Visualizing data is an important steps since it helps understand what is going on in the data without actually looking at the numbers and performing complicated computations.
Bokeh can be easily used in conjunction with NumPy, Pandas, and other Python packages. It can be used to produce interactive plots, dashboards, and so on.
It helps in communicating the quantitative insights to the audience effectively.
Plots can be embedded as output of Flask or Django enabled web applications. Jupyter notebook can also be used to render these plots.
Dependencies of Bokeh −
Numpy Pillow Jinja2 Packaging Pyyaml Six Tornado Python−dateutil
Installation of Bokeh on Windows command prompt
pip3 install bokeh
Installation of Bokeh on Anaconda prompt
conda install bokeh
Let us see how Bokeh can be used to generate line graphs
from bokeh.plotting import figure, output_file, show import numpy as np import math x = np.arange(0, math.pi*4, 0.1) y = np.sin(x) output_file("sample.html") p = figure(title = "A simple sine wave ", x_axis_label = 'x', y_axis_label = 'y') p.line(x, y, legend = "sine", line_width = 2) show(p)
The required packages are imported, and aliased.
The data is generated using NumPy library.
The sine function is defined.
The figure function is called.
The ‘output_file’ function is called to mention the name of the html file that will be generated.
The ‘line’ function present in Bokeh is called.
The ‘show’ function is used to display the plot.