Plotly: How to style a plotly figure so that it doesn't display gaps for missing dates?

Question

I have a plotly graph of the EUR/JPY exchange rate across a few months in 15 minute time intervals, so as a result, there is no data from friday evenings to sunday evenings.

Answer 1

Even if some dates are missing in your dataset, plotly interprets your dates as date values, and shows even missing dates on your timeline. One solution is to grab the first and last dates, build a complete timeline, find out which dates are missing in your original dataset, and include those dates in:

fig.update_xaxes(rangebreaks=[dict(values=dt_breaks)])

This will turn this figure:

Into this:

Complete code:

import plotly.graph_objects as go
from datetime import datetime
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')

# remove some dates to build a similar case as in the question
df = df.drop(df.index[75:110])
df = df.drop(df.index[210:250])
df = df.drop(df.index[460:480])

# build complete timepline from start date to end date
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1])

# retrieve the dates that ARE in the original datset
dt_obs = [d.strftime("%Y-%m-%d") for d in pd.to_datetime(df['Date'])]

# define dates with missing values
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d").tolist() if not d in dt_obs]

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

# hide dates with no values
fig.update_xaxes(rangebreaks=[dict(values=dt_breaks)])

fig.update_layout(yaxis_title='AAPL Stock')

fig.show()

Answer 2

thanks for the amazing sample! works on daily data but with intraday / 5min data rangebreaks only leave one day on chart

    # build complete timepline 
    dt_all = pd.date_range(start=df.index[0],end=df.index[-1], freq="5T")
    # retrieve the dates that ARE in the original datset
    dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in pd.to_datetime(df.index, format="%Y-%m-%d %H:%M:%S")]
    # define dates with missing values
    dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]