Customizing axes

Interactive Data Visualization with Bokeh

George Boorman

Core Curriculum Manager, DataCamp

A line plot

source = ColumnDataSource(data=apple)
fig = figure(x_axis_label="Date", y_axis_label="Market Cap ($)")
fig.line(x="date", y="market_cap", source=source)

output_file(filename="unformatted_plot.html")
show(fig)

apple_unformatted

X-axis type

source = ColumnDataSource(data=apple)
fig = figure(x_axis_label="Date", y_axis_label="Market Cap ($)",

             x_axis_type="datetime")

fig.line(x="date", y="market_cap", source=source)
output_file(filename="unformatted_plot.html")
show(fig)

x_axis_type_plot

Formatting options

NumeralTickFormatter
Argument: format

Value	format	Output
`1500.00`	`"$0.00"`	`$1500.00`
`2000.00`	`"$0"`	`$2000`
`5000.00`	`"$0,0"`	`$5,000`
`1100000`	`"$0.0a"`	`$1.1m`
`5000000000`	`"$0a`	`$5b`

DatetimeTickFormatter
Argument: months

Value	months	Output
`"2018-03-01"`	`"%B %Y"`	`"March 2018"`
`"2019-10-15"`	`"%b %Y"`	`"Oct 2019"`
`"2020-02-09"`	`"%b %y"`	`"Feb 20"`

Other arguments:
- microseconds, milliseconds, seconds, minisec, minutes, hourmin, hours, days, years

NumeralTickFormatter

from bokeh.models import NumeralTickFormatter

fig = figure(x_axis_label="Date", y_axis_label="Price ($)")
fig.line(x="date", y="market_cap", source=source)

fig.yaxis[0].formatter = NumeralTickFormatter(format="$0.0a")

output_file(filename="formatted_y_axis.html")
show(fig)

apple_formatted_y_axis

DatetimeTickFormatter

from bokeh.models import NumeralTickFormatter, DatetimeTickFormatter

fig = figure(x_axis_label="Date", y_axis_label="Price ($)")
fig.line(x="date", y="price", source=source)
fig.yaxis[0].formatter = NumeralTickFormatter(format="$0.0a")

fig.xaxis[0].formatter = DatetimeTickFormatter(months="%b %Y")

output_file(filename="formatted_market_cap.html")
show(fig)

The final plot

apple_formatted

Let's practice!

Interactive Data Visualization with Bokeh