diff --git a/docs/source/recipes/plot_17_recipe.py b/docs/source/recipes/plot_17_recipe.py
index a34f872c3a..17112913eb 100644
--- a/docs/source/recipes/plot_17_recipe.py
+++ b/docs/source/recipes/plot_17_recipe.py
@@ -1,9 +1,11 @@
 """
-Plotting Contour Subplots with different Colour Maps/Scales
-============================================
+Plotting contour subplots with different colour maps/scales
+===========================================================
+
 In this recipe, we will plot data with different colour maps to illustrate
 the importance of choosing the correct one for a plot. This is to ensure
 the use of perceptually uniform scales and avoid unintended bias.
+
 """
 
 # %%
@@ -15,9 +17,8 @@
 
 # %%
 # 2. Read the field in:
-# Here I've used sample data ggap.nc (and later pressure=850), but you could use tas_A1.nc
-# (with time=15)
-
+# Here I've used sample data ggap.nc (and later pressure=850), but you
+# could use tas_A1.nc (with time=15)
 PATH="~/git-repos/cf-plot/cfplot/test/cfplot_data"
 f = cf.read(f"{PATH}/ggap.nc")[0]
 
@@ -26,18 +27,16 @@
 # Choose a set of predefined colour scales to view (based on NCAR)
 # You could also choose your own from
 # https://ncas-cms.github.io/cf-plot/build/colour_scales.html
-# Simply change the name in quotes and ensure the number of rows * number of columns =
-# number of colour scales
+# Simply change the name in quotes and ensure the
+# number of rows * number of columns = number of colour scales
 
 # %%
 # a. Perceptually uniform colour scales, with no zero value
-
 colour_scale = ["viridis", "magma", "inferno", "plasma", "parula", "gray"]
 cfp.gopen(rows=2, columns=3, bottom=0.2)
 
 # %%
 # b. NCAR Command Language - Enhanced to help with colour blindness
-
 colour_scale = [
     "StepSeq25",
     "posneg_2",
@@ -53,12 +52,10 @@
 
 # %%
 # c. Orography/bathymetry colour scales
-
-# These are used to show the shape/contour of landmasses, bear in mind the example data
-# we use is with pressure so doesnt accurately represent this.
-# You could instead use cfp.cscale('wiki_2_0', ncols=16, below=2, above=14) or any other
-# orography colour scale in a similar way
-
+# These are used to show the shape/contour of landmasses, bear in mind the
+# example data we use is with pressure so doesnt accurately represent this.
+# You could instead use cfp.cscale('wiki_2_0', ncols=16, below=2, above=14)
+# or any other orography colour scale in a similar way.
 colour_scale = [
     "os250kmetres",
     "wiki_1_0_2",
@@ -70,9 +67,9 @@
 cfp.gopen(rows=2, columns=3, bottom=0.2, file="ColourPlot.png")
 
 # %%
-# 5. We then use a for loop to cycle through all the different colour maps:
-# Only gpos has 1 added because it can only take 1 as its first value, otherwise there are
-# errors.
+# 4. We then use a for loop to cycle through all the different colour maps:
+# Only gpos has 1 added because it can only take 1 as its first value,
+# otherwise there are errors.
 for i, colour_scale in enumerate(colour_scale):
     cfp.gpos(i + 1)
     cfp.mapset(proj="cyl")