diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3204261..d94289a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,7 +14,7 @@ repos:
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.4.8
+    rev: v0.6.3
     hooks:
       # Run the linter.
       - id: ruff
diff --git a/example/example_ntv_numpy.ipynb b/example/example_ntv_numpy.ipynb
index 46244c5..159cac5 100644
--- a/example/example_ntv_numpy.ipynb
+++ b/example/example_ntv_numpy.ipynb
@@ -142,7 +142,15 @@
    "source": [
     "from base64 import b64encode\n",
     "from IPython.display import Image, display\n",
-    "display(Image(url=\"https://mermaid.ink/img/\" + b64encode(open('struc.mmd', 'r', encoding=\"utf-8\").read().encode(\"ascii\")).decode(\"ascii\")))"
+    "\n",
+    "display(\n",
+    "    Image(\n",
+    "        url=\"https://mermaid.ink/img/\"\n",
+    "        + b64encode(\n",
+    "            open(\"struc.mmd\", \"r\", encoding=\"utf-8\").read().encode(\"ascii\")\n",
+    "        ).decode(\"ascii\")\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -235,25 +243,22 @@
    "outputs": [],
    "source": [
     "example = {\n",
-    "    'example:xdataset': {\n",
-    "        'var1:float[kg]': [['x', 'y'], [2, 2], [10.1, 0.4, 3.4, 8.2]],\n",
-    "        'var1.variance': [[2, 2], [0.1, 0.2, 0.3, 0.4]],\n",
-    "        'var1.mask1': [['x'], [True, False]],\n",
-    "        'var1.mask2': [[2, 2], [True, False, False, True]],\n",
-    "    \n",
-    "        'var2': [['x', 'y'], 'var2.ntv'],    \n",
-    "        \n",
-    "        'x:string': [{'test': 21}, ['23F0AE', '578B98']],\n",
-    "        'y:date': [['2021-01-01', '2022-02-02']],\n",
-    "        \n",
-    "        'ranking:month': [['var1'], [2, 2], [1, 2, 3, 4]],\n",
-    "        'z:float': [['x'], [10, 20]],\n",
-    "        'z.uncertainty': [[0.1, 0.2]],\n",
-    "        \n",
-    "        'z_bis': [['z1_bis', 'z2_bis']],\n",
-    "    \n",
-    "        'info': {'path': 'https://github.com/loco-philippe/ntv-numpy/tree/main/example/'},\n",
-    "        'location:string': [['paris']]\n",
+    "    \"example:xdataset\": {\n",
+    "        \"var1:float[kg]\": [[\"x\", \"y\"], [2, 2], [10.1, 0.4, 3.4, 8.2]],\n",
+    "        \"var1.variance\": [[2, 2], [0.1, 0.2, 0.3, 0.4]],\n",
+    "        \"var1.mask1\": [[\"x\"], [True, False]],\n",
+    "        \"var1.mask2\": [[2, 2], [True, False, False, True]],\n",
+    "        \"var2\": [[\"x\", \"y\"], \"var2.ntv\"],\n",
+    "        \"x:string\": [{\"test\": 21}, [\"23F0AE\", \"578B98\"]],\n",
+    "        \"y:date\": [[\"2021-01-01\", \"2022-02-02\"]],\n",
+    "        \"ranking:month\": [[\"var1\"], [2, 2], [1, 2, 3, 4]],\n",
+    "        \"z:float\": [[\"x\"], [10, 20]],\n",
+    "        \"z.uncertainty\": [[0.1, 0.2]],\n",
+    "        \"z_bis\": [[\"z1_bis\", \"z2_bis\"]],\n",
+    "        \"info\": {\n",
+    "            \"path\": \"https://github.com/loco-philippe/ntv-numpy/tree/main/example/\"\n",
+    "        },\n",
+    "        \"location:string\": [[\"paris\"]],\n",
     "    }\n",
     "}"
    ]
@@ -319,10 +324,9 @@
    ],
    "source": [
     "from ntv_numpy import Xdataset\n",
-    "import ntv_pandas as npd\n",
     "\n",
     "x_example = Xdataset.read_json(example)\n",
-    "x_example.info['structure']"
+    "x_example.info[\"structure\"]"
    ]
   },
   {
@@ -387,7 +391,14 @@
     }
    ],
    "source": [
-    "display(Image(url=\"https://mermaid.ink/img/\" + b64encode(open('interop.mmd', 'r', encoding=\"utf-8\").read().encode(\"ascii\")).decode(\"ascii\")))"
+    "display(\n",
+    "    Image(\n",
+    "        url=\"https://mermaid.ink/img/\"\n",
+    "        + b64encode(\n",
+    "            open(\"interop.mmd\", \"r\", encoding=\"utf-8\").read().encode(\"ascii\")\n",
+    "        ).decode(\"ascii\")\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -1305,7 +1316,9 @@
     }
    ],
    "source": [
-    "x_dataframe_empty_bis = Xdataset.from_dataframe(x_dataframe_empty).to_dataframe(json_name=False, info=False)[list(x_dataframe_empty.columns)]\n",
+    "x_dataframe_empty_bis = Xdataset.from_dataframe(x_dataframe_empty).to_dataframe(\n",
+    "    json_name=False, info=False\n",
+    ")[list(x_dataframe_empty.columns)]\n",
     "x_dataframe_empty_bis.equals(x_dataframe_empty)"
    ]
   },
@@ -1787,7 +1800,7 @@
    ],
    "source": [
     "x_scipp = x_example.to_scipp()\n",
-    "print(x_scipp['example'])"
+    "print(x_scipp[\"example\"])"
    ]
   },
   {
@@ -1927,19 +1940,38 @@
    ],
    "source": [
     "example = {\n",
-    "    'example:xdataset': {\n",
-    "        'data:float64[erg / s]': [[1,2,3,4]],\n",
-    "        'data.mask': [[False, False, True, True]],\n",
-    "        'data.uncertainty:float64[std]': [[1.0, 1.414, 1.732, 2.0]],\n",
-    "        'meta': {'object': 'fictional data.'},\n",
-    "        'wcs':  {'WCSAXES': 2, 'CRPIX1': 2048.0, 'CRPIX2': 1024.0, 'PC1_1': 1.2905625619716e-05,\n",
-    "                 'PC1_2': 5.9530912331034e-06, 'PC2_1': 5.0220581265601e-06, 'PC2_2': -1.2644774105568e-05,\n",
-    "                 'CDELT1': 1.0, 'CDELT2': 1.0, 'CUNIT1': 'deg', 'CUNIT2': 'deg', 'CTYPE1': 'RA---TAN',\n",
-    "                 'CTYPE2': 'DEC--TAN', 'CRVAL1': 5.63056810618, 'CRVAL2': -72.05457184279, 'LONPOLE': 180.0,\n",
-    "                 'LATPOLE': -72.05457184279, 'WCSNAME': 'IDC_qbu1641sj', 'MJDREF': 0.0, 'RADESYS': 'ICRS'},\n",
-    "        'psf:float64[erg / s]': [[1,2,3,4]]}} \n",
+    "    \"example:xdataset\": {\n",
+    "        \"data:float64[erg / s]\": [[1, 2, 3, 4]],\n",
+    "        \"data.mask\": [[False, False, True, True]],\n",
+    "        \"data.uncertainty:float64[std]\": [[1.0, 1.414, 1.732, 2.0]],\n",
+    "        \"meta\": {\"object\": \"fictional data.\"},\n",
+    "        \"wcs\": {\n",
+    "            \"WCSAXES\": 2,\n",
+    "            \"CRPIX1\": 2048.0,\n",
+    "            \"CRPIX2\": 1024.0,\n",
+    "            \"PC1_1\": 1.2905625619716e-05,\n",
+    "            \"PC1_2\": 5.9530912331034e-06,\n",
+    "            \"PC2_1\": 5.0220581265601e-06,\n",
+    "            \"PC2_2\": -1.2644774105568e-05,\n",
+    "            \"CDELT1\": 1.0,\n",
+    "            \"CDELT2\": 1.0,\n",
+    "            \"CUNIT1\": \"deg\",\n",
+    "            \"CUNIT2\": \"deg\",\n",
+    "            \"CTYPE1\": \"RA---TAN\",\n",
+    "            \"CTYPE2\": \"DEC--TAN\",\n",
+    "            \"CRVAL1\": 5.63056810618,\n",
+    "            \"CRVAL2\": -72.05457184279,\n",
+    "            \"LONPOLE\": 180.0,\n",
+    "            \"LATPOLE\": -72.05457184279,\n",
+    "            \"WCSNAME\": \"IDC_qbu1641sj\",\n",
+    "            \"MJDREF\": 0.0,\n",
+    "            \"RADESYS\": \"ICRS\",\n",
+    "        },\n",
+    "        \"psf:float64[erg / s]\": [[1, 2, 3, 4]],\n",
+    "    }\n",
+    "}\n",
     "n_example = Xdataset.read_json(example)\n",
-    "n_example.info['structure']"
+    "n_example.info[\"structure\"]"
    ]
   },
   {
@@ -2089,32 +2121,29 @@
     }
    ],
    "source": [
-    "#only structural data\n",
+    "# only structural data\n",
     "example = {\n",
-    "    'example:xdataset': {\n",
-    "        'var1:float[kg]': [['x', 'y'], [2, 2], 'var1.ntv'],\n",
-    "        'var1.variance': [[2, 2], 'var1_variance.ntv'],\n",
-    "        'var1.mask1': [['x'],'var1_mask1.ntv'], \n",
-    "        'var1.mask2': [[2, 2], 'var1_mask2.ntv'],\n",
-    "    \n",
-    "        'var2': [['x', 'y'],'var2.ntv'],    \n",
-    "        \n",
-    "        'x': [{'test': 21},'x.ntv'], \n",
-    "        'y:date': ['y.ntv'],\n",
-    "        \n",
-    "        'ranking:month': [['var1'], [2, 2], 'ranking.ntv'],\n",
-    "        'z:float': [['x'], 'z.ntv'], \n",
-    "        'z.uncertainty': ['z_uncertainty.ntv'],\n",
-    "        \n",
-    "        'z_bis': ['z_bis.ntv'],\n",
-    "    \n",
-    "        'info': {'path': 'https://github.com/loco-philippe/ntv-numpy/tree/main/example/'},\n",
-    "        'location:string': [['paris']]\n",
+    "    \"example:xdataset\": {\n",
+    "        \"var1:float[kg]\": [[\"x\", \"y\"], [2, 2], \"var1.ntv\"],\n",
+    "        \"var1.variance\": [[2, 2], \"var1_variance.ntv\"],\n",
+    "        \"var1.mask1\": [[\"x\"], \"var1_mask1.ntv\"],\n",
+    "        \"var1.mask2\": [[2, 2], \"var1_mask2.ntv\"],\n",
+    "        \"var2\": [[\"x\", \"y\"], \"var2.ntv\"],\n",
+    "        \"x\": [{\"test\": 21}, \"x.ntv\"],\n",
+    "        \"y:date\": [\"y.ntv\"],\n",
+    "        \"ranking:month\": [[\"var1\"], [2, 2], \"ranking.ntv\"],\n",
+    "        \"z:float\": [[\"x\"], \"z.ntv\"],\n",
+    "        \"z.uncertainty\": [\"z_uncertainty.ntv\"],\n",
+    "        \"z_bis\": [\"z_bis.ntv\"],\n",
+    "        \"info\": {\n",
+    "            \"path\": \"https://github.com/loco-philippe/ntv-numpy/tree/main/example/\"\n",
+    "        },\n",
+    "        \"location:string\": [[\"paris\"]],\n",
     "    }\n",
     "}\n",
     "\n",
     "x_example_mixte = Xdataset.read_json(example)\n",
-    "x_example_mixte.info['structure']"
+    "x_example_mixte.info[\"structure\"]"
    ]
   },
   {
@@ -2147,21 +2176,33 @@
     "from copy import copy\n",
     "\n",
     "# simulation of reading \".ntv\" json files at the indicated \"path\"\n",
-    "var1          = [[10.1, 0.4, 3.4, 8.2]]\n",
-    "var1_variance = ['float', [0.1, 0.2, 0.3, 0.4]]\n",
-    "var1_mask1    = [[True, False]]\n",
-    "var1_mask2    = [[True, False, False, True]]\n",
-    "var2          = ['var2.ntv']\n",
-    "x             = ['string', ['23F0AE', '578B98']]\n",
-    "y             = ['date', ['2021-01-01', '2022-02-02']]\n",
-    "ranking       = [[1, 2, 3, 4]]\n",
-    "z             = [[10.0, 20.0]]\n",
+    "var1 = [[10.1, 0.4, 3.4, 8.2]]\n",
+    "var1_variance = [\"float\", [0.1, 0.2, 0.3, 0.4]]\n",
+    "var1_mask1 = [[True, False]]\n",
+    "var1_mask2 = [[True, False, False, True]]\n",
+    "var2 = [\"var2.ntv\"]\n",
+    "x = [\"string\", [\"23F0AE\", \"578B98\"]]\n",
+    "y = [\"date\", [\"2021-01-01\", \"2022-02-02\"]]\n",
+    "ranking = [[1, 2, 3, 4]]\n",
+    "z = [[10.0, 20.0]]\n",
     "z_uncertainty = [[0.1, 0.2]]\n",
-    "z_bis         = [['z1_bis', 'z2_bis']]\n",
-    "\n",
-    "json_files = [var1, var1_variance, var1_mask1, var1_mask2, var2, x, y, ranking, z, z_uncertainty, z_bis]\n",
+    "z_bis = [[\"z1_bis\", \"z2_bis\"]]\n",
+    "\n",
+    "json_files = [\n",
+    "    var1,\n",
+    "    var1_variance,\n",
+    "    var1_mask1,\n",
+    "    var1_mask2,\n",
+    "    var2,\n",
+    "    x,\n",
+    "    y,\n",
+    "    ranking,\n",
+    "    z,\n",
+    "    z_uncertainty,\n",
+    "    z_bis,\n",
+    "]\n",
     "\n",
-    "x_example_mixte_json =copy(x_example_mixte)\n",
+    "x_example_mixte_json = copy(x_example_mixte)\n",
     "\n",
     "for data, xnda in zip(json_files, x_example_mixte_json.xnd):\n",
     "    xnda.set_ndarray(Ndarray.read_json(data))\n",
@@ -2190,19 +2231,31 @@
     "import numpy as np\n",
     "\n",
     "# simulation of reading files at the indicated \"path\"\n",
-    "var1          = np.array([10.1, 0.4, 3.4, 8.2])\n",
-    "var1_variance = Ndarray([0.1, 0.2, 0.3, 0.4], ntv_type='float')\n",
-    "var1_mask1    = np.array([True, False])\n",
-    "var1_mask2    = np.array([True, False, False, True])\n",
-    "var2          = Ndarray('var2.ntv')\n",
-    "x             = np.array(['23F0AE', '578B98'])\n",
-    "y             = np.array(['2021-01-01', '2022-02-02'], dtype='datetime64[D]')\n",
-    "ranking       = np.array([1, 2, 3, 4])\n",
-    "z             = np.array([10.0, 20.0])\n",
+    "var1 = np.array([10.1, 0.4, 3.4, 8.2])\n",
+    "var1_variance = Ndarray([0.1, 0.2, 0.3, 0.4], ntv_type=\"float\")\n",
+    "var1_mask1 = np.array([True, False])\n",
+    "var1_mask2 = np.array([True, False, False, True])\n",
+    "var2 = Ndarray(\"var2.ntv\")\n",
+    "x = np.array([\"23F0AE\", \"578B98\"])\n",
+    "y = np.array([\"2021-01-01\", \"2022-02-02\"], dtype=\"datetime64[D]\")\n",
+    "ranking = np.array([1, 2, 3, 4])\n",
+    "z = np.array([10.0, 20.0])\n",
     "z_uncertainty = np.array([0.1, 0.2])\n",
-    "z_bis         = np.array(['z1_bis', 'z2_bis'])\n",
-    "\n",
-    "array_data = [var1, var1_variance, var1_mask1, var1_mask2, var2, x, y, ranking, z, z_uncertainty, z_bis]\n",
+    "z_bis = np.array([\"z1_bis\", \"z2_bis\"])\n",
+    "\n",
+    "array_data = [\n",
+    "    var1,\n",
+    "    var1_variance,\n",
+    "    var1_mask1,\n",
+    "    var1_mask2,\n",
+    "    var2,\n",
+    "    x,\n",
+    "    y,\n",
+    "    ranking,\n",
+    "    z,\n",
+    "    z_uncertainty,\n",
+    "    z_bis,\n",
+    "]\n",
     "\n",
     "x_example_mixte_numpy = copy(x_example_mixte)\n",
     "for data, xnda in zip(array_data, x_example_mixte_numpy.xnd):\n",
@@ -2561,17 +2614,18 @@
     "from ntv_numpy import Ndarray\n",
     "\n",
     "examples = [\n",
-    "    np.array([10, 10, 20, 10, 30, 50]).astype('int64').reshape((2, 3)),\n",
-    "    np.array(['test1', 'test2'], dtype='str'),\n",
-    "    np.array(['2022-01-01', '2023-01-01'], dtype='datetime64[D]'),\n",
-    "    np.array(['2022', '2023'], dtype='datetime64[Y]'),\n",
-    "    np.array([b'abc\\x09', b'abc'], dtype='bytes'),\n",
-    "    np.array([True, False], dtype='bool')]\n",
+    "    np.array([10, 10, 20, 10, 30, 50]).astype(\"int64\").reshape((2, 3)),\n",
+    "    np.array([\"test1\", \"test2\"], dtype=\"str\"),\n",
+    "    np.array([\"2022-01-01\", \"2023-01-01\"], dtype=\"datetime64[D]\"),\n",
+    "    np.array([\"2022\", \"2023\"], dtype=\"datetime64[Y]\"),\n",
+    "    np.array([b\"abc\\x09\", b\"abc\"], dtype=\"bytes\"),\n",
+    "    np.array([True, False], dtype=\"bool\"),\n",
+    "]\n",
     "\n",
     "for example in examples:\n",
     "    equal = np.array_equal(example, Ndarray(example).ndarray)\n",
     "    jsn = Ndarray(example).to_json(header=False)\n",
-    "    print('reversibility : ', equal, ', JSON representation : ', jsn)"
+    "    print(\"reversibility : \", equal, \", JSON representation : \", jsn)"
    ]
   },
   {
@@ -2622,23 +2676,29 @@
     "\n",
     "examples = [\n",
     "    np.array([time(10, 2, 3), time(20, 2, 3)]),\n",
-    "    np.array([Decimal('10.5'), Decimal('20.5')]),\n",
-    "    np.array([Point([1,2]), Point([3,4])]),\n",
+    "    np.array([Decimal(\"10.5\"), Decimal(\"20.5\")]),\n",
+    "    np.array([Point([1, 2]), Point([3, 4])]),\n",
     "    np.array([None, None]),\n",
-    "    np.array([{'one':1}, {'two':2}]),\n",
-    "    np.fromiter([[1,2], [3,4]], dtype='object')]\n",
+    "    np.array([{\"one\": 1}, {\"two\": 2}]),\n",
+    "    np.fromiter([[1, 2], [3, 4]], dtype=\"object\"),\n",
+    "]\n",
     "\n",
     "for example in examples:\n",
     "    reverse = Ndarray(example).ndarray\n",
     "    equal = np.array_equal(example, reverse)\n",
     "    jsn_example = Ndarray(example).to_json(header=False)\n",
-    "    print('reversibility : ', equal, ', JSON representation : ', jsn_example)\n",
+    "    print(\"reversibility : \", equal, \", JSON representation : \", jsn_example)\n",
     "\n",
     "examples = [\n",
-    "    np.fromiter([np.array([1, 2], dtype='int64'), \n",
-    "                 np.array(['test1', 'test2'], dtype='str')], dtype='object'),\n",
-    "    np.fromiter([Ntv.obj({':point':[1,2]}), NtvSingle(12, 'noon', 'hour')], dtype='object'),\n",
-    "    np.fromiter([pd.Series([1,2,3]), pd.Series([4,5,6])], dtype='object')]\n",
+    "    np.fromiter(\n",
+    "        [np.array([1, 2], dtype=\"int64\"), np.array([\"test1\", \"test2\"], dtype=\"str\")],\n",
+    "        dtype=\"object\",\n",
+    "    ),\n",
+    "    np.fromiter(\n",
+    "        [Ntv.obj({\":point\": [1, 2]}), NtvSingle(12, \"noon\", \"hour\")], dtype=\"object\"\n",
+    "    ),\n",
+    "    np.fromiter([pd.Series([1, 2, 3]), pd.Series([4, 5, 6])], dtype=\"object\"),\n",
+    "]\n",
     "\n",
     "for example in examples:\n",
     "    reverse = Ndarray(example).ndarray\n",
@@ -2646,7 +2706,7 @@
     "    jsn_reverse = Ndarray(reverse).to_json(header=False)\n",
     "    equal = jsn_example == jsn_reverse\n",
     "\n",
-    "    print('reversibility : ', equal, ', JSON representation : ', jsn_example)"
+    "    print(\"reversibility : \", equal, \", JSON representation : \", jsn_example)"
    ]
   },
   {
@@ -2690,17 +2750,18 @@
     }
    ],
    "source": [
-    "examples_json  = [\n",
-    "    ['int64[kg]', [1, 2, 3, 4]],\n",
-    "    ['month', [2, 2], [1, 2, 3, 4]],\n",
-    "    ['binary', ['010011', '001101', '101']],\n",
-    "    ['json', [1, 'two', {'three': 3}]],\n",
-    "    ['email', ['John Doe <jdoe@mac.example>', 'Anna Doe <adoe@mac.example>']]]\n",
-    "            \n",
+    "examples_json = [\n",
+    "    [\"int64[kg]\", [1, 2, 3, 4]],\n",
+    "    [\"month\", [2, 2], [1, 2, 3, 4]],\n",
+    "    [\"binary\", [\"010011\", \"001101\", \"101\"]],\n",
+    "    [\"json\", [1, \"two\", {\"three\": 3}]],\n",
+    "    [\"email\", [\"John Doe <jdoe@mac.example>\", \"Anna Doe <adoe@mac.example>\"]],\n",
+    "]\n",
+    "\n",
     "for ex_json in examples_json:\n",
-    "    nda = Ndarray.read_json(ex_json)  \n",
+    "    nda = Ndarray.read_json(ex_json)\n",
     "    equal = nda == Ndarray.read_json(nda.to_json())\n",
-    "    print('reversibility : ', equal, ', Numpy entity : ', repr(nda.ndarray))"
+    "    print(\"reversibility : \", equal, \", Numpy entity : \", repr(nda.ndarray))"
    ]
   },
   {
@@ -2740,14 +2801,12 @@
    "source": [
     "from ntv_numpy import Xndarray\n",
     "\n",
-    "examples_json  = [\n",
-    "    {'unit': 'kg'},\n",
-    "    {'meta': {'everything': 1}}]\n",
+    "examples_json = [{\"unit\": \"kg\"}, {\"meta\": {\"everything\": 1}}]\n",
     "\n",
     "for ex_json in examples_json:\n",
-    "    xnda = Xndarray.read_json(ex_json)  \n",
+    "    xnda = Xndarray.read_json(ex_json)\n",
     "    equal = xnda == Xndarray.read_json(xnda.to_json())\n",
-    "    print('reversibility : ', equal, ', Xndarray entity : ', xnda.to_json(header=False))"
+    "    print(\"reversibility : \", equal, \", Xndarray entity : \", xnda.to_json(header=False))"
    ]
   },
   {
@@ -2781,21 +2840,28 @@
     }
    ],
    "source": [
-    "examples  = [Ndarray(np.array([1, 'two', {'three': 3}],  dtype='object'), ntv_type='json'),\n",
-    "             Ndarray(np.array([1, 2, 3, 4],              dtype='object'), ntv_type='int64[kg]'),\n",
-    "             Ndarray(np.array([1, 2, 3, 4],              dtype='object'), ntv_type='month'),\n",
-    "             Ndarray(np.array([[1, 2], [3, 4]],          dtype='object'), ntv_type='int'),\n",
-    "             Ndarray(np.array(['1F23', '236A5E'],        dtype='object'), ntv_type='base16'),\n",
-    "             Ndarray(np.array(['P3Y6M4DT12H30M5S'],      dtype='object'), ntv_type='duration'),\n",
-    "             Ndarray(np.array(['geo:13.4125,103.86673'], dtype='object'), ntv_type='uri'),\n",
-    "             Ndarray(np.array(['192.168.1.1'],           dtype='object'), ntv_type='ipv4'),\n",
-    "             Ndarray(np.array(['John Doe <jdoe@mac.example>', 'Ann Doe <adoe@mac.example>'], dtype='object'), ntv_type='email')\n",
-    "            ]\n",
+    "examples = [\n",
+    "    Ndarray(np.array([1, \"two\", {\"three\": 3}], dtype=\"object\"), ntv_type=\"json\"),\n",
+    "    Ndarray(np.array([1, 2, 3, 4], dtype=\"object\"), ntv_type=\"int64[kg]\"),\n",
+    "    Ndarray(np.array([1, 2, 3, 4], dtype=\"object\"), ntv_type=\"month\"),\n",
+    "    Ndarray(np.array([[1, 2], [3, 4]], dtype=\"object\"), ntv_type=\"int\"),\n",
+    "    Ndarray(np.array([\"1F23\", \"236A5E\"], dtype=\"object\"), ntv_type=\"base16\"),\n",
+    "    Ndarray(np.array([\"P3Y6M4DT12H30M5S\"], dtype=\"object\"), ntv_type=\"duration\"),\n",
+    "    Ndarray(np.array([\"geo:13.4125,103.86673\"], dtype=\"object\"), ntv_type=\"uri\"),\n",
+    "    Ndarray(np.array([\"192.168.1.1\"], dtype=\"object\"), ntv_type=\"ipv4\"),\n",
+    "    Ndarray(\n",
+    "        np.array(\n",
+    "            [\"John Doe <jdoe@mac.example>\", \"Ann Doe <adoe@mac.example>\"],\n",
+    "            dtype=\"object\",\n",
+    "        ),\n",
+    "        ntv_type=\"email\",\n",
+    "    ),\n",
+    "]\n",
     "\n",
     "for example in examples:\n",
-    "    xnda = Xndarray('example', example)\n",
+    "    xnda = Xndarray(\"example\", example)\n",
     "    equal = xnda == Xndarray.read_json(xnda.to_json())\n",
-    "    print('reversibility : ', equal, ', Xndarray entity : ', xnda.to_json(header=False))"
+    "    print(\"reversibility : \", equal, \", Xndarray entity : \", xnda.to_json(header=False))"
    ]
   },
   {
@@ -2823,10 +2889,10 @@
     }
    ],
    "source": [
-    "xnda = Xndarray('example', np.array(['x1', 'x2']))\n",
+    "xnda = Xndarray(\"example\", np.array([\"x1\", \"x2\"]))\n",
     "equal = xnda == Xndarray.read_json(xnda.to_json())\n",
     "\n",
-    "print('reversibility : ', equal, ', Xndarray entity : ', xnda.to_json(header=False))"
+    "print(\"reversibility : \", equal, \", Xndarray entity : \", xnda.to_json(header=False))"
    ]
   },
   {
@@ -2854,10 +2920,10 @@
     }
    ],
    "source": [
-    "xnda = Xndarray('x.mask', np.array([True, False]))\n",
+    "xnda = Xndarray(\"x.mask\", np.array([True, False]))\n",
     "equal = xnda == Xndarray.read_json(xnda.to_json())\n",
     "\n",
-    "print('reversibility : ', equal, ', Xndarray entity : ', xnda.to_json(header=False))"
+    "print(\"reversibility : \", equal, \", Xndarray entity : \", xnda.to_json(header=False))"
    ]
   },
   {
@@ -2885,10 +2951,14 @@
     }
    ],
    "source": [
-    "xnda = Xndarray('var2', Ndarray(np.array([10.1, 0.4, 3.4, 8.2]).reshape([2, 2]), ntv_type='float[kg]'), links = ['x', 'y'])\n",
+    "xnda = Xndarray(\n",
+    "    \"var2\",\n",
+    "    Ndarray(np.array([10.1, 0.4, 3.4, 8.2]).reshape([2, 2]), ntv_type=\"float[kg]\"),\n",
+    "    links=[\"x\", \"y\"],\n",
+    ")\n",
     "equal = xnda == Xndarray.read_json(xnda.to_json())\n",
     "\n",
-    "print('reversibility : ', equal, ', Xndarray entity : ', xnda.to_json(header=False))"
+    "print(\"reversibility : \", equal, \", Xndarray entity : \", xnda.to_json(header=False))"
    ]
   },
   {
@@ -2916,10 +2986,16 @@
     }
    ],
    "source": [
-    "xnda = Xndarray('var1', Ndarray('https://raw.githubusercontent.com/loco-philippe/ntv-numpy/master/example/ex_ndarray.ntv'), links = ['x', 'y'])\n",
+    "xnda = Xndarray(\n",
+    "    \"var1\",\n",
+    "    Ndarray(\n",
+    "        \"https://raw.githubusercontent.com/loco-philippe/ntv-numpy/master/example/ex_ndarray.ntv\"\n",
+    "    ),\n",
+    "    links=[\"x\", \"y\"],\n",
+    ")\n",
     "equal = xnda == Xndarray.read_json(xnda.to_json())\n",
     "\n",
-    "print('reversibility : ', equal, ', Xndarray entity : ', xnda.to_json(header=False))"
+    "print(\"reversibility : \", equal, \", Xndarray entity : \", xnda.to_json(header=False))"
    ]
   },
   {
@@ -2958,12 +3034,17 @@
    "source": [
     "from ntv_numpy import Xdataset\n",
     "\n",
-    "xn1 = Xndarray.read_json({'x:string': [['x1', 'x2']]})\n",
-    "xn2 = Xndarray.read_json({'z:string': [['x'], ['z1', 'z2']]})\n",
+    "xn1 = Xndarray.read_json({\"x:string\": [[\"x1\", \"x2\"]]})\n",
+    "xn2 = Xndarray.read_json({\"z:string\": [[\"x\"], [\"z1\", \"z2\"]]})\n",
     "\n",
     "xd = Xdataset([xn1, xn2])\n",
     "\n",
-    "print(\"reversibility : \", Xdataset.read_json(xd.to_json()) == xd, ', Xdataset entity : ',  xd.to_json(header=False))"
+    "print(\n",
+    "    \"reversibility : \",\n",
+    "    Xdataset.read_json(xd.to_json()) == xd,\n",
+    "    \", Xdataset entity : \",\n",
+    "    xd.to_json(header=False),\n",
+    ")"
    ]
   },
   {
@@ -3007,20 +3088,26 @@
    "source": [
     "from pprint import pprint\n",
     "\n",
-    "example = {'test': {\n",
-    "                'var1': [['x', 'y'], 'https://github.com/loco-philippe/ntv-numpy/tree/main/example/ex_ndarray.ntv'], \n",
-    "                'var2:float[kg]': [['x', 'y'], [2, 2], [10.1, 0.4, 3.4, 8.2]], \n",
-    "                'ranking': [['var2'], [2, 2], [1, 2, 3, 4]], \n",
-    "                'x': [{'test': 21}, ['x1', 'x2']],\n",
-    "                'y': [['y1', 'y2']],\n",
-    "                'z': [['x'], ['z1', 'z2']],\n",
-    "                'z_bis': [['z1_bis', 'z2_bis']],\n",
-    "                'x.mask': [['x'], [True, False]],\n",
-    "                'x.variance': [['x'], [0.1, 0.2]], \n",
-    "                'z.variance': [['x'], [0.1, 0.2]],\n",
-    "                'unit': 'kg',\n",
-    "                'info': {'example': 'everything'}}}\n",
-    "xd = Xdataset.read_json(example)        \n",
+    "example = {\n",
+    "    \"test\": {\n",
+    "        \"var1\": [\n",
+    "            [\"x\", \"y\"],\n",
+    "            \"https://github.com/loco-philippe/ntv-numpy/tree/main/example/ex_ndarray.ntv\",\n",
+    "        ],\n",
+    "        \"var2:float[kg]\": [[\"x\", \"y\"], [2, 2], [10.1, 0.4, 3.4, 8.2]],\n",
+    "        \"ranking\": [[\"var2\"], [2, 2], [1, 2, 3, 4]],\n",
+    "        \"x\": [{\"test\": 21}, [\"x1\", \"x2\"]],\n",
+    "        \"y\": [[\"y1\", \"y2\"]],\n",
+    "        \"z\": [[\"x\"], [\"z1\", \"z2\"]],\n",
+    "        \"z_bis\": [[\"z1_bis\", \"z2_bis\"]],\n",
+    "        \"x.mask\": [[\"x\"], [True, False]],\n",
+    "        \"x.variance\": [[\"x\"], [0.1, 0.2]],\n",
+    "        \"z.variance\": [[\"x\"], [0.1, 0.2]],\n",
+    "        \"unit\": \"kg\",\n",
+    "        \"info\": {\"example\": \"everything\"},\n",
+    "    }\n",
+    "}\n",
+    "xd = Xdataset.read_json(example)\n",
     "\n",
     "pprint(xd.to_json())\n",
     "print(\"\\nreversibility : \", Xdataset.read_json(xd.to_json()) == xd)"
@@ -3072,7 +3159,7 @@
     }
    ],
    "source": [
-    "xd.info['structure']"
+    "xd.info[\"structure\"]"
    ]
   },
   {
@@ -3103,8 +3190,8 @@
     }
    ],
    "source": [
-    "del(xd[('var1')])\n",
-    "xd.info['structure']"
+    "del xd[(\"var1\")]\n",
+    "xd.info[\"structure\"]"
    ]
   },
   {
@@ -3144,11 +3231,11 @@
     "from json_ntv import NtvList\n",
     "\n",
     "nd = np.array([1, 2, 3, 4])\n",
-    "xn1 = Xndarray('example', nda=np.array(['x1', 'x2']))\n",
-    "xn2 = Xndarray.read_json({'z:string': [['x'], ['z1', 'z2']]})\n",
-    "xd = Xdataset([xn1, xn2], 'test')\n",
+    "xn1 = Xndarray(\"example\", nda=np.array([\"x1\", \"x2\"]))\n",
+    "xn2 = Xndarray.read_json({\"z:string\": [[\"x\"], [\"z1\", \"z2\"]]})\n",
+    "xd = Xdataset([xn1, xn2], \"test\")\n",
     "\n",
-    "print('example NTVsingle\\n')\n",
+    "print(\"example NTVsingle\\n\")\n",
     "print(NtvSingle(nd))"
    ]
   },
@@ -3169,7 +3256,7 @@
     }
    ],
    "source": [
-    "print('example NTVlist\\n')\n",
+    "print(\"example NTVlist\\n\")\n",
     "print(NtvList([nd, xn1, xd]))"
    ]
   },
@@ -3190,8 +3277,16 @@
     }
    ],
    "source": [
-    "print('example with mixed data (Json representation)\\n')\n",
-    "mixte = Ntv.obj({'mixed': [xn1, {'coordinate':Point(1,2)}, {'pandas series': pd.Series([1,2,3])}]})\n",
+    "print(\"example with mixed data (Json representation)\\n\")\n",
+    "mixte = Ntv.obj(\n",
+    "    {\n",
+    "        \"mixed\": [\n",
+    "            xn1,\n",
+    "            {\"coordinate\": Point(1, 2)},\n",
+    "            {\"pandas series\": pd.Series([1, 2, 3])},\n",
+    "        ]\n",
+    "    }\n",
+    ")\n",
     "print(mixte)"
    ]
   },
@@ -3217,8 +3312,8 @@
     }
    ],
    "source": [
-    "print('example with mixed data (object representation)\\n')\n",
-    "pprint(mixte.to_obj(format='obj'), width=20)"
+    "print(\"example with mixed data (object representation)\\n\")\n",
+    "pprint(mixte.to_obj(format=\"obj\"), width=20)"
    ]
   },
   {
@@ -3245,7 +3340,7 @@
    ],
    "source": [
     "sr = pd.Series([1, 2, nd, xn1])\n",
-    "mixin = Ntv.obj({'mixin': sr})\n",
+    "mixin = Ntv.obj({\"mixin\": sr})\n",
     "print(mixin)"
    ]
   },
@@ -3299,8 +3394,15 @@
     }
    ],
    "source": [
-    "df= pd.DataFrame({'a': [2,2,3,4,2], 'b': [1,1,0,0,1], 'c': [1,1,1,0,1], 'd': [1,1,1,1,1]})\n",
-    "xnd =Xdataset.from_dataframe(df)\n",
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"a\": [2, 2, 3, 4, 2],\n",
+    "        \"b\": [1, 1, 0, 0, 1],\n",
+    "        \"c\": [1, 1, 1, 0, 1],\n",
+    "        \"d\": [1, 1, 1, 1, 1],\n",
+    "    }\n",
+    ")\n",
+    "xnd = Xdataset.from_dataframe(df)\n",
     "print(xnd.to_json())"
    ]
   },
@@ -3369,19 +3471,19 @@
    ],
    "source": [
     "example_tab = {\n",
-    "    'tabular:tab': {\n",
-    "        'first_name::string':   ['john', 'judith', 'jim'],\n",
-    "        'equinox::date':  ['2023-09-23', '2023-03-20', '2024-09-22']\n",
+    "    \"tabular:tab\": {\n",
+    "        \"first_name::string\": [\"john\", \"judith\", \"jim\"],\n",
+    "        \"equinox::date\": [\"2023-09-23\", \"2023-03-20\", \"2024-09-22\"],\n",
     "    }\n",
     "}\n",
     "\n",
     "example_multi = {\n",
-    "    'multi:xdataset': {\n",
-    "        'first_name:string':   [['john', 'judith', 'jim']],\n",
-    "        'equinox:date':  [['2023-09-23', '2023-03-20', '2024-09-22']]\n",
+    "    \"multi:xdataset\": {\n",
+    "        \"first_name:string\": [[\"john\", \"judith\", \"jim\"]],\n",
+    "        \"equinox:date\": [[\"2023-09-23\", \"2023-03-20\", \"2024-09-22\"]],\n",
     "    }\n",
-    "}    \n",
-    "Xdataset.read_json(example_multi).to_dataframe()    "
+    "}\n",
+    "Xdataset.read_json(example_multi).to_dataframe()"
    ]
   },
   {
@@ -3430,7 +3532,7 @@
     }
    ],
    "source": [
-    "NtvSingle('kg', 'mass', 'unit')"
+    "NtvSingle(\"kg\", \"mass\", \"unit\")"
    ]
   },
   {
@@ -3460,8 +3562,10 @@
    ],
    "source": [
     "examples = [\n",
-    "    Ndarray([2., 2.5, 3., 3.5, 4., 4.5, 5.], ntv_type='float[m/s]'),\n",
-    "    Xndarray('example', Ndarray([2., 2.5, 3., 3.5, 4., 4.5, 5.], ntv_type='float[m/s]'))\n",
+    "    Ndarray([2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], ntv_type=\"float[m/s]\"),\n",
+    "    Xndarray(\n",
+    "        \"example\", Ndarray([2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], ntv_type=\"float[m/s]\")\n",
+    "    ),\n",
     "]\n",
     "for example in examples:\n",
     "    print(example.to_json(header=False))\n",
@@ -3490,11 +3594,18 @@
     }
    ],
    "source": [
-    "ntv = Ntv.obj({\"list_of_ndarray::ndarray[kg]\": { \"array1\": [[2, 2], [1, 2, 3, 4]], \"array2\": [[2, 2], [5, 6, 7, 8]]}})\n",
+    "ntv = Ntv.obj(\n",
+    "    {\n",
+    "        \"list_of_ndarray::ndarray[kg]\": {\n",
+    "            \"array1\": [[2, 2], [1, 2, 3, 4]],\n",
+    "            \"array2\": [[2, 2], [5, 6, 7, 8]],\n",
+    "        }\n",
+    "    }\n",
+    ")\n",
     "\n",
-    "print('json representation :\\n', ntv[0])\n",
-    "print('\\njson representation of a list :\\n', ntv)\n",
-    "print('\\nNdarray representation :\\n', ntv[0].to_obj(format='obj', type=True))"
+    "print(\"json representation :\\n\", ntv[0])\n",
+    "print(\"\\njson representation of a list :\\n\", ntv)\n",
+    "print(\"\\nNdarray representation :\\n\", ntv[0].to_obj(format=\"obj\", type=True))"
    ]
   },
   {