Plotly lineplot with different colors based on label column

Question

I want to generate a plotly lineplot of time series data (one value over time) and display this time series with different colors based on a label column. The data looks a bit like this:

data_temp = pd.DataFrame( {'VOC_Room_RAW': {Timestamp('2022-11-14 13:30:42.170881+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:30:52.173304+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:31:02.169674+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:31:12.176177+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:31:22.163966+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:31:32.178971+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:31:42.166390+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:31:52.172687+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:32:02.169452+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:32:12.177531+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:32:22.165460+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:32:32.178472+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:32:42.166544+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:32:52.170993+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:33:02.159443+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:33:12.175043+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:33:22.162845+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:33:32.168938+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:33:42.165498+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:33:52.172036+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:34:02.159681+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:34:12.166938+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:34:22.164066+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:34:32.169855+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:34:42.157552+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:34:52.164171+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:35:02.163900+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:35:12.168078+0000', tz='UTC'): -1.6, Timestamp('2022-11-14 13:35:22.156496+0000', tz='UTC'): -1.5, Timestamp('2022-11-14 13:35:32.163096+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:35:42.160587+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:35:52.167359+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:36:02.155874+0000', tz='UTC'): -1.4, Timestamp('2022-11-14 13:36:12.162717+0000', tz='UTC'): -1.4, Timestamp('2022-11-14 13:36:22.159900+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:36:32.166696+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:36:42.155219+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:36:52.162284+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:37:02.159573+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:37:12.167862+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:37:22.154922+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:37:32.161580+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:37:42.158671+0000', tz='UTC'): -1.2, Timestamp('2022-11-14 13:37:52.165415+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:38:02.153860+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:38:12.160584+0000', tz='UTC'): -1.3, Timestamp('2022-11-14 13:38:22.157597+0000', tz='UTC'): -1.4, Timestamp('2022-11-14 13:38:32.163685+0000', tz='UTC'): -1.5, Timestamp('2022-11-14 13:38:42.150995+0000', tz='UTC'): -1.5, Timestamp('2022-11-14 13:38:52.166477+0000', tz='UTC'): -1.6, Timestamp('2022-11-14 13:39:02.154987+0000', tz='UTC'): -1.6, Timestamp('2022-11-14 13:39:12.160450+0000', tz='UTC'): -1.6, Timestamp('2022-11-14 13:39:22.156904+0000', tz='UTC'): -1.6, Timestamp('2022-11-14 13:39:32.162589+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:39:42.150555+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:39:52.167209+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:40:02.153529+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:40:12.159516+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:40:22.157159+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:40:32.162248+0000', tz='UTC'): -1.7, Timestamp('2022-11-14 13:40:42.149829+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:40:52.164636+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:41:02.152116+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:41:12.172102+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:41:22.178258+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:41:32.200465+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:41:42.149235+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:41:52.181677+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:42:02.216922+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:42:12.252449+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:42:22.153720+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:42:32.220811+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:42:42.256163+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:42:52.189255+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:43:02.224632+0000', tz='UTC'): -1.8, Timestamp('2022-11-14 13:43:12.156886+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:43:22.193032+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:43:32.234139+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:43:42.144419+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:43:52.159229+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:44:02.147415+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:44:12.152869+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:44:22.149148+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:44:32.155063+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:44:42.151774+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:44:52.157120+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:45:02.144945+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:45:12.159299+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:45:22.146951+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:45:32.153065+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:45:42.149337+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:45:52.155004+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:46:02.143785+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:46:12.157143+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:46:22.215777+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:46:32.151833+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:46:42.184176+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:46:52.219645+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:47:02.141007+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:47:12.188116+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:47:22.142773+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:47:32.158141+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:47:42.144595+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:47:52.227231+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:48:02.146784+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:48:12.152311+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:48:22.230991+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:48:32.154166+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:48:42.141442+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:48:52.147183+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:49:02.145220+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:49:12.148978+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:49:22.145016+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:49:32.150643+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:49:42.137980+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:49:52.152619+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:50:02.139944+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:50:12.145561+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:50:22.143900+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:50:32.147504+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:50:42.143450+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:50:52.149911+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:51:02.136407+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:51:12.150895+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:51:22.138097+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:51:32.143922+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:51:42.140051+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:51:52.145550+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:52:02.141577+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:52:12.147502+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:52:22.134924+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:52:32.149514+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:52:42.136817+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:52:52.142377+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:53:02.138448+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:53:12.144579+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:53:22.140144+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:53:32.146092+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:53:42.134612+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:53:52.147919+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:54:02.135146+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:54:12.140733+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:54:22.137382+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 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13:57:12.161803+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:57:22.197110+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:57:32.144262+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:57:42.165584+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:57:52.200977+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:58:02.236318+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:58:12.137332+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:58:22.204684+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:58:32.240095+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:58:42.173129+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:58:52.141553+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:59:02.243990+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:59:12.176967+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:59:22.130697+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:59:32.136503+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 13:59:42.180795+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 13:59:52.216188+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 14:00:02.125358+0000', tz='UTC'): -1.9, Timestamp('2022-11-14 14:00:12.140322+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 14:00:22.219985+0000', tz='UTC'): -2.0, Timestamp('2022-11-14 14:00:32.153002+0000', tz='UTC'): -1.9}, 'scenario_label': {Timestamp('2022-11-14 13:30:42.170881+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:30:52.173304+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:31:02.169674+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:31:12.176177+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:31:22.163966+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:31:32.178971+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:31:42.166390+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:31:52.172687+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:32:02.169452+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:32:12.177531+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:32:22.165460+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:32:32.178472+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:32:42.166544+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:32:52.170993+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:33:02.159443+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:33:12.175043+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:33:22.162845+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:33:32.168938+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:33:42.165498+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:33:52.172036+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:34:02.159681+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:34:12.166938+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:34:22.164066+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:34:32.169855+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:34:42.157552+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:34:52.164171+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:35:02.163900+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:35:12.168078+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:35:22.156496+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:35:32.163096+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:35:42.160587+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:35:52.167359+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:36:02.155874+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:36:12.162717+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:36:22.159900+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:36:32.166696+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:36:42.155219+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:36:52.162284+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:37:02.159573+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:37:12.167862+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:37:22.154922+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:37:32.161580+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:37:42.158671+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:37:52.165415+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:38:02.153860+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:38:12.160584+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:38:22.157597+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:38:32.163685+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:38:42.150995+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:38:52.166477+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:39:02.154987+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:39:12.160450+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:39:22.156904+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:39:32.162589+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:39:42.150555+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:39:52.167209+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 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tz='UTC'): 'Cable', Timestamp('2022-11-14 13:42:42.256163+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:42:52.189255+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:43:02.224632+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:43:12.156886+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:43:22.193032+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:43:32.234139+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:43:42.144419+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:43:52.159229+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:44:02.147415+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:44:12.152869+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:44:22.149148+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:44:32.155063+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:44:42.151774+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:44:52.157120+0000', tz='UTC'): 'Cable', Timestamp('2022-11-14 13:45:02.144945+0000', tz='UTC'): 'Cable', 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I´ve used the following code:

# Define measurement
measurement = 'VOC_Room_RAW'

# Create the figure and line chart
fig = px.line(data_temp, x='Date', y=measurement, color='scenario_label', line_group=None, render_mode='split')

# Add layout and style
fig.update_layout(
    title="Time Series Data",
    xaxis_title="Date",
    yaxis_title=str(measurement),
    legend_title="Label",
    font=dict(
        family="Arial",
        size=12,
        color="black"
    )
)

# Show the figure
fig.show()

which correctly plots the data and uses different colors depending on the label column. But one problem that occours every time is that the different colored "sublines" are connected automatically even if the label isn´t present, looks a bit like the following plot. But I don´t want these connections in between.

Plot generated by the presented code

I think there are some errors in your example df. Where is data_temp coming from? Please fix these inconsistencies in your question and also include the imports you made. This will help others to look into this question. — tetris programming, Mar 06 '23 at 15:16
I did not see any connections between different colored lines. Your lines are overlapped. II also did not understand what you meant by even if the label is not present. Are there some lines plotted without labels? — Hamzah, Mar 06 '23 at 21:19
@pascal_ Please share a [sample](https://stackoverflow.com/questions/63163251/pandas-how-to-easily-share-a-sample-dataframe-using-df-to-dict/63163254#63163254) of your data. — vestland, Mar 07 '23 at 07:56
@ Hamzah I mean connections between the sublines with the same label (e.g. when the label "Cable" occours, a subline in blue is plotted correctly but there is a linear connection to the next occourence of that label (next subline). This is what i meant with "even if the label isn´t present" . — pascal_, Mar 08 '23 at 07:49
@ Hamzah And you´re correct, thats the reason for overlapping lines in the end. — pascal_, Mar 08 '23 at 07:56

Plotly lineplot with different colors based on label column

0 Answers0