diff --git a/doc/examples/ERA5-GRIB-example.ipynb b/doc/examples/ERA5-GRIB-example.ipynb
index caa702ebe53..5d09f1a7431 100644
--- a/doc/examples/ERA5-GRIB-example.ipynb
+++ b/doc/examples/ERA5-GRIB-example.ipynb
@@ -37,7 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = xr.tutorial.load_dataset('era5-2mt-2019-03-uk.grib', engine='cfgrib')"
+    "ds = xr.tutorial.load_dataset(\"era5-2mt-2019-03-uk.grib\", engine=\"cfgrib\")"
    ]
   },
   {
@@ -72,11 +72,14 @@
    "source": [
     "import cartopy.crs as ccrs\n",
     "import cartopy\n",
-    "fig = plt.figure(figsize=(10,10))\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
     "ax = plt.axes(projection=ccrs.Robinson())\n",
-    "ax.coastlines(resolution='10m')\n",
-    "plot = ds.t2m[0].plot(cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={'shrink':0.6})\n",
-    "plt.title('ERA5 - 2m temperature British Isles March 2019')"
+    "ax.coastlines(resolution=\"10m\")\n",
+    "plot = ds.t2m[0].plot(\n",
+    "    cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={\"shrink\": 0.6}\n",
+    ")\n",
+    "plt.title(\"ERA5 - 2m temperature British Isles March 2019\")"
    ]
   },
   {
@@ -92,8 +95,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds.t2m.sel(longitude=0,latitude=51.5).plot()\n",
-    "plt.title('ERA5 - London 2m temperature March 2019')"
+    "ds.t2m.sel(longitude=0, latitude=51.5).plot()\n",
+    "plt.title(\"ERA5 - London 2m temperature March 2019\")"
    ]
   }
  ],
diff --git a/doc/examples/ROMS_ocean_model.ipynb b/doc/examples/ROMS_ocean_model.ipynb
index b699c4d5ba9..82d7a8d58af 100644
--- a/doc/examples/ROMS_ocean_model.ipynb
+++ b/doc/examples/ROMS_ocean_model.ipynb
@@ -26,6 +26,7 @@
     "import cartopy.crs as ccrs\n",
     "import cartopy.feature as cfeature\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "%matplotlib inline\n",
     "\n",
     "import xarray as xr"
@@ -73,9 +74,9 @@
    "outputs": [],
    "source": [
     "# load in the file\n",
-    "ds = xr.tutorial.open_dataset('ROMS_example.nc', chunks={'ocean_time': 1})\n",
+    "ds = xr.tutorial.open_dataset(\"ROMS_example.nc\", chunks={\"ocean_time\": 1})\n",
     "\n",
-    "# This is a way to turn on chunking and lazy evaluation. Opening with mfdataset, or \n",
+    "# This is a way to turn on chunking and lazy evaluation. Opening with mfdataset, or\n",
     "# setting the chunking in the open_dataset would also achive this.\n",
     "ds"
    ]
@@ -105,12 +106,12 @@
    "source": [
     "if ds.Vtransform == 1:\n",
     "    Zo_rho = ds.hc * (ds.s_rho - ds.Cs_r) + ds.Cs_r * ds.h\n",
-    "    z_rho = Zo_rho + ds.zeta * (1 + Zo_rho/ds.h)\n",
+    "    z_rho = Zo_rho + ds.zeta * (1 + Zo_rho / ds.h)\n",
     "elif ds.Vtransform == 2:\n",
     "    Zo_rho = (ds.hc * ds.s_rho + ds.Cs_r * ds.h) / (ds.hc + ds.h)\n",
     "    z_rho = ds.zeta + (ds.zeta + ds.h) * Zo_rho\n",
     "\n",
-    "ds.coords['z_rho'] = z_rho.transpose()   # needing transpose seems to be an xarray bug\n",
+    "ds.coords[\"z_rho\"] = z_rho.transpose()  # needing transpose seems to be an xarray bug\n",
     "ds.salt"
    ]
   },
@@ -148,7 +149,7 @@
    "outputs": [],
    "source": [
     "section = ds.salt.isel(xi_rho=50, eta_rho=slice(0, 167), ocean_time=0)\n",
-    "section.plot(x='lon_rho', y='z_rho', figsize=(15, 6), clim=(25, 35))\n",
+    "section.plot(x=\"lon_rho\", y=\"z_rho\", figsize=(15, 6), clim=(25, 35))\n",
     "plt.ylim([-100, 1]);"
    ]
   },
@@ -167,7 +168,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds.salt.isel(s_rho=-1, ocean_time=0).plot(x='lon_rho', y='lat_rho')"
+    "ds.salt.isel(s_rho=-1, ocean_time=0).plot(x=\"lon_rho\", y=\"lat_rho\")"
    ]
   },
   {
@@ -186,11 +187,13 @@
     "proj = ccrs.LambertConformal(central_longitude=-92, central_latitude=29)\n",
     "fig = plt.figure(figsize=(15, 5))\n",
     "ax = plt.axes(projection=proj)\n",
-    "ds.salt.isel(s_rho=-1, ocean_time=0).plot(x='lon_rho', y='lat_rho', \n",
-    "                                          transform=ccrs.PlateCarree())\n",
+    "ds.salt.isel(s_rho=-1, ocean_time=0).plot(\n",
+    "    x=\"lon_rho\", y=\"lat_rho\", transform=ccrs.PlateCarree()\n",
+    ")\n",
     "\n",
-    "coast_10m = cfeature.NaturalEarthFeature('physical', 'land', '10m',\n",
-    "                                        edgecolor='k', facecolor='0.8')\n",
+    "coast_10m = cfeature.NaturalEarthFeature(\n",
+    "    \"physical\", \"land\", \"10m\", edgecolor=\"k\", facecolor=\"0.8\"\n",
+    ")\n",
     "ax.add_feature(coast_10m)"
    ]
   },
diff --git a/doc/examples/apply_ufunc_vectorize_1d.ipynb b/doc/examples/apply_ufunc_vectorize_1d.ipynb
index e9a48d70173..d1d6a52919c 100644
--- a/doc/examples/apply_ufunc_vectorize_1d.ipynb
+++ b/doc/examples/apply_ufunc_vectorize_1d.ipynb
@@ -674,7 +674,9 @@
     "        exclude_dims=set((dim,)),  # dimensions allowed to change size. Must be a set!\n",
     "        # vectorize=True,  # not needed since numba takes care of vectorizing\n",
     "        dask=\"parallelized\",\n",
-    "        output_dtypes=[data.dtype],  # one per output; could also be float or np.dtype(\"float64\")\n",
+    "        output_dtypes=[\n",
+    "            data.dtype\n",
+    "        ],  # one per output; could also be float or np.dtype(\"float64\")\n",
     "    ).rename({\"__newdim__\": dim})\n",
     "    interped[dim] = newdim  # need to add this manually\n",
     "\n",
diff --git a/doc/examples/monthly-means.ipynb b/doc/examples/monthly-means.ipynb
index 3490fc9a4fe..fd31e21a872 100644
--- a/doc/examples/monthly-means.ipynb
+++ b/doc/examples/monthly-means.ipynb
@@ -29,7 +29,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import xarray as xr\n",
-    "import matplotlib.pyplot as plt "
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -50,7 +50,7 @@
    },
    "outputs": [],
    "source": [
-    "ds = xr.tutorial.open_dataset('rasm').load()\n",
+    "ds = xr.tutorial.open_dataset(\"rasm\").load()\n",
     "ds"
    ]
   },
@@ -88,13 +88,15 @@
    "outputs": [],
    "source": [
     "# Calculate the weights by grouping by 'time.season'.\n",
-    "weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()\n",
+    "weights = (\n",
+    "    month_length.groupby(\"time.season\") / month_length.groupby(\"time.season\").sum()\n",
+    ")\n",
     "\n",
     "# Test that the sum of the weights for each season is 1.0\n",
-    "np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))\n",
+    "np.testing.assert_allclose(weights.groupby(\"time.season\").sum().values, np.ones(4))\n",
     "\n",
     "# Calculate the weighted average\n",
-    "ds_weighted = (ds * weights).groupby('time.season').sum(dim='time')"
+    "ds_weighted = (ds * weights).groupby(\"time.season\").sum(dim=\"time\")"
    ]
   },
   {
@@ -123,7 +125,7 @@
    "outputs": [],
    "source": [
     "# only used for comparisons\n",
-    "ds_unweighted = ds.groupby('time.season').mean('time')\n",
+    "ds_unweighted = ds.groupby(\"time.season\").mean(\"time\")\n",
     "ds_diff = ds_weighted - ds_unweighted"
    ]
   },
@@ -139,39 +141,54 @@
    "outputs": [],
    "source": [
     "# Quick plot to show the results\n",
-    "notnull = pd.notnull(ds_unweighted['Tair'][0])\n",
-    "\n",
-    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14,12))\n",
-    "for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')):\n",
-    "    ds_weighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n",
-    "        ax=axes[i, 0], vmin=-30, vmax=30, cmap='Spectral_r', \n",
-    "        add_colorbar=True, extend='both')\n",
-    "    \n",
-    "    ds_unweighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n",
-    "        ax=axes[i, 1], vmin=-30, vmax=30, cmap='Spectral_r', \n",
-    "        add_colorbar=True, extend='both')\n",
-    "\n",
-    "    ds_diff['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n",
-    "        ax=axes[i, 2], vmin=-0.1, vmax=.1, cmap='RdBu_r',\n",
-    "        add_colorbar=True, extend='both')\n",
+    "notnull = pd.notnull(ds_unweighted[\"Tair\"][0])\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14, 12))\n",
+    "for i, season in enumerate((\"DJF\", \"MAM\", \"JJA\", \"SON\")):\n",
+    "    ds_weighted[\"Tair\"].sel(season=season).where(notnull).plot.pcolormesh(\n",
+    "        ax=axes[i, 0],\n",
+    "        vmin=-30,\n",
+    "        vmax=30,\n",
+    "        cmap=\"Spectral_r\",\n",
+    "        add_colorbar=True,\n",
+    "        extend=\"both\",\n",
+    "    )\n",
+    "\n",
+    "    ds_unweighted[\"Tair\"].sel(season=season).where(notnull).plot.pcolormesh(\n",
+    "        ax=axes[i, 1],\n",
+    "        vmin=-30,\n",
+    "        vmax=30,\n",
+    "        cmap=\"Spectral_r\",\n",
+    "        add_colorbar=True,\n",
+    "        extend=\"both\",\n",
+    "    )\n",
+    "\n",
+    "    ds_diff[\"Tair\"].sel(season=season).where(notnull).plot.pcolormesh(\n",
+    "        ax=axes[i, 2],\n",
+    "        vmin=-0.1,\n",
+    "        vmax=0.1,\n",
+    "        cmap=\"RdBu_r\",\n",
+    "        add_colorbar=True,\n",
+    "        extend=\"both\",\n",
+    "    )\n",
     "\n",
     "    axes[i, 0].set_ylabel(season)\n",
-    "    axes[i, 1].set_ylabel('')\n",
-    "    axes[i, 2].set_ylabel('')\n",
+    "    axes[i, 1].set_ylabel(\"\")\n",
+    "    axes[i, 2].set_ylabel(\"\")\n",
     "\n",
     "for ax in axes.flat:\n",
     "    ax.axes.get_xaxis().set_ticklabels([])\n",
     "    ax.axes.get_yaxis().set_ticklabels([])\n",
-    "    ax.axes.axis('tight')\n",
-    "    ax.set_xlabel('')\n",
-    "    \n",
-    "axes[0, 0].set_title('Weighted by DPM')\n",
-    "axes[0, 1].set_title('Equal Weighting')\n",
-    "axes[0, 2].set_title('Difference')\n",
-    "        \n",
+    "    ax.axes.axis(\"tight\")\n",
+    "    ax.set_xlabel(\"\")\n",
+    "\n",
+    "axes[0, 0].set_title(\"Weighted by DPM\")\n",
+    "axes[0, 1].set_title(\"Equal Weighting\")\n",
+    "axes[0, 2].set_title(\"Difference\")\n",
+    "\n",
     "plt.tight_layout()\n",
     "\n",
-    "fig.suptitle('Seasonal Surface Air Temperature', fontsize=16, y=1.02)"
+    "fig.suptitle(\"Seasonal Surface Air Temperature\", fontsize=16, y=1.02)"
    ]
   },
   {
@@ -186,18 +203,20 @@
    "outputs": [],
    "source": [
     "# Wrap it into a simple function\n",
-    "def season_mean(ds, calendar='standard'):\n",
+    "def season_mean(ds, calendar=\"standard\"):\n",
     "    # Make a DataArray with the number of days in each month, size = len(time)\n",
     "    month_length = ds.time.dt.days_in_month\n",
     "\n",
     "    # Calculate the weights by grouping by 'time.season'\n",
-    "    weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()\n",
+    "    weights = (\n",
+    "        month_length.groupby(\"time.season\") / month_length.groupby(\"time.season\").sum()\n",
+    "    )\n",
     "\n",
     "    # Test that the sum of the weights for each season is 1.0\n",
-    "    np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))\n",
+    "    np.testing.assert_allclose(weights.groupby(\"time.season\").sum().values, np.ones(4))\n",
     "\n",
     "    # Calculate the weighted average\n",
-    "    return (ds * weights).groupby('time.season').sum(dim='time')"
+    "    return (ds * weights).groupby(\"time.season\").sum(dim=\"time\")"
    ]
   }
  ],
diff --git a/doc/examples/multidimensional-coords.ipynb b/doc/examples/multidimensional-coords.ipynb
index 3327192e324..f095d1137de 100644
--- a/doc/examples/multidimensional-coords.ipynb
+++ b/doc/examples/multidimensional-coords.ipynb
@@ -48,7 +48,7 @@
    },
    "outputs": [],
    "source": [
-    "ds = xr.tutorial.open_dataset('rasm').load()\n",
+    "ds = xr.tutorial.open_dataset(\"rasm\").load()\n",
     "ds"
    ]
   },
@@ -94,7 +94,7 @@
    },
    "outputs": [],
    "source": [
-    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4))\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 4))\n",
     "ds.xc.plot(ax=ax1)\n",
     "ds.yc.plot(ax=ax2)"
    ]
@@ -140,12 +140,14 @@
    },
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(14,6))\n",
+    "plt.figure(figsize=(14, 6))\n",
     "ax = plt.axes(projection=ccrs.PlateCarree())\n",
     "ax.set_global()\n",
-    "ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False)\n",
+    "ds.Tair[0].plot.pcolormesh(\n",
+    "    ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n",
+    ")\n",
     "ax.coastlines()\n",
-    "ax.set_ylim([0,90]);"
+    "ax.set_ylim([0, 90]);"
    ]
   },
   {
@@ -169,11 +171,13 @@
    "outputs": [],
    "source": [
     "# define two-degree wide latitude bins\n",
-    "lat_bins = np.arange(0,91,2)\n",
+    "lat_bins = np.arange(0, 91, 2)\n",
     "# define a label for each bin corresponding to the central latitude\n",
-    "lat_center = np.arange(1,90,2)\n",
+    "lat_center = np.arange(1, 90, 2)\n",
     "# group according to those bins and take the mean\n",
-    "Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean(dim=xr.ALL_DIMS)\n",
+    "Tair_lat_mean = ds.Tair.groupby_bins(\"xc\", lat_bins, labels=lat_center).mean(\n",
+    "    dim=xr.ALL_DIMS\n",
+    ")\n",
     "# plot the result\n",
     "Tair_lat_mean.plot()"
    ]
diff --git a/doc/examples/visualization_gallery.ipynb b/doc/examples/visualization_gallery.ipynb
index 831f162d998..e6fa564db0d 100644
--- a/doc/examples/visualization_gallery.ipynb
+++ b/doc/examples/visualization_gallery.ipynb
@@ -18,6 +18,7 @@
     "import cartopy.crs as ccrs\n",
     "import matplotlib.pyplot as plt\n",
     "import xarray as xr\n",
+    "\n",
     "%matplotlib inline"
    ]
   },
@@ -34,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = xr.tutorial.load_dataset('air_temperature')"
+    "ds = xr.tutorial.load_dataset(\"air_temperature\")"
    ]
   },
   {
@@ -62,10 +63,13 @@
     "# This is the map projection we want to plot *onto*\n",
     "map_proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=45)\n",
     "\n",
-    "p = air.plot(transform=ccrs.PlateCarree(),  # the data's projection\n",
-    "             col='time', col_wrap=1,  # multiplot settings\n",
-    "             aspect=ds.dims['lon'] / ds.dims['lat'],  # for a sensible figsize\n",
-    "             subplot_kws={'projection': map_proj})  # the plot's projection\n",
+    "p = air.plot(\n",
+    "    transform=ccrs.PlateCarree(),  # the data's projection\n",
+    "    col=\"time\",\n",
+    "    col_wrap=1,  # multiplot settings\n",
+    "    aspect=ds.dims[\"lon\"] / ds.dims[\"lat\"],  # for a sensible figsize\n",
+    "    subplot_kws={\"projection\": map_proj},\n",
+    ")  # the plot's projection\n",
     "\n",
     "# We have to set the map's options on all axes\n",
     "for ax in p.axes.flat:\n",
@@ -93,25 +97,25 @@
     "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6))\n",
     "\n",
     "# The first plot (in kelvins) chooses \"viridis\" and uses the data's min/max\n",
-    "air.plot(ax=ax1, cbar_kwargs={'label': 'K'})\n",
-    "ax1.set_title('Kelvins: default')\n",
-    "ax2.set_xlabel('')\n",
+    "air.plot(ax=ax1, cbar_kwargs={\"label\": \"K\"})\n",
+    "ax1.set_title(\"Kelvins: default\")\n",
+    "ax2.set_xlabel(\"\")\n",
     "\n",
     "# The second plot (in celsius) now chooses \"BuRd\" and centers min/max around 0\n",
     "airc = air - 273.15\n",
-    "airc.plot(ax=ax2, cbar_kwargs={'label': '°C'})\n",
-    "ax2.set_title('Celsius: default')\n",
-    "ax2.set_xlabel('')\n",
-    "ax2.set_ylabel('')\n",
+    "airc.plot(ax=ax2, cbar_kwargs={\"label\": \"°C\"})\n",
+    "ax2.set_title(\"Celsius: default\")\n",
+    "ax2.set_xlabel(\"\")\n",
+    "ax2.set_ylabel(\"\")\n",
     "\n",
     "# The center doesn't have to be 0\n",
-    "air.plot(ax=ax3, center=273.15, cbar_kwargs={'label': 'K'})\n",
-    "ax3.set_title('Kelvins: center=273.15')\n",
+    "air.plot(ax=ax3, center=273.15, cbar_kwargs={\"label\": \"K\"})\n",
+    "ax3.set_title(\"Kelvins: center=273.15\")\n",
     "\n",
     "# Or it can be ignored\n",
-    "airc.plot(ax=ax4, center=False, cbar_kwargs={'label': '°C'})\n",
-    "ax4.set_title('Celsius: center=False')\n",
-    "ax4.set_ylabel('')\n",
+    "airc.plot(ax=ax4, center=False, cbar_kwargs={\"label\": \"°C\"})\n",
+    "ax4.set_title(\"Celsius: center=False\")\n",
+    "ax4.set_ylabel(\"\")\n",
     "\n",
     "# Make it nice\n",
     "plt.tight_layout()"
@@ -143,9 +147,10 @@
     "\n",
     "# Plot data\n",
     "air2d.plot(ax=ax1, levels=levels)\n",
-    "air2d.plot(ax=ax2, levels=levels, cbar_kwargs={'ticks': levels})\n",
-    "air2d.plot(ax=ax3, levels=levels, cbar_kwargs={'ticks': levels,\n",
-    "                                               'spacing': 'proportional'})\n",
+    "air2d.plot(ax=ax2, levels=levels, cbar_kwargs={\"ticks\": levels})\n",
+    "air2d.plot(\n",
+    "    ax=ax3, levels=levels, cbar_kwargs={\"ticks\": levels, \"spacing\": \"proportional\"}\n",
+    ")\n",
     "\n",
     "# Show plots\n",
     "plt.tight_layout()"
@@ -178,12 +183,12 @@
     "isel_lats = [10, 15, 20]\n",
     "\n",
     "# Temperature vs longitude plot - illustrates the \"hue\" kwarg\n",
-    "air.isel(time=0, lat=isel_lats).plot.line(ax=ax1, hue='lat')\n",
-    "ax1.set_ylabel('°C')\n",
+    "air.isel(time=0, lat=isel_lats).plot.line(ax=ax1, hue=\"lat\")\n",
+    "ax1.set_ylabel(\"°C\")\n",
     "\n",
     "# Temperature vs time plot - illustrates the \"x\" and \"add_legend\" kwargs\n",
-    "air.isel(lon=30, lat=isel_lats).plot.line(ax=ax2, x='time', add_legend=False)\n",
-    "ax2.set_ylabel('')\n",
+    "air.isel(lon=30, lat=isel_lats).plot.line(ax=ax2, x=\"time\", add_legend=False)\n",
+    "ax2.set_ylabel(\"\")\n",
     "\n",
     "# Show\n",
     "plt.tight_layout()"
@@ -216,12 +221,12 @@
     "\n",
     "# The data is in UTM projection. We have to set it manually until\n",
     "# https://github.com/SciTools/cartopy/issues/813 is implemented\n",
-    "crs = ccrs.UTM('18')\n",
+    "crs = ccrs.UTM(\"18\")\n",
     "\n",
     "# Plot on a map\n",
     "ax = plt.subplot(projection=crs)\n",
-    "da.plot.imshow(ax=ax, rgb='band', transform=crs)\n",
-    "ax.coastlines('10m', color='r')"
+    "da.plot.imshow(ax=ax, rgb=\"band\", transform=crs)\n",
+    "ax.coastlines(\"10m\", color=\"r\")"
    ]
   },
   {
@@ -250,20 +255,27 @@
     "\n",
     "da = xr.tutorial.open_rasterio(\"RGB.byte\")\n",
     "\n",
-    "x, y = np.meshgrid(da['x'], da['y'])\n",
+    "x, y = np.meshgrid(da[\"x\"], da[\"y\"])\n",
     "transformer = Transformer.from_crs(da.crs, \"EPSG:4326\", always_xy=True)\n",
     "lon, lat = transformer.transform(x, y)\n",
-    "da.coords['lon'] = (('y', 'x'), lon)\n",
-    "da.coords['lat'] = (('y', 'x'), lat)\n",
+    "da.coords[\"lon\"] = ((\"y\", \"x\"), lon)\n",
+    "da.coords[\"lat\"] = ((\"y\", \"x\"), lat)\n",
     "\n",
     "# Compute a greyscale out of the rgb image\n",
-    "greyscale = da.mean(dim='band')\n",
+    "greyscale = da.mean(dim=\"band\")\n",
     "\n",
     "# Plot on a map\n",
     "ax = plt.subplot(projection=ccrs.PlateCarree())\n",
-    "greyscale.plot(ax=ax, x='lon', y='lat', transform=ccrs.PlateCarree(),\n",
-    "               cmap='Greys_r', shading=\"auto\",add_colorbar=False)\n",
-    "ax.coastlines('10m', color='r')"
+    "greyscale.plot(\n",
+    "    ax=ax,\n",
+    "    x=\"lon\",\n",
+    "    y=\"lat\",\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    cmap=\"Greys_r\",\n",
+    "    shading=\"auto\",\n",
+    "    add_colorbar=False,\n",
+    ")\n",
+    "ax.coastlines(\"10m\", color=\"r\")"
    ]
   }
  ],