How to plot daily data against a 24 hour axis (00:00 - 23:59:59)

I have a dataset with date_time, date, time, and a VALUE1 column that shows measurement values of each time point. For the same ID, there are multiple measurements over a day. Besides, there are 6 different 24 hour measurements for an ID, which is shown in INSPECTION column.

import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker

random.seed(0)

df = pd.DataFrame({'DATE_TIME': pd.date_range('2022-11-01', '2022-11-06 23:00:00', freq='20min'),
                   'ID': [random.randrange(1, 3) for n in range(430)]})
df['VALUE1'] = [random.uniform(110, 160) for n in range(430)]
df['VALUE2'] = [random.uniform(50, 80) for n in range(430)]
df['INSPECTION'] = df['DATE_TIME'].dt.day
# df['INSPECTION'] = df['INSPECTION'].replace(6, 1)
# df['INSPECTION'] = df['INSPECTION'].replace(3, 1)

df['MODE'] = np.select([df['INSPECTION'] == 1, df['INSPECTION'].isin([2, 3])], ['A', 'B'], 'C')
df['TIME'] = df['DATE_TIME'].dt.time
df['TIME'] = df['TIME'].astype('str')

df['TIMEINTERVAL'] = df.DATE_TIME.diff().astype('timedelta64[m]')
df['TIMEINTERVAL'] = df['TIMEINTERVAL'].fillna(0)


def to_day_period(s):
    bins = ['0', '06:00:00', '13:00:00', '18:00:00', '23:00:00', '24:00:00']
    labels = ['Nighttime', 'Daytime', 'Daytime', 'Nighttime', 'Nighttime']

    return pd.cut(
        pd.to_timedelta(s),
        bins=list(map(pd.Timedelta, bins)),
        labels=labels, right=False, ordered=False
    )


df['TIME_OF_DAY'] = to_day_period(df['TIME'])
df_monthly = df

# ++++++++++++++++++++++++++++++++ sns plot ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
df_id = df[df.ID==1]
sns.set_style('darkgrid')
sns.set(rc={'figure.figsize':(14,8)})
#print(df_id.INSPECTION.unique())
ax = sns.lineplot(data=df_id, x ='TIME', y = 'VALUE1',
                  hue='INSPECTION', palette='viridis',
                  legend='full', lw=3)

ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
plt.legend(bbox_to_anchor=(1, 1))
plt.ylabel('VALUE1')
plt.xlabel('TIME')
plt.show()

How can I show a 24 hours day cycle on the x-axis without repeating the time again? To articulate, x-axis starts at 00:40:00 and then it shows 00:00:00 again. Is there a way to deal with this too? I want to show only time from 00:00:00 until 23:59:00 on the x-axis without repeating the time.

• Create a column representing the total number of seconds for a given day, which will be used as the x-axis, and will ensure every point for a given 'INSPECTION' is properly positioned.
  • Given a specific day, subtract the day at midnight from the current datetime, and use the .total_seconds() method.
  • df.DATE_TIME.apply(lambda row: (row - row.replace(hour=0, minute=0, second=0, microsecond=0)).total_seconds())
• Set ticks to be every hour.
  • ax.xaxis.set_major_locator(tkr.MultipleLocator(3600))
• Create a list of every hour, which will be used as the labels. [''] is for the last tick at '00:00' of the next day.
  • hours = [dtime(i).strftime('%H:%M') for i in range(24)] + ['']
• This can also be done with fig, (ax1, ax2) = plt.subplots(2, 1), but that's a cosmetic change that's not relevant to the question.
  • See How to plot in multiple subplots for additional details about plotting in subplots.
• A seaborn legend should be move with sns.move_legend, not plt.legend, as per Move seaborn plot legend to a different position.
• It is more consistent to stick with the object oriented interface using ax, the alias for matplotlib.axes.Axes, than to alternate between ax and plt.
• Tested in python 3.11.2, pandas 2.0.0, matplotlib 3.7.1, seaborn 0.12.2

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
from datetime import time as dtime

# given the existing dataframe with the DATE_TIME column as a datetime Dtype

# add a column for total seconds
df['total_seconds'] = df.DATE_TIME.apply(lambda row: (row - row.replace(hour=0, minute=0, second=0, microsecond=0)).total_seconds())

# iterate through each ID
for id_ in sorted(df.ID.unique()):
    
    # select the data for the given id_
    data = df[df.ID.eq(id_)]

    # create a figure
    fig = plt.figure(figsize=(10, 6))

    # plot the data
    ax = sns.lineplot(data=data, x ='total_seconds', y = 'VALUE1', hue='INSPECTION', palette='viridis', legend='full')
    
    # set the title and labels
    ax.set(title=f'ID: {id_}', xlabel='TIME', ylabel='VALUE1')
    
    # move the legend
    sns.move_legend(ax, bbox_to_anchor=(1.0, 0.5), loc='center left', frameon=False)

    # constrain the x-axis limits to the number of seconds in a day
    ax.set_xlim(0, 24*3600)

    # create labels for every hour in the day, and add an extra spot for the last tick position
    hours = [dtime(i).strftime('%H:%M') for i in range(24)] + ['']

    # create xticks at every hour
    ax.xaxis.set_major_locator(tkr.MultipleLocator(3600))
    
    # set the ticks and corresponding labels; cut off extra starting and ending ticks to match labels
    ax.set_xticks(ticks=ax.get_xticks()[1:-1], labels=hours, rotation=90)
    
    # remove spines
    ax.spines[['top', 'right']].set_visible(False)

df.head()

            DATE_TIME  ID      VALUE1     VALUE2  INSPECTION MODE      TIME    TIMEINTERVAL  total_seconds TIME_OF_DAY
0 2022-11-01 00:00:00   2  145.003985  57.488269           1    A  00:00:00             NaT            0.0   Nighttime
1 2022-11-01 00:20:00   2  142.449613  75.888882           1    A  00:20:00 0 days 00:20:00         1200.0   Nighttime
2 2022-11-01 00:40:00   1  119.748681  70.052981           1    A  00:40:00 0 days 00:20:00         2400.0   Nighttime
3 2022-11-01 01:00:00   2  149.170848  69.793085           1    A  01:00:00 0 days 00:20:00         3600.0   Nighttime
4 2022-11-01 01:20:00   2  148.873049  56.777515           1    A  01:20:00 0 days 00:20:00         4800.0   Nighttime