Enable nbqa pre-commit hooks for isort and black. (#12848)

This enables `black` and `isort` linters for ipynb notebooks via [nbqa](https://github.com/nbQA-dev/nbQA). I propose this change to avoid manually linting notebooks like #12595. cc: @galipremsagar

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #12848

.pre-commit-config.yaml

@@ -61,6 +61,16 @@ repos:
                 # https://github.com/PyCQA/pydocstyle/issues/603
                 additional_dependencies: [toml]
                 args: ["--config=pyproject.toml"]
+      - repo: https://github.com/nbQA-dev/nbQA
+        rev: 1.6.3
+        hooks:
+              - id: nbqa-isort
+                # Use the cudf_kafka isort orderings in notebooks so that dask
+                # and RAPIDS packages have their own sections.
+                args: ["--settings-file=python/cudf_kafka/pyproject.toml"]
+              - id: nbqa-black
+                # Explicitly specify the pyproject.toml at the repo root, not per-project.
+                args: ["--config=pyproject.toml"]
       - repo: https://github.com/pre-commit/mirrors-clang-format
         rev: v11.1.0
         hooks:

docs/cudf/source/user_guide/10min.ipynb

@@ -35,6 +35,7 @@
     "\n",
     "import cupy as cp\n",
     "import pandas as pd\n",
+    "\n",
     "import cudf\n",
     "import dask_cudf\n",
     "\n",

docs/cudf/source/user_guide/cupy-interop.ipynb

@@ -18,9 +18,10 @@
    "outputs": [],
    "source": [
     "import timeit\n",
-    "from packaging import version\n",
     "\n",
     "import cupy as cp\n",
+    "from packaging import version\n",
+    "\n",
     "import cudf\n",
     "\n",
     "if version.parse(cp.__version__) >= version.parse(\"10.0.0\"):\n",
@@ -63,10 +64,13 @@
    ],
    "source": [
     "nelem = 10000\n",
-    "df = cudf.DataFrame({'a':range(nelem),\n",
-    "                     'b':range(500, nelem + 500),\n",
-    "                     'c':range(1000, nelem + 1000)}\n",
-    "                   )\n",
+    "df = cudf.DataFrame(\n",
+    "    {\n",
+    "        \"a\": range(nelem),\n",
+    "        \"b\": range(500, nelem + 500),\n",
+    "        \"c\": range(1000, nelem + 1000),\n",
+    "    }\n",
+    ")\n",
     "\n",
     "%timeit arr_cupy = cupy_from_dlpack(df.to_dlpack())\n",
     "%timeit arr_cupy = df.values\n",
@@ -138,7 +142,7 @@
     }
    ],
    "source": [
-    "col = 'a'\n",
+    "col = \"a\"\n",
     "\n",
     "%timeit cola_cupy = cp.asarray(df[col])\n",
     "%timeit cola_cupy = cupy_from_dlpack(df[col].to_dlpack())\n",
@@ -1088,14 +1092,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def cudf_to_cupy_sparse_matrix(data, sparseformat='column'):\n",
-    "    \"\"\"Converts a cuDF object to a CuPy Sparse Column matrix.\n",
-    "    \"\"\"\n",
-    "    if sparseformat not in ('row', 'column',):\n",
+    "def cudf_to_cupy_sparse_matrix(data, sparseformat=\"column\"):\n",
+    "    \"\"\"Converts a cuDF object to a CuPy Sparse Column matrix.\"\"\"\n",
+    "    if sparseformat not in (\n",
+    "        \"row\",\n",
+    "        \"column\",\n",
+    "    ):\n",
     "        raise ValueError(\"Let's focus on column and row formats for now.\")\n",
-    "    \n",
+    "\n",
     "    _sparse_constructor = cp.sparse.csc_matrix\n",
-    "    if sparseformat == 'row':\n",
+    "    if sparseformat == \"row\":\n",
     "        _sparse_constructor = cp.sparse.csr_matrix\n",
     "\n",
     "    return _sparse_constructor(cupy_from_dlpack(data.to_dlpack()))"
@@ -1121,8 +1127,8 @@
     "nonzero = 1000\n",
     "for i in range(20):\n",
     "    arr = cp.random.normal(5, 5, nelem)\n",
-    "    arr[cp.random.choice(arr.shape[0], nelem-nonzero, replace=False)] = 0\n",
-    "    df['a' + str(i)] = arr"
+    "    arr[cp.random.choice(arr.shape[0], nelem - nonzero, replace=False)] = 0\n",
+    "    df[\"a\" + str(i)] = arr"
    ]
   },
   {