In chapter 2, we discussed why the pie chart is easy to be messed up. Here is another example. When the observations are more than 5, the pie chart will become hard to read or deliver information. You should be very careful before using it.
df = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
df.loc[df['pop'] < 10.e6, 'country'] = 'Other countries' # Represent only large countries
fig = px.pie(df, values='pop', names='country', title='Population of Asia continent',width=800, height=400)
fig.show()
Let's start with a simple donut chart.
import plotly.graph_objects as go
labels = ['Apple','Banana','Kiwi','Grape']
values = [3000, 5000, 2000, 2500]
# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()
This donut chart is not sweet enough, we can add some customization to make it look better. For example,
- change the color set
- make the font size bigger
update_traces - make the donut hole bigger
hole - cut a slice out
pull - put the legend closer to the donut
update_layout
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5, pull=[0, 0.1, 0, 0])])
colors = ['gold', 'dodgerblue', 'tomato', 'lightgreen']
fig.update_traces(hoverinfo='label+percent', textinfo='value',
textfont_size=16,marker=dict(colors=colors))
fig.update_layout(legend=dict(orientation="h",yanchor="bottom",y=1.02,
xanchor="right",x=0.7))
fig.show()
Sunburst plots visualize hierarchical data spanning outwards radially from root to leaves. It looks fancy and stylish, however, it could be even messer than a pie chart. Be careful!
fig = px.sunburst(df, path=['continent', 'country'], values='pop',
color='lifeExp', hover_data=['iso_alpha'])
fig.show()
