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See more examples of bar charts (including vertical bar charts) and styling options here.
Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. For a horizontal bar char, use the px.bar function with orientation='h'.
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="total_bill", y="day", orientation='h')
fig.show()In this example a column is used to color the bars, and we add the information from other columns to the hover data.
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="total_bill", y="sex", color='day', orientation='h',
hover_data=["tip", "size"],
height=400,
title='Restaurant bills')
fig.show()You can also use the more generic go.Bar class from plotly.graph_objects. All the options of go.Bar are documented in the reference https://plotly.com/python/reference/bar/
import plotly.graph_objects as go
fig = go.Figure(go.Bar(
x=[20, 14, 23],
y=['giraffes', 'orangutans', 'monkeys'],
orientation='h'))
fig.show()import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Bar(
y=['giraffes', 'orangutans', 'monkeys'],
x=[20, 14, 23],
name='SF Zoo',
orientation='h',
marker=dict(
color='rgba(246, 78, 139, 0.6)',
line=dict(color='rgba(246, 78, 139, 1.0)', width=3)
)
))
fig.add_trace(go.Bar(
y=['giraffes', 'orangutans', 'monkeys'],
x=[12, 18, 29],
name='LA Zoo',
orientation='h',
marker=dict(
color='rgba(58, 71, 80, 0.6)',
line=dict(color='rgba(58, 71, 80, 1.0)', width=3)
)
))
fig.update_layout(barmode='stack')
fig.show()Bar charts with multiple components pose a fundamental trade off between presenting the total clearly and presenting the component values clearly. This small multiples approach shows the component magnitudes clearly at the cost of slightly obscuring the totals. A stacked bar does the opposite. Small multiple bar charts often work better in a horizontal orientation; and are easy to create with the px.bar orientation and facet_col parameters.
.
import pandas as pd
import plotly.express as px
data = {
"Quarter": ["Q1", "Q2", "Q3", "Q4"] * 3,
"Region": ["North", "North", "North", "North", "South", "South", "South", "South", "West", "West", "West", "West"],
"Outcome": [150, 200, 250, 300, 120, 180, 240, 310, 100, 150, 220, 280]
}
df = pd.DataFrame(data)
fig = px.bar(
df,
x="Outcome",
y="Region",
orientation="h",
facet_col="Quarter",
title="Number of Patients Served by Region and Quarter",
labels={"Outcome": "Patients Served", "Region": "Region"}
)
## the section below is optional clean up to make this presentation ready
fig.update_layout(
height=400, #the Plotly default makes the bars awkwardly large; setting a height improves the display
showlegend=False, # the legend does not add anything
)
#remove up the default "facet_variable =" text from the title of each facet graph
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
# Remove duplicate axis labels
fig.for_each_yaxis(lambda axis: axis.update(title=None))
fig.for_each_xaxis(lambda axis: axis.update(title=None))
# add the one valuable axis label back in
fig.update_xaxes(title="Count", row=1, col=1)
fig.show()
import plotly.graph_objects as go
top_labels = ['Strongly<br>agree', 'Agree', 'Neutral', 'Disagree',
'Strongly<br>disagree']
colors = ['rgba(38, 24, 74, 0.8)', 'rgba(71, 58, 131, 0.8)',
'rgba(122, 120, 168, 0.8)', 'rgba(164, 163, 204, 0.85)',
'rgba(190, 192, 213, 1)']
x_data = [[21, 30, 21, 16, 12],
[24, 31, 19, 15, 11],
[27, 26, 23, 11, 13],
[29, 24, 15, 18, 14]]
y_data = ['The course was effectively<br>organized',
'The course developed my<br>abilities and skills ' +
'for<br>the subject', 'The course developed ' +
'my<br>ability to think critically about<br>the subject',
'I would recommend this<br>course to a friend']
fig = go.Figure()
for i in range(0, len(x_data[0])):
for xd, yd in zip(x_data, y_data):
fig.add_trace(go.Bar(
x=[xd[i]], y=[yd],
orientation='h',
marker=dict(
color=colors[i],
line=dict(color='rgb(248, 248, 249)', width=1)
)
))
fig.update_layout(
xaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
zeroline=False,
domain=[0.15, 1]
),
yaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
zeroline=False,
),
barmode='stack',
paper_bgcolor='rgb(248, 248, 255)',
plot_bgcolor='rgb(248, 248, 255)',
margin=dict(l=120, r=10, t=140, b=80),
showlegend=False,
)
annotations = []
for yd, xd in zip(y_data, x_data):
# labeling the y-axis
annotations.append(dict(xref='paper', yref='y',
x=0.14, y=yd,
xanchor='right',
text=str(yd),
font=dict(family='Arial', size=14,
color='rgb(67, 67, 67)'),
showarrow=False, align='right'))
# labeling the first percentage of each bar (x_axis)
annotations.append(dict(xref='x', yref='y',
x=xd[0] / 2, y=yd,
text=str(xd[0]) + '%',
font=dict(family='Arial', size=14,
color='rgb(248, 248, 255)'),
showarrow=False))
# labeling the first Likert scale (on the top)
if yd == y_data[-1]:
annotations.append(dict(xref='x', yref='paper',
x=xd[0] / 2, y=1.1,
text=top_labels[0],
font=dict(family='Arial', size=14,
color='rgb(67, 67, 67)'),
showarrow=False))
space = xd[0]
for i in range(1, len(xd)):
# labeling the rest of percentages for each bar (x_axis)
annotations.append(dict(xref='x', yref='y',
x=space + (xd[i]/2), y=yd,
text=str(xd[i]) + '%',
font=dict(family='Arial', size=14,
color='rgb(248, 248, 255)'),
showarrow=False))
# labeling the Likert scale
if yd == y_data[-1]:
annotations.append(dict(xref='x', yref='paper',
x=space + (xd[i]/2), y=1.1,
text=top_labels[i],
font=dict(family='Arial', size=14,
color='rgb(67, 67, 67)'),
showarrow=False))
space += xd[i]
fig.update_layout(annotations=annotations)
fig.show()import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
y_saving = [1.3586, 2.2623000000000002, 4.9821999999999997, 6.5096999999999996,
7.4812000000000003, 7.5133000000000001, 15.2148, 17.520499999999998
]
y_net_worth = [93453.919999999998, 81666.570000000007, 69889.619999999995,
78381.529999999999, 141395.29999999999, 92969.020000000004,
66090.179999999993, 122379.3]
x = ['Japan', 'United Kingdom', 'Canada', 'Netherlands',
'United States', 'Belgium', 'Sweden', 'Switzerland']
# Creating two subplots
fig = make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_xaxes=True,
shared_yaxes=False, vertical_spacing=0.001)
fig.append_trace(go.Bar(
x=y_saving,
y=x,
marker=dict(
color='rgba(50, 171, 96, 0.6)',
line=dict(
color='rgba(50, 171, 96, 1.0)',
width=1),
),
name='Household savings, percentage of household disposable income',
orientation='h',
), 1, 1)
fig.append_trace(go.Scatter(
x=y_net_worth, y=x,
mode='lines+markers',
line_color='rgb(128, 0, 128)',
name='Household net worth, Million USD/capita',
), 1, 2)
fig.update_layout(
title=dict(text='Household savings & net worth for eight OECD countries'),
yaxis=dict(
showgrid=False,
showline=False,
showticklabels=True,
domain=[0, 0.85],
),
yaxis2=dict(
showgrid=False,
showline=True,
showticklabels=False,
linecolor='rgba(102, 102, 102, 0.8)',
linewidth=2,
domain=[0, 0.85],
),
xaxis=dict(
zeroline=False,
showline=False,
showticklabels=True,
showgrid=True,
domain=[0, 0.42],
),
xaxis2=dict(
zeroline=False,
showline=False,
showticklabels=True,
showgrid=True,
domain=[0.47, 1],
side='top',
dtick=25000,
),
legend=dict(x=0.029, y=1.038, font_size=10),
margin=dict(l=100, r=20, t=70, b=70),
paper_bgcolor='rgb(248, 248, 255)',
plot_bgcolor='rgb(248, 248, 255)',
)
annotations = []
y_s = np.round(y_saving, decimals=2)
y_nw = np.rint(y_net_worth)
# Adding labels
for ydn, yd, xd in zip(y_nw, y_s, x):
# labeling the scatter savings
annotations.append(dict(xref='x2', yref='y2',
y=xd, x=ydn - 20000,
text='{:,}'.format(ydn) + 'M',
font=dict(family='Arial', size=12,
color='rgb(128, 0, 128)'),
showarrow=False))
# labeling the bar net worth
annotations.append(dict(xref='x1', yref='y1',
y=xd, x=yd + 3,
text=str(yd) + '%',
font=dict(family='Arial', size=12,
color='rgb(50, 171, 96)'),
showarrow=False))
# Source
annotations.append(dict(xref='paper', yref='paper',
x=-0.2, y=-0.109,
text='OECD "' +
'(2015), Household savings (indicator), ' +
'Household net worth (indicator). doi: ' +
'10.1787/cfc6f499-en (Accessed on 05 June 2015)',
font=dict(family='Arial', size=10, color='rgb(150,150,150)'),
showarrow=False))
fig.update_layout(annotations=annotations)
fig.show()See more examples of bar charts and styling options here.
See https://plotly.com/python/reference/bar/ for more information and chart attribute options!