plotly Candlestick Chart in Python (3 Examples)


Hi! This tutorial will show you how to build a plotly candlestick chart in Python.

Here is an overview:

Let’s get into the Python code!


Install & Import plotly & pandas Library

To install and import plotly and pandas, run the lines of code below in your preferred Python programming IDE or editor:

# install plotly & pandas
pip install plotly pandas
# import plotly & pandas
import plotly.graph_objects as go
import pandas as pd

When it comes to DataFrame manipulation in Python, pandas is the go-to library. We need pandas in this project to be able to read data from an external source.

So, with plotly and pandas installed and imported into our Python programming environment, we can now build a candlestick chart.

First, though, we need to get the sample dataset that we will visualize. This is where pandas comes in.

Get Sample Data

We will fetch the data from GitHub using the line of code below:

df = pd.read_csv('')

You can then preview the first 10 rows of the data by running:

#  	Date 	        AAPL.Open 	AAPL.High 	AAPL.Low 	AAPL.Close 	AAPL.Volume 	AAPL.Adjusted 	dn 	        mavg 	        up 	        direction
# 0 	2015-02-17 	127.489998 	128.880005 	126.919998 	127.830002 	63152400 	122.905254 	106.741052 	117.927667 	129.114281 	Increasing
# 1 	2015-02-18 	127.629997 	128.779999 	127.449997 	128.720001 	44891700 	123.760965 	107.842423 	118.940333 	130.038244 	Increasing
# 2 	2015-02-19 	128.479996 	129.029999 	128.330002 	128.449997 	37362400 	123.501363 	108.894245 	119.889167 	130.884089 	Decreasing
# 3 	2015-02-20 	128.619995 	129.500000 	128.050003 	129.500000 	48948400 	124.510914 	109.785449 	120.763500 	131.741551 	Increasing
# 4 	2015-02-23 	130.020004 	133.000000 	129.660004 	133.000000 	70974100 	127.876074 	110.372516 	121.720167 	133.067817 	Increasing
# 5 	2015-02-24 	132.940002 	133.600006 	131.169998 	132.169998 	69228100 	127.078049 	111.094869 	122.664834 	134.234798 	Decreasing
# 6 	2015-02-25 	131.559998 	131.600006 	128.149994 	128.789993 	74711700 	123.828261 	113.211918 	123.629667 	134.047415 	Decreasing
# 7 	2015-02-26 	128.789993 	130.869995 	126.610001 	130.419998 	91287500 	125.395469 	114.165299 	124.282333 	134.399367 	Increasing
# 8 	2015-02-27 	130.000000 	130.570007 	128.240005 	128.460007 	62014800 	123.510987 	114.966848 	124.842667 	134.718485 	Decreasing
# 9 	2015-03-02 	129.250000 	130.279999 	128.300003 	129.089996 	48096700 	124.116706 	115.877090 	125.403667 	134.930243 	Decreasing

Now that we have loaded the dataset, we can build the plotly candlestick visualization.

Example 1: Build Candlestick Chart Chart

In this first example, we will build a basic candlestick chart:

fig = go.Figure(data=[go.Candlestick(x=df['Date'],

In the above example, we passed an array to the go.Figure() function containing the go.Candlestick() function, wherein we passed the DataFrame df specifying the Date column as the x axis.

Then, we also passed the respective columns in the DataFrame to the open, high, low, and close arguments in the go.Candlestick() function.

Finally, we displayed the chart using

Example 2: Remove Range Slider from Candlestick Chart

In this second example, we will remove the range slider from the candlestick chart:

fig = go.Figure(data=[go.Candlestick(x=df['Date'],

Here, we only added a line of code to remove the range slider. In the fig.update_layout() method, we defined the xaxis_rangeslider_visible = argument as False in order to hide the range slider.

Example 3: Customize Chart Colors in Candlestick Chart

In this final example, we will customize the colors of the candlestick chart:

fig = go.Figure(data=[go.Candlestick(
    open=df['AAPL.Open'], high=df['AAPL.High'],
    low=df['AAPL.Low'], close=df['AAPL.Close'],
    increasing_line_color= 'blue', decreasing_line_color= 'yellow'

In the above example, we have changed the colors of the candlestick chart. To do so, all we had to do was to define the increasing_line_color = and decreasing_line_color = arguments in the go.Candlestick() function with the colors we want, which in this case were blue and yellow.

That way, we have customized the colors of the candlestick chart.

Video, Further Resources & Summary

Do you need more explanations on how to build plotly candlestick charts in Python? Then you should have a look at the following YouTube video of the Statistics Globe YouTube channel.

In the video, we explain in some more detail how to build plotly candlestick charts in Python.


The YouTube video will be added soon.


With that, we have demonstrated how to build a plotly candlestick chart in Python. Furthermore, you could have a look at some of the other interesting plotly in Python tutorials on Statistics Globe:

This post has shown how to build plotly candlestick charts in Python. I hope you found this tutorial helpful! In case you have further questions, you may leave a comment below.


R & Python Expert Ifeanyi Idiaye

This page was created in collaboration with Ifeanyi Idiaye. You might check out Ifeanyi’s personal author page to read more about his academic background and the other articles he has written for the Statistics Globe website.


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