Horizontal Bar Charts in Python

How to make horizontal bar charts in Python with Plotly.


New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

See more examples of bar charts (including vertical bar charts) and styling options here.

Horizontal Bar Chart with Plotly Express

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on "tidy" data and produces easy-to-style figures. For a horizontal bar char, use the px.bar function with orientation='h'.

Basic Horizontal Bar Chart with Plotly Express

In [1]:
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="total_bill", y="day", orientation='h')
fig.show()

Configure horizontal bar chart

In this example a column is used to color the bars, and we add the information from other columns to the hover data.

In [2]:
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()

Horizontal Bar Chart with go.Bar

When data are not available as a tidy dataframe, you can use the more generic function go.Bar from plotly.graph_objects. All the options of go.Bar are documented in the reference https://plotly.com/python/reference/#bar

Basic Horizontal Bar Chart

In [3]:
import plotly.graph_objects as go

fig = go.Figure(go.Bar(
            x=[20, 14, 23],
            y=['giraffes', 'orangutans', 'monkeys'],
            orientation='h'))

fig.show()

Colored Horizontal Bar Chart

In [4]:
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()

Color Palette for Bar Chart

In [5]:
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()

Bar Chart with Line Plot

In [6]:
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='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()

Reference

See more examples of bar charts and styling options here.
See https://plotly.com/python/reference/#bar for more information and chart attribute options!

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter