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

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",
     ")"
    ]
   },