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Note: Bullet Charts are available in version 2.2.2+
Run pip install plotly --upgrade to update your Plotly version
import plotly
plotly.__version__
'2.2.2'
Stephen Few's Bullet Chart was invented to replace dashboard gauges and meters, combining both types of charts into simple bar charts with qualitative bars (ranges), quantitiative bars (measures) and performance points (markers) all into one simple layout. ranges typically are broken into three values: bad, okay, good, the measures are the darker bars that represent the actual values that a particular variable reached, and the points or markers usually indicate a goal point relative to the value achieved by the measure bar.
To use this figure factory, you can input either a pandas DataFrame as your data, or a sequence (ex. list, tuple, np.array, etc.) of dictionaries. You must map the column to the name of the particular column or key in your data. For example, if you want column A in your DataFrame to be the measures column, your function call will look like:
ff.create_bullet(data, measures='A', ...)
The valid params to set your DataFrame columns or dictionary keys to are titles, subtitles, ranges, measures and markers. The variable for titles and subtitles must contain strings as its elements and the rest lists.
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')
fig = ff.create_bullet(
data, markers='markers', measures='measures',
ranges='ranges', subtitles='subtitle', titles='title',
)
py.iplot(fig, filename='bullet chart from a dataframe')
This example uses a tuple of dictionaries as its data input.
import plotly.plotly as py
import plotly.figure_factory as ff
data = (
{"label": "Revenue", "sublabel": "US$, in thousands",
"range": [150, 225, 300], "performance": [220,270], "point": [250]},
{"label": "Profit", "sublabel": "%", "range": [20, 25, 30],
"performance": [21, 23], "point": [26]},
{"label": "Order Size", "sublabel":"US$, average","range": [350, 500, 600],
"performance": [100,320],"point": [550]},
{"label": "New Customers", "sublabel": "count", "range": [1400, 2000, 2500],
"performance": [1000, 1650],"point": [2100]},
{"label": "Satisfaction", "sublabel": "out of 5","range": [3.5, 4.25, 5],
"performance": [3.2, 4.7], "point": [4.4]}
)
fig = ff.create_bullet(
data, titles='label', subtitles='sublabel', markers='point',
measures='performance', ranges='range', orientation='v',
)
py.iplot(fig, filename='bullet chart from dict')
You can use different colors for the range and the measure columns. Set range_colors and measure_colors to a 2-item list of two colors to interpolate between.
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')
measure_colors=['rgb(63,102,153)', 'rgb(120,194,195)']
range_colors=['rgb(245,225,218)', 'rgb(241,241,241)']
fig = ff.create_bullet(
data, orientation='h', markers='markers', measures='measures',
ranges='ranges', subtitles='subtitle', titles='title',
range_colors=range_colors,
measure_colors=measure_colors
)
py.iplot(fig, filename='bullet chart - custom colors')
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')
fig = ff.create_bullet(
data, orientation='v', markers='markers', measures='measures',
ranges='ranges', subtitles='subtitle', titles='title',
title='lots of kwargs', width=600, showlegend=True,
scatter_options={'marker': {'size': 30,
'color': 'rgb(21, 166, 20)',
'symbol': 'hourglass'}}
)
# group legends
chart_elements = 6 # 3 grey bars, 2 blue bars, 1 marker
for cols, title in enumerate(data['title']):
for ele in range(chart_elements):
if ele == 0:
showlegend = True
else:
showlegend = False
fig['data'][cols * 6 + ele].update(
{
'legendgroup': '{}'.format(title),
'name': title,
'showlegend': showlegend
}
)
py.iplot(fig, filename='bullet chart - custom kwargs')
help(ff.create_bullet)
Help on function create_bullet in module plotly.figure_factory._bullet:
create_bullet(data, markers=None, measures=None, ranges=None, subtitles=None, titles=None, orientation='h', range_colors=('rgb(200, 200, 200)', 'rgb(245, 245, 245)'), measure_colors=('rgb(31, 119, 180)', 'rgb(176, 196, 221)'), horizontal_spacing=None, vertical_spacing=None, scatter_options={'marker': {'color': 'rgb(0, 0, 0)', 'size': 12, 'symbol': 'diamond-tall'}}, **layout_options)
Returns figure for bullet chart.
:param (pd.DataFrame | list | tuple) data: either a list/tuple of
dictionaries or a pandas DataFrame.
:param (str) markers: the column name or dictionary key for the markers in
each subplot.
:param (str) measures: the column name or dictionary key for the measure
bars in each subplot. This bar usually represents the quantitative
measure of performance, usually a list of two values [a, b] and are
the blue bars in the foreground of each subplot by default.
:param (str) ranges: the column name or dictionary key for the qualitative
ranges of performance, usually a 3-item list [bad, okay, good]. They
correspond to the grey bars in the background of each chart.
:param (str) subtitles: the column name or dictionary key for the subtitle
of each subplot chart. The subplots are displayed right underneath
each title.
:param (str) titles: the column name or dictionary key for the main label
of each subplot chart.
:param (bool) orientation: if 'h', the bars are placed horizontally as
rows. If 'v' the bars are placed vertically in the chart.
:param (list) range_colors: a tuple of two colors between which all
the rectangles for the range are drawn. These rectangles are meant to
be qualitative indicators against which the marker and measure bars
are compared.
Default=('rgb(200, 200, 200)', 'rgb(245, 245, 245)')
:param (list) measure_colors: a tuple of two colors which is used to color
the thin quantitative bars in the bullet chart.
Default=('rgb(31, 119, 180)', 'rgb(176, 196, 221)')
:param (float) horizontal_spacing: see the 'horizontal_spacing' param in
plotly.tools.make_subplots. Ranges between 0 and 1.
:param (float) vertical_spacing: see the 'vertical_spacing' param in
plotly.tools.make_subplots. Ranges between 0 and 1.
:param (dict) scatter_options: describes attributes for the scatter trace
in each subplot such as name and marker size. Call
help(plotly.graph_objs.Scatter) for more information on valid params.
:param layout_options: describes attributes for the layout of the figure
such as title, height and width. Call help(plotly.graph_objs.Layout)
for more information on valid params.
Example 1: Use a Dictionary
```
import plotly
import plotly.plotly as py
import plotly.figure_factory as ff
data = [
{"label": "Revenue", "sublabel": "US$, in thousands",
"range": [150, 225, 300], "performance": [220,270], "point": [250]},
{"label": "Profit", "sublabel": "%", "range": [20, 25, 30],
"performance": [21, 23], "point": [26]},
{"label": "Order Size", "sublabel":"US$, average","range": [350, 500, 600],
"performance": [100,320],"point": [550]},
{"label": "New Customers", "sublabel": "count", "range": [1400, 2000, 2500],
"performance": [1000, 1650],"point": [2100]},
{"label": "Satisfaction", "sublabel": "out of 5","range": [3.5, 4.25, 5],
"performance": [3.2, 4.7], "point": [4.4]}
]
fig = ff.create_bullet(
data, titles='label', subtitles='sublabel', markers='point',
measures='performance', ranges='range', orientation='h',
title='my simple bullet chart'
)
py.iplot(fig)
```
Example 2: Use a DataFrame with Custom Colors
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')
fig = ff.create_bullet(
data, titles='title', markers='markers', measures='measures',
orientation='v', measure_colors=['rgb(14, 52, 75)', 'rgb(31, 141, 127)'],
scatter_options={'marker': {'symbol': 'circle'}}, width=700
)
py.iplot(fig)
```
! pip install git+https://github.com/plotly/publisher.git --upgrade
import publisher
publisher.publish(
'bullet-chart.ipynb', 'python/bullet-charts/', 'Bullet Charts',
"How to create Stephen Few Bullet Charts in Python with Plotly.",
title = 'Python Bullet Charts | plotly',
has_thumbnail='true', thumbnail='thumbnail/bullet_charts.jpg',
language='python',
display_as='statistical', order=9)
Collecting git+https://github.com/plotly/publisher.git
Cloning https://github.com/plotly/publisher.git to /private/var/folders/tc/bs9g6vrd36q74m5t8h9cgphh0000gn/T/pip-UhEHKw-build
Installing collected packages: publisher
Found existing installation: publisher 0.11
Uninstalling publisher-0.11:
Successfully uninstalled publisher-0.11
Running setup.py install for publisher ... done
Successfully installed publisher-0.11
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead. /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning: Did you "Save" this notebook before running this command? Remember to save, always save.