Bar chart with Plotly Express¶

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.

With px.bar, each row of the DataFrame is represented as a rectangular mark.

In [1]:

import plotly.express as px
data_canada = px.data.gapminder().query("country == 'Canada'")
fig = px.bar(data_canada, x='year', y='pop')
fig.show()

Bar chart with Long Format Data¶

Long-form data has one row per observation, and one column per variable. This is suitable for storing and displaying multivariate data i.e. with dimension greater than 2. This format is sometimes called "tidy".

To learn more about how to provide a specific form of column-oriented data to 2D-Cartesian Plotly Express functions such as px.bar, see the Plotly Express Wide-Form Support in Python documentation.

For detailed column-input-format documentation, see the Plotly Express Arguments documentation.

In [2]:

import plotly.express as px

long_df = px.data.medals_long()

fig = px.bar(long_df, x="nation", y="count", color="medal", title="Long-Form Input")
fig.show()

In [3]:

long_df

Out[3]:

	nation	medal	count
0	South Korea	gold	24
1	China	gold	10
2	Canada	gold	9
3	South Korea	silver	13
4	China	silver	15
5	Canada	silver	12
6	South Korea	bronze	11
7	China	bronze	8
8	Canada	bronze	12

Bar chart with Wide Format Data¶

Wide-form data has one row per value of one of the first variable, and one column per value of the second variable. This is suitable for storing and displaying 2-dimensional data.

In [4]:

import plotly.express as px

wide_df = px.data.medals_wide()

fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input")
fig.show()

In [5]:

wide_df

Out[5]:

	nation	gold	silver	bronze
0	South Korea	24	13	11
1	China	10	15	8
2	Canada	9	12	12

Customize bar chart with Plotly Express¶

The bar plot can be customized using keyword arguments.

In [6]:

import plotly.express as px
data = px.data.gapminder()

data_canada = data[data.country == 'Canada']
fig = px.bar(data_canada, x='year', y='pop',
             hover_data=['lifeExp', 'gdpPercap'], color='lifeExp',
             labels={'pop':'population of Canada'}, height=400)
fig.show()

When several rows share the same value of x (here Female or Male), the rectangles are stacked on top of one another by default.

In [7]:

import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color='time')
fig.show()

In [8]:

# Change the default stacking
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill",
             color='smoker', barmode='group',
             height=400)
fig.show()

Facetted subplots¶

Use the keyword arguments facet_row (resp. facet_col) to create facetted subplots, where different rows (resp. columns) correspond to different values of the dataframe column specified in facet_row.

In [9]:

import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color="smoker", barmode="group",
             facet_row="time", facet_col="day",
             category_orders={"day": ["Thur", "Fri", "Sat", "Sun"],
                              "time": ["Lunch", "Dinner"]})
fig.show()

To learn more, see the link to px.bar reference page.

Basic Bar Chart with plotly.graph_objects¶

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Bar class from plotly.graph_objects.

In [10]:

import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys']

fig = go.Figure([go.Bar(x=animals, y=[20, 14, 23])])
fig.show()

Grouped Bar Chart¶

Customize the figure using fig.update.

In [11]:

import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys']

fig = go.Figure(data=[
    go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),
    go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()

Stacked Bar Chart¶

In [12]:

import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys']

fig = go.Figure(data=[
    go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),
    go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])
])
# Change the bar mode
fig.update_layout(barmode='stack')
fig.show()

Bar Chart with Hover Text¶

In [13]:

import plotly.graph_objects as go

x = ['Product A', 'Product B', 'Product C']
y = [20, 14, 23]

# Use the hovertext kw argument for hover text
fig = go.Figure(data=[go.Bar(x=x, y=y,
            hovertext=['27% market share', '24% market share', '19% market share'])])
# Customize aspect
fig.update_traces(marker_color='rgb(158,202,225)', marker_line_color='rgb(8,48,107)',
                  marker_line_width=1.5, opacity=0.6)
fig.update_layout(title_text='January 2013 Sales Report')
fig.show()

Bar Chart with Direct Labels¶

In [14]:

import plotly.graph_objects as go

x = ['Product A', 'Product B', 'Product C']
y = [20, 14, 23]

# Use textposition='auto' for direct text
fig = go.Figure(data=[go.Bar(
            x=x, y=y,
            text=y,
            textposition='auto',
        )])

fig.show()

Controlling text fontsize with uniformtext¶

If you want all the text labels to have the same size, you can use the uniformtext layout parameter. The minsize attribute sets the font size, and the mode attribute sets what happens for labels which cannot fit with the desired fontsize: either hide them or show them with overflow. In the example below we also force the text to be outside of bars with textposition.

In [15]:

import plotly.express as px

df = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
fig = px.bar(df, y='pop', x='country', text='pop')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()

Rotated Bar Chart Labels¶

In [16]:

import plotly.graph_objects as go

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

fig = go.Figure()
fig.add_trace(go.Bar(
    x=months,
    y=[20, 14, 25, 16, 18, 22, 19, 15, 12, 16, 14, 17],
    name='Primary Product',
    marker_color='indianred'
))
fig.add_trace(go.Bar(
    x=months,
    y=[19, 14, 22, 14, 16, 19, 15, 14, 10, 12, 12, 16],
    name='Secondary Product',
    marker_color='lightsalmon'
))

# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()

Customizing Individual Bar Colors¶

In [17]:

import plotly.graph_objects as go

colors = ['lightslategray',] * 5
colors[1] = 'crimson'

fig = go.Figure(data=[go.Bar(
    x=['Feature A', 'Feature B', 'Feature C',
       'Feature D', 'Feature E'],
    y=[20, 14, 23, 25, 22],
    marker_color=colors # marker color can be a single color value or an iterable
)])
fig.update_layout(title_text='Least Used Feature')

Customizing Individual Bar Widths¶

In [18]:

import plotly.graph_objects as go

fig = go.Figure(data=[go.Bar(
    x=[1, 2, 3, 5.5, 10],
    y=[10, 8, 6, 4, 2],
    width=[0.8, 0.8, 0.8, 3.5, 4] # customize width here
)])

fig.show()

Bar charts with custom widths can be used to make mekko charts (also known as marimekko charts, mosaic plots, or variwide charts).

In [19]:

import plotly.graph_objects as go
import numpy as np

labels = ["apples","oranges","pears","bananas"]
widths = np.array([10,20,20,50])

data = {
    "South": [50,80,60,70],
    "North": [50,20,40,30]
}

fig = go.Figure()
for key in data:
    fig.add_trace(go.Bar(
        name=key,
        y=data[key],
        x=np.cumsum(widths)-widths,
        width=widths,
        offset=0,
        customdata=np.transpose([labels, widths*data[key]]),
        texttemplate="%{y} x %{width} =<br>%{customdata[1]}",
        textposition="inside",
        textangle=0,
        textfont_color="white",
        hovertemplate="<br>".join([
            "label: %{customdata[0]}",
            "width: %{width}",
            "height: %{y}",
            "area: %{customdata[1]}",
        ])
    ))

fig.update_xaxes( 
    tickvals=np.cumsum(widths)-widths/2, 
    ticktext= ["%s<br>%d" % (l, w) for l, w in zip(labels, widths)]
)

fig.update_xaxes(range=[0,100])
fig.update_yaxes(range=[0,100])

fig.update_layout(
    title_text="Marimekko Chart",
    barmode="stack",
    uniformtext=dict(mode="hide", minsize=10),
)

Customizing Individual Bar Base¶

In [20]:

import plotly.graph_objects as go

years = ['2016','2017','2018']

fig = go.Figure()
fig.add_trace(go.Bar(x=years, y=[500, 600, 700],
                base=[-500,-600,-700],
                marker_color='crimson',
                name='expenses'))
fig.add_trace(go.Bar(x=years, y=[300, 400, 700],
                base=0,
                marker_color='lightslategrey',
                name='revenue'
                ))

fig.show()

Colored and Styled Bar Chart¶

In this example several parameters of the layout as customized, hence it is convenient to use directly the go.Layout(...) constructor instead of calling fig.update.

In [21]:

import plotly.graph_objects as go

years = [1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
         2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012]

fig = go.Figure()
fig.add_trace(go.Bar(x=years,
                y=[219, 146, 112, 127, 124, 180, 236, 207, 236, 263,
                   350, 430, 474, 526, 488, 537, 500, 439],
                name='Rest of world',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=years,
                y=[16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270,
                   299, 340, 403, 549, 499],
                name='China',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='US Export of Plastic Scrap',
    xaxis_tickfont_size=14,
    yaxis=dict(
        title='USD (millions)',
        titlefont_size=16,
        tickfont_size=14,
    ),
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

Bar Chart with Relative Barmode¶

With "relative" barmode, the bars are stacked on top of one another, with negative values below the axis, positive values above.

In [22]:

import plotly.graph_objects as go
x = [1, 2, 3, 4]

fig = go.Figure()
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16]))
fig.add_trace(go.Bar(x=x, y=[6, -8, -4.5, 8]))
fig.add_trace(go.Bar(x=x, y=[-15, -3, 4.5, -8]))
fig.add_trace(go.Bar(x=x, y=[-1, 3, -3, -4]))

fig.update_layout(barmode='relative', title_text='Relative Barmode')
fig.show()

Bar Chart with Sorted or Ordered Categories¶

Set categoryorder to "category ascending" or "category descending" for the alphanumerical order of the category names or "total ascending" or "total descending" for numerical order of values. categoryorder for more information. Note that sorting the bars by a particular trace isn't possible right now - it's only possible to sort by the total values. Of course, you can always sort your data before plotting it if you need more customization.

This example orders the bar chart alphabetically with categoryorder: 'category ascending'

In [23]:

import plotly.graph_objects as go

x=['b', 'a', 'c', 'd']
fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))
fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))

fig.update_layout(barmode='stack', xaxis={'categoryorder':'category ascending'})
fig.show()

This example shows how to customise sort ordering by defining categoryorder to "array" to derive the ordering from the attribute categoryarray.

In [24]:

import plotly.graph_objects as go

x=['b', 'a', 'c', 'd']
fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))
fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))

fig.update_layout(barmode='stack', xaxis={'categoryorder':'array', 'categoryarray':['d','a','c','b']})
fig.show()

This example orders the bar chart by descending value with categoryorder: 'total descending'

In [25]:

import plotly.graph_objects as go

x=['b', 'a', 'c', 'd']
fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))
fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))
fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))

fig.update_layout(barmode='stack', xaxis={'categoryorder':'total descending'})
fig.show()

Horizontal Bar Charts¶

See examples of horizontal bar charts here.

Bar Charts With Multicategory Axis Type¶

If your traces have arrays for x or y, then the axis type is automatically inferred to be multicategory.

In [26]:

import plotly.graph_objects as go
x = [
    ["BB+", "BB+", "BB+", "BB", "BB", "BB"],
    [16, 17, 18, 16, 17, 18,]
]
fig = go.Figure()
fig.add_bar(x=x,y=[1,2,3,4,5,6])
fig.add_bar(x=x,y=[6,5,4,3,2,1])
fig.update_layout(barmode="relative")
fig.show()

Reference¶

See function reference for px.bar() or 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