Generally, changing the color scheme for a plotly express figure is very straight-forward. What's causing the problems here is the fact that species is a categorical variable. Continuous or numerical values are actually easier, but we'll get to that in a bit.

For categorical values, using color_discrete_map is a perfectly valid, albeit cumbersome approach. I prefer using the keyword argument continuous_colorscale in combination with px.colors.qualitative.Antique, where Antique can be changed to any of the discrete color schemes available in plotly express. Just run dir(px.colors.qualitative) to see what are available to you in the plotly version you are running:

['Alphabet',
 'Antique',
 'Bold',
 'D3',
 'Dark2',
 'Dark24',
 'G10',......]

Code 1:

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length",
                 color="species", color_discrete_sequence=px.colors.qualitative.Antique)

fig.show()

Plot 1:

So what about continuous variables?

Consider the following snippet:

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length",
                 color="sepal_length", color_continuous_scale=px.colors.sequential.Viridis)

fig.show()

Running this will produce this plot:

You can change the colors to any other theme available under dir(px.colors.sequential), for example color_continuous_scale=px.colors.sequential.Inferno, and get this plot:

What's possibly causing confusion here, is that setting color='species, and keeping color_continuous_scale=px.colors.sequential.Inferno will give you this plot:

The figure now jumps straight back to using the default plotly colors, without giving you any warning about color_continuous_scale=px.colors.sequential.Inferno not having an effect. This is because species is a categorical variable with these different values : ['setosa', 'versicolor', 'virginica'], so color_continuous_scale is simply ignored. For color_continuous_scale to take effect you'll have to use a numerical value, like sepal_length = [5.1, 4.9, 4.7, 4.6, 5. , 5.4, ...]

And this brings us right back to my initial answer for categorical values:

Use the keyword argument continuous_colorscale in combination with px.colors.qualitative

You can achieve this using only 2 more parameters in px.line:

• markers=True
• color_discrete_sequence=my_plotly_continuous_sequence

The complete code would look something like this (Note the list slicing [::4] so that the colors are well spaced):

import plotly.express as px
import pandas as pd
import numpy as np

x = [1, 2, 3, 4, 5, 6]

df = pd.DataFrame(
    {
        'x': x * 3,
        'y': list(np.array(x)) + list(np.array(x) ** 2) + list(np.array(x) ** .5),
        'color': list(np.array(x) * 0) + list(np.array(x) * 0 + 1) + list(np.array(x) * 0 + 2),
    }
)

fig = px.line(
    df,
    x='x',
    y='y',
    color='color',
    color_discrete_sequence=px.colors.sequential.Plasma[::4],
    markers=True,
    template='plotly'
)
fig.show()

This produces the following output.

In case you have more lines than the colors present in the colormap, you can construct a custom colorscale so that you get one complete sequence instead of a cycling sequence:

rgb = px.colors.convert_colors_to_same_type(px.colors.sequential.RdBu)[0]

colorscale = []
n_steps = 4  # Control the number of colors in the final colorscale
for i in range(len(rgb) - 1):
    for step in np.linspace(0, 1, n_steps):
        colorscale.append(px.colors.find_intermediate_color(rgb[i], rgb[i + 1], step, colortype='rgb'))

fig = px.line(df_e, x='temperature', y='probability', color='year', color_discrete_sequence=colorscale, height=900)
fig.show()

You should add new plots to the first one, I have a small number of samples to plot, you can play with other configurations as you want:

import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
from openpyxl import load_workbook
import numpy as np
import pandas as pd
from io import StringIO

import numpy as np; 
np.random.seed(0)
import seaborn as sns;




data = '''
x   y   Base_TT
14.1315559  55.75811117 1.871884861
14.66228957 57.02751992 1.599971112
14.49407157 56.06563489 1.307165278
13.21996788 55.4786748  1.411554445
14.00501286 55.72120854 1.968138334
12.73736102 56.71097302 1.309849028
14.56668525 56.74872925 1.719116945
13.24138764 56.41359089 2.000620417
14.94308088 56.54283706 1.668724723
14.5744739  56.05695327 1.266861528
'''

df_centroid_Coord = pd.read_csv(StringIO(data), delim_whitespace=True)


data = '''
ID "Ambulance station name" Longtitude Latitude
0 1 AImhult 14.128734 56.547992
1 2 Angelhdm 12.870739 56.242114
2 3 Alvesta 14.549503 56.920740
3 4 "Ostra Ljungby" 13.057450 56.188099
4 5 Broby 14.080958 56.254481
5 6 Bromölla 14.466869 56.072272
6 7 Försláv 12.814913 56.350098
7 9 Hasslehdm 13.778234 56.161536
8 10 Haganas 12.556995 56.206016
9 11 Hörby 13.643265 55.849811
10 12 "Halmstad.Vaster" 12.819960 56.674306 
'''

df_station = pd.read_csv(StringIO(data), delim_whitespace=True) 
df_station.rename(columns={'Longtitude':'x', 'Latitude':'y'}, inplace=True)
df_centroid_Coord['Ambulance_Treatment_Time'] = df_centroid_Coord ['Base_TT']

fig = px.scatter(df_centroid_Coord, x="x", y="y", 
                 title="Southern Region Centroids", 
                 color='Ambulance_Treatment_Time', 
                 log_x=True,
                 size_max=60, 
                 color_continuous_scale='Reds',
                 range_color=(0.5,2),
                 )

fig.update_traces(marker={'size': 4, 'symbol': 1})
fig.add_trace(go.Scatter(x=df_station['x'],
                         y=df_station['y'],
                         mode='markers+text',
                         text=df_station['Ambulance station name'],
                         textposition='top center',
                         showlegend=True,
                         marker=dict(
                             size=8,
                             symbol=2,
                             color='black'
                         )
                        )
             )



fig.show()