update ( layout_title_text = 'Van Gogh: 5 Most Prominent Colors Shown Proportionally', layout_showlegend = False ) fig = go. For further tuning, we call fig.updatetraces to set other parameters of the chart (you can also use fig.update. In the example below, we first create a pie chart with px,pie, using some of its options such as hoverdata (which columns should appear in the hover) or labels (renaming column names). update_traces ( hoverinfo = 'label+percent+name', textinfo = 'none' ) fig. Customizing a pie chart created with px.pie. Pie ( labels = labels, values =, name = 'The Night Café', marker_colors = cafe_colors ), 2, 2 ) # Tune layout and hover info fig. In pandas, you can draw a multiple line chart using a code as follows: df. Pie ( labels = labels, values =, name = 'Irises', marker_colors = irises_colors ), 2, 1 ) fig. Pie ( labels = labels, values =, name = 'Sunflowers', marker_colors = sunflowers_colors ), 1, 2 ) fig. Pie ( labels = labels, values =, name = 'Starry Night', marker_colors = night_colors ), 1, 1 ) fig. Import aph_objects as go from plotly.subplots import make_subplots labels = # Create subplots: use 'domain' type for Pie subplot fig = make_subplots ( rows = 1, cols = 2, specs = ] fig = make_subplots ( rows = 2, cols = 2, specs = specs ) # Define pie charts fig.
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