Plotly Express: Prevent bars from stacking when Y-axis catgories have the same name - bar-chart

Question

I'm new to plotly.
Working with:
Ubuntu 20.04
Python 3.8.10
plotly==5.10.0
I'm doing a comparative graph using a horizontal bar chart. Different instruments measuring the same chemical compounds. I want to be able to do an at-a-glance, head-to-head comparison if the measured value amongst all machines.
The problem is; if the compound has the same name amongst the different instruments - Plotly stacks the data bars into a single bar with segment markers. I very much want each bar to appear individually. Is there a way to prevent Plotly Express from automatically stacking the common bars??
Examples:
CODE
gobardata = []
for blended_name in _df[:20].blended_name: # should always be unique
##################################
# Unaltered compound names
compound_names = [str(c) for c in _df[_df.blended_name == blended_name]["injcompound_name"].tolist()]
# Random number added to end of compound_names to make every string unique
# compound_names = ["{} ({})".format(str(c),random.randint(0, 1000)) for c in _df[_df.blended_name == blended_name]["injcompound_name"].tolist()]
##################################
deltas = _df[_df.blended_name == blended_name]["delta_rettime"].to_list()
gobardata.append(
go.Bar(
name = blended_name,
x = deltas,
y = compound_names,
orientation='h',
))
fig = go.Figure(data = gobardata)
fig.update_traces(width=1)
fig.update_layout(
bargap=1,
bargroupgap=.1,
xaxis_title="Delta Retention Time (Expected - actual)",
yaxis_title="Instrument name(Injection ID)"
)
fig.show()
What I'm getting (Using actual, but repeated, compound names)
What I want (Adding random text to each compound name to make it unique)

Answer 1

OK. Figured it out. This is probably pretty klugy, but it consistently works.
Basically...
Use go.FigureWidget...
...with make_subplots having a common x-axis...
...controlling the height of each subplot based on number of bars.
Every bar in each subplot is added as an individual trace...
...using a dictionary matching bar name to a common color.
The y-axis labels for each subplot is a list containing the machine name as [0], and then blank placeholders ('') so the length of the y-axis list matches the number of bars.
And manually manipulating the legend so each bar name appears only once.
# Get lists of total data
all_compounds = list(_df.injcompound_name.unique())
blended_names = list(_df.blended_name.unique())
#################################################################
# The heights of each subplot have to be set when fig is created.
# fig has to be created before adding traces.
# So, create a list of dfs, and use these to calculate the subplot heights
dfs = []
subplot_height_multiplier = 20
subplot_heights = []
for blended_name in blended_names:
df = _df[(_df.blended_name == blended_name)]#[["delta_rettime", "injcompound_name"]]
dfs.append(df)
subplot_heights.append(df.shape[0] * subplot_height_multiplier)
chart_height = sum(subplot_heights) # Prep for the height of the overall chart.
chart_width = 1000
# Make the figure
fig = make_subplots(
rows=len(blended_names),
cols=1,
row_heights = subplot_heights,
shared_xaxes=True,
)
# Create the color dictionary to match a color to each compound
_CSS_color = CSS_chart_color_list()
colors = {}
for compound in all_compounds:
try: colors[compound] = _CSS_color.pop()
except IndexError:
# Probably ran out of colors, so just reuse
_CSS_color = CSS_color.copy()
colors[compound] = _CSS_color.pop()
rowcount = 1
for df in dfs:
# Add bars individually to each subplot
bars = []
for label, labeldf in df.groupby('injcompound_name'):
fig.add_trace(
go.Bar(x = labeldf.delta_rettime,
y = [labeldf.blended_name.iloc[0]]+[""]*(len(labeldf.delta_rettime)-1),
name = label,
marker = {'color': colors[label]},
orientation = 'h',
),
row=rowcount,
col=1,
)
rowcount += 1
# Set figure to FigureWidget
fig = go.FigureWidget(fig)
# Adding individual traces creates redundancies in the legend.
# This removes redundancies from the legend
names = set()
fig.for_each_trace(
lambda trace:
trace.update(showlegend=False)
if (trace.name in names) else names.add(trace.name))
fig.update_layout(
height=chart_height,
width=chart_width,
title_text="∆ of observed RT to expected RT",
showlegend = True,
)
fig.show()