",
     "plt.title(\"Iris Classification results\")\n",
     "plt.show()"
    ]