Python – Data visualization using Bokeh
Last Updated :
22 May, 2024
Bokeh is a data visualization library in Python that provides high-performance interactive charts and plots. Bokeh output can be obtained in various mediums like notebook, html and server. It is possible to embed bokeh plots in Django and flask apps.
Bokeh provides two visualization interfaces to users:
bokeh.models : A low level interface that provides high flexibility to application developers.
bokeh.plotting : A high level interface for creating visual glyphs.
To install bokeh package, run the following command in the terminal:
pip install bokeh
The dataset used for generating bokeh graphs is collected from Kaggle.
Code #1: Scatter Markers
To create scatter circle markers, circle() method is used.
from bokeh.plotting import figure, output_notebook, show
output_notebook()
p = figure(plot_width = 400, plot_height = 400)
p.circle([1, 2, 3, 4, 5], [4, 7, 1, 6, 3],
size = 10, color = "navy", alpha = 0.5)
show(p)
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Output :
Code #2: Single line
To create a single line, line() method is used.
from bokeh.plotting import figure, output_notebook, show
output_notebook()
p = figure(plot_width = 400, plot_height = 400)
p.line([1, 2, 3, 4, 5], [3, 1, 2, 6, 5],
line_width = 2, color = "green")
show(p)
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Output :
Code #3: Bar Chart
Bar chart presents categorical data with rectangular bars. The length of the bar is proportional to the values that are represented.
import pandas as pd
from bokeh.charts import Bar, output_notebook, show
output_notebook()
df = pd.read_csv(r"D:/kaggle/mcdonald/menu.csv")
p = Bar(df, "Category", values = "Calories",
title = "Total Calories by Category",
legend = "top_right")
show(p)
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Output :
Code #4: Box Plot
Box plot is used to represent statistical data on a plot. It helps to summarize statistical properties of various data groups present in the data.
from bokeh.charts import BoxPlot, output_notebook, show
import pandas as pd
output_notebook()
df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv")
p = BoxPlot(df, values = "Protein", label = "Category",
color = "yellow", title = "Protein Summary (grouped by category)",
legend = "top_right")
show(p)
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Output :
Code #5: Histogram
Histogram is used to represent distribution of numerical data. The height of a rectangle in a histogram is proportional to the frequency of values in a class interval.
from bokeh.charts import Histogram, output_notebook, show
import pandas as pd
output_notebook()
df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv")
p = Histogram(df, values = "Total Fat",
title = "Total Fat Distribution",
color = "navy")
show(p)
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Output :
Code #6: Scatter plot
Scatter plot is used to plot values of two variables in a dataset. It helps to find correlation among the two variables that are selected.
from bokeh.charts import Scatter, output_notebook, show
import pandas as pd
output_notebook()
df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv")
p = Scatter(df, x = "Carbohydrates", y = "Saturated Fat",
title = "Saturated Fat vs Carbohydrates",
xlabel = "Carbohydrates", ylabel = "Saturated Fat",
color = "orange")
show(p)
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Output :
