Chart styling

Chart styling#

This section discusses chart styling that is common across chart types.

Important note about styling

The purpose of styling a chart is to make it easier to read and understand. It is important to remember that the chart is a tool to communicate information. If the styling makes it harder to understand the information, then it is not good styling. The examples on this page are not good styling. They have been intentionally over-styled to demonstrate the options available. As a rule of thumb, if you are not sure whether to use a particular styling option, then err on the side of simplicity.

Let’s start with our stock data from plotly express.

import plotly.express as px

df_stocks = px.data.stocks()
df_stocks

	date	GOOG	AAPL	AMZN	FB	NFLX	MSFT
0	2018-01-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2018-01-08	1.018172	1.011943	1.061881	0.959968	1.053526	1.015988
2	2018-01-15	1.032008	1.019771	1.053240	0.970243	1.049860	1.020524
3	2018-01-22	1.066783	0.980057	1.140676	1.016858	1.307681	1.066561
4	2018-01-29	1.008773	0.917143	1.163374	1.018357	1.273537	1.040708
...	...	...	...	...	...	...	...
100	2019-12-02	1.216280	1.546914	1.425061	1.075997	1.463641	1.720717
101	2019-12-09	1.222821	1.572286	1.432660	1.038855	1.421496	1.752239
102	2019-12-16	1.224418	1.596800	1.453455	1.104094	1.604362	1.784896
103	2019-12-23	1.226504	1.656000	1.521226	1.113728	1.567170	1.802472
104	2019-12-30	1.213014	1.678000	1.503360	1.098475	1.540883	1.788185

105 rows × 7 columns

Templates#

Plotly provides several different chart templates by default. We can choose from the following:
plotly, plotly_white, plotly_dark, ggplot2, seaborn, simple_white, none

Documentation:
https://plotly.com/python/templates/

Here we have the standard line chart template.

stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
)
stock_fig

We can change the style by using the template= option in the chart.
For example, the simple_white and plotly_dark templates are shown below.

# simple_white template
stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
	template='simple_white',
)
stock_fig

# plotly_dark template
stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
	template='plotly_dark',
)
stock_fig

Plot size#

We can manually change the size of the chart using the width and height options.

stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y='NFLX',
	width=500,
	height=500,
	template='plotly_dark'
)
stock_fig

Plot margins#

Margins control how much space is between the edge of the chart and the edge of the container. In order to change the margins, we need to use the .update_layout() method and pass a dictionary to the margin option. The dictionary should specify the margin (in pixels) for the top, bottom, left, and right.

Let’s continue using the dark theme since it makes it easier to visualize the edges.

stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
	template='plotly_dark'
)
# setting all 4 margins
stock_fig.update_layout(
	margin=dict(t=50, b=0, l=200, r=300)
)
stock_fig

Titles#

Documentation:
https://plotly.com/python/reference/layout/#layout-title

Now let’s add a title to our stock chart. The easiest way to do this is by using the title= option in the chart — in this example, px.line().

stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
	template='plotly_dark',
	title='The value of a $1 investment',
)
stock_fig

Again using the .update_layout() method, we can edit the title properties.

stock_fig.update_layout(
	# update the title text
	title_text='The Value of $1',

	# change the font color
	title_font_color='dimgray',

	# change the font family
	title_font_family='Arial',

	# change the font size
	title_font_size=24,

	# center the title; can take a value between 0 and 1
	title_x=0.5,

	# sets whether the alignment is relative to the 'paper' or the 'container'
	# in this case, 'paper' centers the title above the chart,
	# while 'container' centers the title in the container.
	# this is most easily seen with a heavy margin on one side
	title_xref='paper',
)
stock_fig

As of April 2022, there is not a convenient way to add a subtitle. Instead you must use an “annotation” work around.

Axes#

Documentation:
https://plotly.com/python/axes/
https://plotly.com/python/reference/layout/xaxis/
https://plotly.com/python/reference/layout/yaxis/

Back to our original starting point:

# the default
stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
)
stock_fig

We can use .update_yaxes() and .update_xaxes() to change a variety of parameters.

# update the Y axis
stock_fig.update_yaxes(
	# put the tick label inside the chart and above the line
	ticklabelposition='inside top',

	# no title
	title=None,

	# put a dollar sign in front of the values
	tickprefix='$',

	# formatted with two decimal points
	tickformat='.2f',

	# manually set the range
	range=[0.5, 2],

	# mark the y-axis with a line
	showline=True,
	linewidth=1,
	linecolor='black',
)

# update the X axis
stock_fig.update_xaxes(
	# new title
	title='Date',

	# title font properties
	title_font={
		'size': 24,
		'color': 'firebrick',
	},

	# tick font properties
	tickfont={
		'size': 16,
		'color': 'darkgrey',
	},

	# the angle of the ticks
	tickangle=15,

	# the date format of the ticks
	tickformat="%b '%y",

	# mark the x-axis with a line
	showline=True,
	linewidth=1,
	linecolor='black',
)

stock_fig

Background color and grid#

Documentation:
https://plotly.com/python/axes/#axis-lines-grid-and-zerolines

# the default
stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
)
stock_fig

# the default
stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
)
# turn off the x axis grid
stock_fig.update_xaxes(
	showgrid=False,
)
stock_fig.update_yaxes(
	# set the width in pixels for the gridlines
	gridwidth=3,
	# set the grid color
	gridcolor='green',
)
# set the background color for the 'paper' and the 'plot'
stock_fig.update_layout(
	paper_bgcolor='lightblue',
	plot_bgcolor='grey',
)
stock_fig

It is also possible to make the backgrounds transparent.

This might be a bad idea, depending on how you plan to use the chart.

# the default
stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL'],
)

# make both bg transparent
# it is the "a" (alpha) in "rgba" that does this
stock_fig.update_layout(
	paper_bgcolor='rgba(0, 0, 0, 0)',
	plot_bgcolor='rgba(0, 0, 0, 0)',
)
stock_fig

Legend#

Documentation:
https://plotly.com/python/legend/
https://plotly.com/python/reference/layout/#layout-legend

You can click on legend items to show/hide items.

stock_fig = px.line(
	data_frame=df_stocks,
	x='date',
	y=['GOOG', 'AAPL', 'FB'],
)
stock_fig.update_layout(
	# no legend title
	legend_title=None,
	# legend items in rows (h) or columns (v)
	legend_orientation='h',
	# reference the center of the legend in x dimension for positioning
	legend_xanchor='center',
	# put the legend in the center in the x dimension
	legend_x=0.5,
	# reference the top of the legend in y dimension for positioning
	legend_yanchor='top',
	# put the legend near the top in the y dimension
	legend_y=0.98,
	# set the background color and border properties
	legend_bgcolor='lightgrey',
	legend_bordercolor='black',
	legend_borderwidth=2,
)
stock_fig

Chart styling

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