python plotly how to remove datetime gaps in candle stick chart?

For your code change the Datetime to string to force it to none datetime type axis

df["Datetime"] = df.index.dt.strftime("%Y/%m/%d %H:%M")

Below is the code to hide outside trading hours and weekends.

    fig = go.Figure(data = [go.Candlestick(x = df['date'], open = df['Open'], high = df['High'], low = df['Low'], close = df['Close'])])
    fig.update_xaxes(
          rangeslider_visible = True,
          rangebreaks = [
             # NOTE: Below values are bound(not single values), ie.hide x to y
             dict(bounds = ["sat", "mon"]), # hide weekends, eg.hide sat to before mon
             dict(bounds = [16, 9.5], pattern = "hour"), # hide hours outside of 9.30 am - 4 pm
             # dict(values = ["2019-12-25", "2020-12-24"]) # hide holidays(Christmas and New Year 's, etc)]
          ) fig.update_layout(
             title = 'Stock Analysis',
             yaxis_title = f '{symbol} Stock'
          )

          fig.show()

fill nan where we have gaps (i.e. reindexing the dataframe);
create datebreaks for all nan values;
set rangebreak values on xaxes config

begin_date, end_date = [df.iloc[0].name, df.iloc[-1].name]
df = df.reindex(pd.date_range(begin_date, end_date, freq = 'D'))
datebreaks = df['Close'][df_plot['Close'].isnull()].index
fig.update_xaxes(rangebreaks = [dict(values = datebreaks)])

Suggestion : 2

Time series can be represented using either plotly.express functions (px.line, px.scatter, px.bar etc) or plotly.graph_objects charts objects (go.Scatter, go.Bar etc). For more examples of such charts, see the documentation of line and scatter plots or bar charts.,The data range can be set manually using either datetime.datetime objects, or date strings.,Note: a known limitation of this feature is that it does not support scattergl traces. When using this feature on plots with more than a few hundred data points with px.scatter or px.line or px.area, you may need to pass in render_mode="svg" to ensure that the underlying trace type is scatter and not scattergl.,Any kind of cartesian chart can be placed on date axes, for example this bar chart of relative stock ticker values.

# Using plotly.express
import plotly.express as px

df = px.data.stocks()
fig = px.line(df, x = 'date', y = "GOOG")
fig.show()

# Using graph_objects
import plotly.graph_objects as go

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

fig = go.Figure([go.Scatter(x = df['Date'], y = df['AAPL.High'])])
fig.show()

import plotly.express as px

df = px.data.stocks(indexed = True) - 1
fig = px.bar(df, x = df.index, y = "GOOG")
fig.show()

import plotly.express as px

df = px.data.stocks(indexed = True) - 1
fig = px.area(df, facet_col = "company", facet_col_wrap = 2)
fig.show()

import plotly.express as px
df = px.data.stocks()
fig = px.line(df, x = "date", y = df.columns,
   hover_data = {
      "date": "|%B %d, %Y"
   },
   title = 'custom tick labels')
fig.update_xaxes(
   dtick = "M1",
   tickformat = "%b\n%Y")
fig.show()

import plotly.express as px
df = px.data.stocks()
fig = px.line(df, x = "date", y = df.columns,
   hover_data = {
      "date": "|%B %d, %Y"
   },
   title = 'custom tick labels with ticklabelmode="period"')
fig.update_xaxes(
   dtick = "M1",
   tickformat = "%b\n%Y",
   ticklabelmode = "period")
fig.show()

Suggestion : 3

This Answer says xaxis=dict(type = "category") but I don't know where to use that argument (fresh from matplotlib only for candlesticks),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 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

Following some Links, I found that running the below code on Day data, I can easily remove gaps:

dt_all = pd.date_range(start = stocks.iloc[0, 0], end = stocks.iloc[-1, 0], freq = f '{freq}min')
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in pd.to_datetime(stocks.DATE)]
dt_breaks = [d
   for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs
]

range_selector = dict(buttons = list([
   dict(count = 5, label = '5Min', step = 'minute', stepmode = 'backward'),
   dict(count = 15, label = '15Min', step = 'minute', stepmode = 'backward'),
   dict(count = 75, label = '75M', step = 'minute', stepmode = 'backward'),
   dict(count = 1, label = '1D', step = 'day', stepmode = 'backward'),
   dict(step = 'all')
]))

candle = go.Figure(data = [go.Candlestick(opacity = 0.9, x = stocks['Date'], name = 'X',
   open = stocks['Open'],
   high = stocks['High'],
   low = stocks['Low'],
   close = stocks['Close']), ])

candle.update_xaxes(
   title_text = 'Date',
   rangeslider_visible = True,
   rangebreaks = [dict(values = dt_breaks)],
   range_selector = range_selector)

But I have 5 Minute Data as:

    DATE OPEN HIGH LOW CLOSE 52 W H 52 W L SYMBOL
    374 2022 - 01 - 14 15: 25: 00 + 05: 30 720.25 722.35 720.25 721.55 NaN NaN BHARTIARTL
    373 2022 - 01 - 14 15: 20: 00 + 05: 30 720.30 720.45 719.45 720.25 NaN NaN BHARTIARTL
    372 2022 - 01 - 14 15: 15: 00 + 05: 30 720.75 720.90 720.15 720.30 NaN NaN BHARTIARTL
    371 2022 - 01 - 14 15: 10: 00 + 05: 30 720.35 720.90 720.20 720.70 NaN NaN BHARTIARTL
    370 2022 - 01 - 14 15: 05: 00 + 05: 30 720.70 720.90 720.05 720.20 NaN NaN BHARTIARTL
       ...........................
       4 2022 - 01 - 10 09: 35: 00 + 05: 30 706.05 707.15 705.65 706.55 NaN NaN BHARTIARTL
    3 2022 - 01 - 10 09: 30: 00 + 05: 30 705.90 706.40 705.05 706.05 NaN NaN BHARTIARTL
    2 2022 - 01 - 10 09: 25: 00 + 05: 30 707.10 707.95 705.60 705.60 NaN NaN BHARTIARTL
    1 2022 - 01 - 10 09: 20: 00 + 05: 30 709.00 709.40 706.15 707.10 NaN NaN BHARTIARTL
    0 2022 - 01 - 10 09: 15: 00 + 05: 30 705.40 709.00 705.40 708.55 NaN NaN BHARTIARTL

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)])

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()

Suggestion : 4

Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks),Plotly is another Python library that provides functionality to create candlestick charts. It allows us to create interactive candlestick charts.,The first library which we'll explore for plotting candlestick charts in Python is mplfinance. It used to be available as a matplotlib module earlier but now it has moved out and has become an independent library. We can generate static candlestick charts using it.,Below we have explained another example of creating a candlestick chart using cufflinks. We have added Bollinger bands, simple moving average, and relative strength index studies to the chart as well this time.

import mplfinance as fplt

print("MPLFinance Version : {}".format(fplt.__version__))

MPLFinance Version: 0.12 .3 a3

import plotly

print("Plotly Version : {}".format(plotly.__version__))

Plotly Version: 4.5 .2

import bokeh

print("Bokeh Version : {}".format(bokeh.__version__))

Bokeh Version: 2.0 .2