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Pratik Pathak
Pratik Pathak

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25+ Python Bokeh Example. Learn Bokeh From Examples

#programming #python #bokehapppython #bokehdata

Python Bokeh is one of the best Python packages for data visualization. Today we are going to see some Python Bokeh Examples. I have also provided the Python Bokeh project source code GitHub. Learn this easy visualization tool and add it to your Python stack.

What is Python Bokeh?

Python Bokeh is a data visualization tool or we can also say Python Bokeh is used to plot various types of graphs. There are various other graph plotting libraries like matplotlib but Python Bokeh graphs are dynamic in nature means you can interact with the generated graph. See the below examples…

Installation 💻:

Python Bokeh can be easily installed using PIP. You can install the Python Bokeh easily by running the command:

pip install bokeh
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Now everything is ready let’s go through the examples 🏃‍♂️…

1. LinePlot 

from bokeh.plotting import figure, show, output_notebook x = [1, 2, 3, 4, 5] y = [4, 5, 5, 7, 3] p = figure(title="{LinePlot Python Bokeh Example") p.line(x, y, line_width=2) output_notebook() show(p)
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2. Scatter Plot 

from bokeh.plotting import figure, show, output_notebook x = [1, 2, 3, 4, 5] y = [4, 5, 5, 7, 3] p = figure(title="Scatter Plot Python Bokeh Example by PratikPathak.com") p.circle(x, y, size=10, color="navy", alpha=0.5) output_notebook() show(p)
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3. Bar Chart 

from bokeh.plotting import figure, show, output_notebook categories = ["A", "B", "C", "D", "E"] values = [10, 15, 8, 12, 6] p = figure(x_range=categories, title="Bar Chart Python Bokeh Example by PratikPathak.com") p.vbar(x=categories, top=values, width=0.9) output_notebook() show(p)
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4. Histogram 

from bokeh.plotting import figure, show, output_notebook import numpy as np data = np.random.normal(0, 1, 1000) p = figure(title="Histogram Python Bokeh Example by PratikPathak.com") p.hist(data, bins=30, color="navy", alpha=0.5) output_notebook() show(p)
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5. Pie Chart 

from bokeh.plotting import figure, show, output_notebook labels = ["A", "B", "C", "D"] values = [10, 15, 8, 12] p = figure(title="Pie Chart Python Bokeh Example by PratikPathak.com") p.wedge(x=0, y=0, radius=0.4, start_angle=0.6, end_angle=2.6, color=["red", "green", "blue", "yellow"], legend_label=labels) output_notebook() show(p)
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6. Time Series Plot 

from bokeh.plotting import figure, show, output_notebook from datetime import datetime, timedelta start = datetime(2023, 1, 1) end = start + timedelta(days=30) x = [start + timedelta(days=i) for i in range((end-start).days)] y = [10, 15, 8, 12, 6, 18, 9, 14, 7, 11, 5, 16, 8, 13, 6] p = figure(x_axis_type="datetime", title="Time Series Plot Python Bokeh Example by PratikPathak.com") p.line(x, y, line_width=2) output_notebook() show(p)
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7. Linked Brushing 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import ColumnDataSource x1 = [1, 2, 3, 4, 5] y1 = [4, 5, 5, 7, 3] x2 = [2, 3, 4, 5, 6] y2 = [2, 4, 6, 8, 4] source = ColumnDataSource(data=dict(x1=x1, y1=y1, x2=x2, y2=y2)) p1 = figure(title="Scatter Plot 1") p1.circle('x1', 'y1', source=source) p2 = figure(title="Scatter Plot 2 Python Bokeh Example by PratikPathak.com") p2.circle('x2', 'y2', source=source) output_notebook() show(p1, p2)
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8. Hover Tooltips 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import HoverTool x = [1, 2, 3, 4, 5] y = [4, 5, 5, 7, 3] p = figure(title="Hover Tooltips Python Bokeh Example by PratikPathak.com") p.circle(x, y, size=15, fill_color="navy", line_color="white", alpha=0.5) hover = HoverTool(tooltips=[("(x,y)", "(@x, @y)")]) p.add_tools(hover) output_notebook() show(p)
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9. Annotations 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import Arrow, VectorRenderer, Label x = [1, 2, 3, 4, 5] y = [4, 5, 5, 7, 3] p = figure(title="Annotations Python Bokeh Example by PratikPathak.com") p.circle(x, y, size=15, fill_color="navy", line_color="white", alpha=0.5) arrow = Arrow(x_start=2, y_start=4, x_end=3, y_end=5, line_width=2, line_color="red") label = Label(x=3, y=6, text="This is a label", render_mode='css', border_line_color='black', border_line_alpha=1.0, background_fill_color='white', background_fill_alpha=0.5) p.add_layout(arrow) p.add_layout(label) output_notebook() show(p)
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10. Custom Glyphs 

from bokeh.plotting import figure, show, output_notebook from bokeh.models.glyphs import Asterisk x = [1, 2, 3, 4, 5] y = [4, 5, 5, 7, 3] p = figure(title="Custom Glyphs Python Bokeh Example by PratikPathak.com") p.add_glyph(x, y, Asterisk(size=20, line_color="red", fill_color="yellow")) output_notebook() show(p)
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11. Gridlines and Axes 

from bokeh.plotting import figure, show, output_notebook x = [1, 2, 3, 4, 5] y = [4, 5, 5, 7, 3] p = figure(title="Gridlines and Axes Python Bokeh Example by PratikPathak.com", x_range=(0, 6), y_range=(0, 8)) p.grid.grid_line_color = "grey" p.grid.grid_line_dash = [6, 4] p.xaxis.axis_label = "X-axis" p.yaxis.axis_label = "Y-axis" p.line(x, y, line_width=2) output_notebook() show(p)
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12. Legend 

from bokeh.plotting import figure, show, output_notebook x1 = [1, 2, 3, 4, 5] y1 = [4, 5, 5, 7, 3] x2 = [2, 3, 4, 5, 6] y2 = [2, 4, 6, 8, 4] p = figure(title="Legend Python Bokeh Example by PratikPathak.com") p.line(x1, y1, line_width=2, color="red", legend_label="Line 1") p.line(x2, y2, line_width=2, color="blue", legend_label="Line 2") p.legend.location = "top_left" output_notebook() show(p)
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13. Categorical Plots 

from bokeh.plotting import figure, show, output_notebook fruits = ["Apples", "Pears", "Nectarines", "Plums", "Grapes", "Strawberries"] counts = [5, 3, 4, 2, 4, 6] p = figure(x_range=fruits, title="Categorical Plots Python Bokeh Example by PratikPathak.com") p.vbar(x=fruits, top=counts, width=0.9) p.xaxis.axis_label = "Fruit" p.yaxis.axis_label = "Count" p.xaxis.axis_label_text_font_size = "12pt" p.yaxis.axis_label_text_font_size = "12pt" output_notebook() show(p)
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14. Subplots 

from bokeh.plotting import figure, show, output_notebook from bokeh.layouts import gridplot x1 = [1, 2, 3, 4, 5] y1 = [4, 5, 5, 7, 3] x2 = [2, 3, 4, 5, 6] y2 = [2, 4, 6, 8, 4] p1 = figure(title="Scatter Plot 1 Python Bokeh Example by PratikPathak.com") p1.circle(x1, y1, size=10, color="navy", alpha=0.5) p2 = figure(title="Scatter Plot 2 Python Bokeh Example by PratikPathak.com") p2.circle(x2, y2, size=10, color="red", alpha=0.5) grid = gridplot([[p1, p2]], plot_width=400, plot_height=400) output_notebook() show(grid)
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15. Interactive Plots 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import ColumnDataSource, HoverTool, BoxSelectTool import numpy as np x = np.random.random(1000) y = np.random.random(1000) source = ColumnDataSource(data=dict(x=x, y=y)) p = figure(title="Interactive Plots Python Bokeh Example by PratikPathak.com", tools="hover,box_select") p.circle('x', 'y', source=source, size=3, color="navy", alpha=0.5) hover = HoverTool(tooltips=[("(x,y)", "(@x, @y)")]) p.add_tools(hover) output_notebook() show(p)
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16. Linked Panning and Zooming 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import Range1d x1 = [1, 2, 3, 4, 5] y1 = [4, 5, 5, 7, 3] x2 = [2, 3, 4, 5, 6] y2 = [2, 4, 6, 8, 4] p1 = figure(title="Scatter Plot 1 Python Bokeh Example by PratikPathak.com", x_range=Range1d(0, 6), y_range=Range1d(0, 8)) p1.circle(x1, y1, size=10, color="navy", alpha=0.5) p2 = figure(title="Scatter Plot 2 Python Bokeh Example by PratikPathak.com", x_range=p1.x_range, y_range=p1.y_range) p2.circle(x2, y2, size=10, color="red", alpha=0.5) output_notebook() show(p1, p2)
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17. Linked Axis Ranges 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import Range1d x1 = [1, 2, 3, 4, 5] y1 = [4, 5, 5, 7, 3] x2 = [2, 3, 4, 5, 6] y2 = [2, 4, 6, 8, 4] p1 = figure(title="Scatter Plot 1", x_range=(0, 6), y_range=(0, 8)) p1.circle(x1, y1, size=10, color="navy", alpha=0.5) p2 = figure(title="Scatter Plot 2", x_range=p1.x_range, y_range=p1.y_range) p2.circle(x2, y2, size=10, color="red", alpha=0.5) output_notebook() show(p1, p2)
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18. Datetime Axis 

from bokeh.plotting import figure, show, output_notebook from datetime import datetime, timedelta start = datetime(2023, 1, 1) end = start + timedelta(days=30) x = [start + timedelta(days=i) for i in range((end-start).days)] y = [10, 15, 8, 12, 6, 18, 9, 14, 7, 11, 5, 16, 8, 13, 6] p = figure(x_axis_type="datetime", title="Datetime Axis Python Bokeh Example by PratikPathak.com") p.line(x, y, line_width=2) output_notebook() show(p)
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19. Heatmap 

from bokeh.plotting import figure, show, output_notebook import numpy as np data = np.random.rand(10, 10) p = figure(title="Heatmap Python Bokeh Example by PratikPathak.com") p.image(image=[data], x=0, y=0, dw=10, dh=10, palette="Viridis256") output_notebook() show(p)
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20. Streamgraph 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import ColumnDataSource import numpy as np # Generate some sample data x = np.linspace(0, 10, 100) y1 = np.cumsum(np.random.randn(100)) y2 = np.cumsum(np.random.randn(100)) y3 = np.cumsum(np.random.randn(100)) y4 = np.cumsum(np.random.randn(100)) y5 = np.cumsum(np.random.randn(100)) source = ColumnDataSource(data=dict(x=x, y1=y1, y2=y2, y3=y3, y4=y4, y5=y5)) p = figure(title="Streamgraph Python Bokeh Example by PratikPathak.com") p.area(x='x', y1='y1', y2='y2', y3='y3', y4='y4', y5='y5', source=source, line_color=None) output_notebook() show(p)
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21. Polar Plot 

from bokeh.plotting import figure, show, output_notebook import numpy as np r = np.linspace(0.1, 1, 10) theta = np.linspace(0, 2*np.pi, 10) p = figure(title="Polar Plot Python Bokeh Example by PratikPathak.com", plot_width=600, plot_height=600, x_range=(-1.2, 1.2), y_range=(-1.2, 1.2), toolbar_location=None) p.polar(r, theta, color="navy", alpha=0.5, line_width=2) output_notebook() show(p)
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22. Donut Chart 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import ColumnDataSource labels = ["A", "B", "C", "D"] values = [10, 15, 8, 12] source = ColumnDataSource(data=dict(labels=labels, values=values)) p = figure(title="Donut Chart Python Bokeh Example by PratikPathak.com", x_range=labels) p.wedge(x=0, y=1, radius=0.4, start_angle=0.6, end_angle=2.6, color=["red", "green", "blue", "yellow"], legend_field="labels") p.wedge(x=0, y=1, inner_radius=0.3, outer_radius=0.4, start_angle=0.6, end_angle=2.6, color="white") p.axis.visible = False p.grid.grid_line_color = None output_notebook() show(p)
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23. Treemap 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import TreemapRenderer, ColumnDataSource import pandas as pd # Sample data data = pd.DataFrame({ 'label': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'], 'parent': ['', 'A', 'A', 'B', 'B', 'C', 'C', 'D'], 'value': [10, 5, 4, 3, 2, 1, 1, 1] }) source = ColumnDataSource(data) p = figure(title="Treemap Python Bokeh Example by PratikPathak.com") treemap = TreemapRenderer( source=source, label='label', parent='parent', value='value', color_discrete_sequence=["#e6550d", "#3182bd", "#31a354", "#756bb1", "#636363", "#9e9ac8", "#de2d26", "#fd8d3c"], ) p.add_layout(treemap) output_notebook() show(p)
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If you get any error make sure you have installed python pandas by using “pip install pandas” command

24. Network Plot 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import ColumnDataSource, HoverTool, NodesAndLinkedEdges import networkx as nx # Sample data nodes = ['A', 'B', 'C', 'D', 'E', 'F'] edges = [('A', 'B'), ('A', 'C'), ('B', 'D'), ('B', 'E'), ('C', 'F')] G = nx.Graph() G.add_nodes_from(nodes) G.add_edges_from(edges) node_positions = nx.spring_layout(G) node_x = [node_positions[node][0] for node in G.nodes()] node_y = [node_positions[node][1] for node in G.nodes()] node_color = ['red', 'green', 'blue', 'yellow', 'purple', 'orange'] source = ColumnDataSource(data=dict( x=node_x, y=node_y, color=node_color, node=nodes )) p = figure(title="Network Plot Python Bokeh Example by PratikPathak.com", x_range=(-1.1, 1.1), y_range=(-1.1, 1.1)) p.add_tools(HoverTool(tooltips=[("Node", "@node")])) p.circle('x', 'y', size=15, color='color', source=source) edge_x = [] edge_y = [] for start, end in edges: x0, y0 = node_positions[start] x1, y1 = node_positions[end] edge_x.append(x0) edge_x.append(x1) edge_x.append(None) edge_y.append(y0) edge_y.append(y1) edge_y.append(None) p.multi_line(edge_x, edge_y, color="black", alpha=0.5, line_width=1) output_notebook() show(p)
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25. Sankey Diagram 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import Sankey, ColumnDataSource # Sample data flows = [ ('A', 'X', 5), ('B', 'X', 3), ('A', 'Y', 2), ('B', 'Y', 4), ('X', 'C', 4), ('X', 'D', 3), ('Y', 'C', 2), ('Y', 'D', 4) ] source = ColumnDataSource(data=dict( source=[flow[0] for flow in flows], target=[flow[1] for flow in flows], value=[flow[2] for flow in flows] )) p = figure(plot_width=800, plot_height=400, title="Sankey Diagram Python Bokeh Example by PratikPathak.com") sankey = Sankey(source=source, label_text_color="white", label_text_font_size="8pt") p.renderers.append(sankey) output_notebook() show(p)
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26. Chord Diagram 

from bokeh.plotting import figure, show, output_notebook from bokeh.models import ColumnDataSource, CategoricalColorMapper import numpy as np # Sample data nodes = ['A', 'B', 'C', 'D', 'E', 'F'] connections = [ ('A', 'B', 0.5), ('A', 'C', 0.3), ('A', 'D', 0.2), ('B', 'C', 0.4), ('B', 'D', 0.3), ('B', 'E', 0.3), ('C', 'D', 0.2), ('C', 'E', 0.3), ('C', 'F', 0.5), ('D', 'E', 0.2), ('D', 'F', 0.3), ('E', 'F', 0.4) ] source = ColumnDataSource(data=dict( start=np.array([con[0] for con in connections]), end=np.array([con[1] for con in connections]), value=np.array([con[2] for con in connections]) )) p = figure(title="Chord Diagram Python Bokeh Example by PratikPathak.com", plot_width=800, plot_height=800) chord = p.chord( source=source, start_angle=np.pi / 2, direction="clockwise", colors=CategoricalColorMapper(factors=nodes, palette="Spectral11") ) output_notebook() show(p)
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How to Contribute?

Feel free to open a PR request on our GitHub repo.

Steps to contribute:

  1. Fork the repo
  2. Make changes in the Forked repo and save
  3. Open a Pull Request
  4. That’s it 😄!

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Conclusion

In this article I have shared you 25+ Python Bokeh examples which can help you to learn python bokeh. Feel free to contribute to our github repo and keep it updated.

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