Ignore Non Trading days (Holidays / remove gaps) in Plotly candlesticks for "Minutes / Hours) data

There are other answers to Plotly: How to remove empty dates from x axis that will suit your use-case better. This can be a bit tricky for 5 minute intervals. Just consider the formatting of your timestamps, and follow these steps carefully:

• find all time intervals starting from your first observation to your last
• find out which of your observations that occur in that complete timeline
• isolate the rest of the dates and include them in the rangebreaks attribute of the x-axis
• adjust the dvalues attribute of rangebreaks to match your time interval in milliseconds with fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)])

Essential code elements:

# grab first and last observations from df.date and make a continuous date range from that
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1], freq = '5min')

# check which dates from your source that also accur in the continuous date range
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in df['Date']]

# isolate missing timestamps
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]

# adjust xaxis for rangebreaks
fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)])

Plot 1: Missing timestamps are showing

Plot 2: Missing timestamps are not showing

Complete code:

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np

# sample data
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv').tail(90)
df = df[df.columns[:6]]
df['Date'] = pd.date_range("2018-01-01", periods=len(df), freq="5min")
df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
df = df.tail(10)

# remove some data
np.random.seed(0)
remove_n = 4
drop_indices = np.random.choice(df.index, remove_n, replace=False)
df = df.drop(drop_indices)

# plotly candlestick figure
fig = go.Figure(data=[go.Candlestick(
    x=df['Date'],
    open=df['Open'], high=df['High'],
    low=df['Low'], close=df['Close'],
)])

# grab first and last observations from df.date and make a continuous date range from that
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1], freq = '5min')

# check which dates from your source that also accur in the continuous date range
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in df['Date']]

# isolate missing timestamps
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
dt_breaks = pd.to_datetime(dt_breaks)

fig.show()
fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)] )
print(fig.layout.xaxis.rangebreaks)
fig.show()