Used code with a lot of remarks
def create_line_chart_employee(dff):
#dff consists of columns, the ones used here are:
#orderDate
#orderDate_ym: date becomes something like 2025-07
#revenue
#revenue_ly => revenue last year
#employeeName => only used for the visual title, not
#relevant for the intersection part, the incoming dataframe is already
#filtered by employee and consists of 12 rows, 1 per month
#get interesting values meaning, max, min, start and end current revenue
ymax = dff['revenue'].max()
xmax = dff.loc[dff['revenue'].idxmax(),'orderDate_ym']
#get min revenue
ymin = dff['revenue'].min()
xmin = dff.loc[dff['revenue'].idxmin(),'orderDate_ym']
#first month
ystart = dff['revenue'].iloc[0]
xstart = dff['orderDate_ym'].iloc[0]
#last month
yend = dff['revenue'].iloc[-1]
xend = dff['orderDate_ym'].iloc[-1]
#title equals name of employee
employee_name = dff['employeeName'].iloc[0]
#before we begin, a copy of the original data is necessery
#to print the 2 linecharts as they are meant to be, not with
#intersections, the dff_org index will be adjusted to
#the index used for the df with intersections (date), so taht
#the x-axis can be shared.
dff_org = dff.copy(deep=True)
dff_org.index = pd.to_datetime(dff_org.orderDate)
#keep only relevant columns
dff_org= dff_org[['revenue', 'revenue_ly']]
#dff is the input dataframe with 12 datapoints, 1 for every month
#to calculate the intersections, the index is set to date/time
#and dff is stripped to the new index + revenue + revenue_ly
dff.index = pd.to_datetime(dff.orderDate)
dff = dff[['revenue', 'revenue_ly']]
#a new df, to create the intersections and join with calculated intersections in the end
df1 = dff.copy()
#initialize the column intersect and give all datapoints (12) the value none,
#intersection points will get start/stop
df1['intsect'] = 'none'
# --- Step 1: Find Intersection Points ---
intersections = []
date_index = df1.index
for i in range(len(df1) - 1):
x1, x2 = date_index[i], date_index[i + 1]
y1, y2 = df1.revenue.iloc[i], df1.revenue.iloc[i + 1]
y1_ly, y2_ly = df1.revenue_ly.iloc[i], df1.revenue_ly.iloc[i + 1]
# Check if the lines cross
if (y1 > y1_ly and y2 < y2_ly) or (y1 < y1_ly and y2 > y2_ly):
# Calculate slopes
days_diff = (x2 - x1).days + 1e-9
slope1 = (y2 - y1) / days_diff
slope2 = (y2_ly - y1_ly) / days_diff
# Solve for x where revenue = revenue_ly
x_intersect_days = (y1_ly - y1) / (slope1 - slope2 + 1e-9)
x_intersect = x1 + pd.Timedelta(days=x_intersect_days)
# Set revenue_ly = revenue at the intersection point
# This was actually something that went wrong with chatgpt
# the whole time unless I explained that if the revenue
# intersection points were correct, but the revenue_ly
# was incorrect, and revenue_ly equals revenue at the intersection
# it might as well use the revenue instead of calculated wrong revenue_ly
y_intersect = y1 + slope1 * x_intersect_days
# Store intersection points, intersection points get a intsect value stop
# the first en start, the second.
# The idea is that an intersection point is the end of an area
# and the start of a new one. The 10 minutes difference is for date index sake.
intersections.append((x_intersect, y_intersect, y_intersect, 'stop'))
#these 10 minutes are fake but needed because we're talking the index
intersections.append((x_intersect + pd.Timedelta(minutes=10), y_intersect, y_intersect, 'start'))
#intersections.append((closest_date, y_intersect, y_intersect, "start"))
# Convert intersections to DataFrame with same columns as df1
intersections_df = pd.DataFrame(intersections, columns=['date', 'revenue', 'revenue_ly','intsect'])
intersections_df.set_index('date', inplace=True)
# Merge intersection points into df1 (copy original with 12 datapoints and intersect value none)
df1 = pd.concat([df1, intersections_df], ignore_index=False).sort_index()
# labelvalue 1 equals better than last year, 0 ... You need to now the label of the
# previous row then finish the definition of an area correct.
df1['label'] = 0
prev_label = 0
for index, row in df1.iterrows():
#processing normal row meaning one which belongs to an original datapoint.
if row['intsect'] == 'none':
df1.at[index,'label'] = 1 if row['revenue'] > row['revenue_ly'] else 0
prev_label = df1.at[index,'label']
#processing intersection, stop means end of area, start means new area switch label
#meaning switch color
if ((row['revenue'] == row['revenue_ly']) and (row['intsect'] == 'stop')):
#end area at intersection
df1.at[index,'label'] = prev_label
prev_label = df1.at[index,'label']
else:
#start a new area from intersection
df1.at[index,'label'] = 0 if prev_label == 1 else 1
prev_label = df1.at[index,'label']
#the result in df1 is datapoints with labels and a grouping number,
#each grouping number defines an area.
df1['group'] = df1['label'].ne(df1['label'].shift()).cumsum()
# following lines create a dataframe in dfs per group
df1 = df1.groupby('group')
dfs = []
for name, data in df1:
dfs.append(data)
# # custom function to set fill color
def fillcol(label):
if label >= 1:
return 'rgba(255,0,0,1)'
else:
return 'rgba(0,142,150,1)'
fig = go.Figure()
#Creating the coloured areas, the line color is black, opacity
#0 meaning not visible. Each df in the dfs holds the datapoints for 1 green or
#red area. The more intersections, the more areas to define in the
#dfs, hence more df's to draw.
for df in dfs:
fig.add_traces(go.Scatter(x=df.index, y = df.revenue,
line = dict(color='rgba(0,0,0,0)')))
fig.add_traces(go.Scatter(x=df.index, y = df.revenue_ly,
line = dict(color='rgba(0,0,0,0)'),
fill='tonexty',
fillcolor = fillcol(df['label'].iloc[0])))
#printing the lines with the original datapoints
fig.add_traces(go.Scatter(x=dff_org.index, y = dff_org.revenue,
line = dict(color = 'rgba(64,64,64,1)', width=1),
fill='tozeroy', fillcolor = 'rgba(166,166,166,.1)'))
#ma2 => 'revenue_lastyear
fig.add_traces(go.Scatter(x=dff_org.index, y = dff_org.revenue_ly,
line = dict(color = 'rgba(166,166,166,1)', width=1)))
#START DOING NORMAL THINGS YOU DO WITH A GO.FIGURE OBJECT.
fig.update_layout(showlegend=False)
#add min max, start and last monthly sales value
fig.add_scatter(x=[xmax, xmin, xstart, xend],
y=[ymax, ymin, ystart, yend],
text=[ymax, ymin, ystart, yend],
texttemplate = "%{y:.3s}",
textposition=['top center','bottom center','top center','top center'],
mode='markers + text', marker=dict(color='Black', size=[10,10,10,14]))
fig.add_hline( y=0, line_dash="solid", line_width=1, opacity=1, line_color="Black")
# Adding labels of AC and PY at start of line (left)
# We know xy start for AC, have to query for PY
annotations = []
# # labeling the left_side of the plot with AC
annotations.append(dict(xref='paper', x=0.05, y=ystart,
xanchor='right', yanchor='middle',
text='AC',
font=dict(family='Arial',
size=16),
showarrow=False))
# # labeling the left_side of the plot with PY (first value revenue_ly)
annotations.append(dict(xref='paper', x=0.05, y=dff['revenue_ly'].iloc[0],
xanchor='right', yanchor='middle',
text='PY',
font=dict(family='Arial',
size=16),
showarrow=False))
fig.update_yaxes(title='', visible=False, showticklabels=False)
fig.update_xaxes(title='', visible=False, showticklabels=False)
fig.update_layout(
margin=dict(l=5, r=5, t=25, b=5),
plot_bgcolor='white',
annotations=annotations,
showlegend=False,
title = {
'text': f"{employee_name}",
},
)
return dcc.Graph(figure = fig)
and than it went 
), thank you for your support!