Data Visualization in Python using Bokeh Library

Bokeh is a Python library which is used for data visualization through high-performance interactive charts and plots. It creates its plots using HTML and JavaScript languages. The output of the bokeh library can be generated on several platforms such as browser, HTML, server, and notebook. It is also possible to create the bokeh plots in Django and flask applications.

The bokeh library provides two visualization interfaces to the users:

models: it is a low-level interface which provides high flexibility to the application developers.
plotting: it is a high-level interface which is used for creating visual glyphs.

In this tutorial, we will learn how to create different types of data visualization graphs and charts using the bokeh library in Python.

Installation:

To install the bokeh library, we can use the following command:

<br />
!pip3 install bokeh<br />

Code 1: To Create Scatter Circle Markers

We can create Scatter Circle Markers on the Plot by using the bokeh library. For this, we will use the circle() function.

Example:

<br />
# First, we will import the required modules<br />
from bokeh.plotting import figure as fig<br />
from bokeh.plotting import output_notebook as ON<br />
from bokeh.plotting import show</p>
<p># output to the notebook<br />ON()</p>
<p># creating figure<br />plot1 = fig(plot_width = 500, plot_height = 500, title = “Scatter Markers”)</p>
<p># adding a circle renderer with size, color and alpha<br />plot1.circle([1, 2, 3, 4, 5, 6], [2, 1, 6, 8, 0],<br />         size = 12, color = “green”, alpha = 1)</p>
<p># show the results<br />show(plot1)<br />

Output:

Data Visualization in Python using Bokeh Library

Code 2: To Create a Single Line

We can create a single line by using the bokeh library in Python. For this, we will use the line() function.

Example:

<br />
# First, we will import the required modules<br />
from bokeh.plotting import figure as fig<br />
from bokeh.plotting import output_notebook as ON<br />
from bokeh.plotting import show</p>
<p># output to the notebook<br />ON()</p>
<p># creating figure<br />plot1 = fig(plot_width = 500, plot_height = 500, title = “Scatter Markers”)</p>
<p># adding a line renderer<br />plot1.line([1, 2, 3, 4, 5, 6], [2, 1, 6, 8, 0],<br />        line_width = 4, color = “red”)</p>
<p># show the results<br />show(plot1)<br />

Output:

Data Visualization in Python using Bokeh Library

Code 3: To Create a Bar chart

Bar charts are used for representing the categorical data with rectangular bars. The length of the bar is proportional to the values they are representing.

Example: Vertical Bar Chart

<br />
# First, we will import the required modules<br />
from bokeh.plotting import figure as fig<br />
from bokeh.plotting import output_file as OF<br />
from bokeh.plotting import show</p>
<p># file to save the model<br />output_file(“jtp.html”)</p>
<p># Now, instantiate the figure object<br />graph1 = fig(title = “Bokeh Bar Graph”)</p>
<p># x-coordinates to be plotted<br />x = [1, 2, 3, 4, 5, 6, 7, 8]</p>
<p># y-coordinates of the top edges<br />top1 = [5, 1, 4, 3, 2, 7, 6, 8]</p>
<p># width / thickness of the bars<br />width1 = 0.7</p>
<p># plot the graph<br />graph1.vbar(x,<br />           top = top1,<br />           width = width1,<br />           color = “green”)</p>
<p># display the model<br />show(graph1)<br />

Output:

Data Visualization in Python using Bokeh Library

Example 2: Horizontal Bar Chart

<br />
# First, we will import the required modules<br />
from bokeh.plotting import figure as fig<br />
from bokeh.plotting import output_file as OF<br />
from bokeh.plotting import show</p>
<p># file to save the model<br />output_file(“jtp.html”)</p>
<p># Now, instantiate the figure object<br />graph1 = fig(title = “Bokeh Bar Graph”)</p>
<p># x-coordinates to be plotted<br />x = [1, 2, 3, 4, 5, 6, 7, 8]</p>
<p># y-coordinates of the top edges<br />right1 = [5, 1, 4, 3, 2, 7, 6, 8]</p>
<p># width / thickness of the bars<br />height1 = 0.7</p>
<p># plot the graph<br />graph1.hbar(x,<br />           right = right1,<br />           height = height1,<br />           color = “green”)</p>
<p># display the model<br />show(graph1)<br />

Output:

Data Visualization in Python using Bokeh Library

Code 4: To Create Box Plot

Box plot is used for representing the statistical data on the plots, and it is helpful for summarizing the statistical properties of several groups present in the dataset.

Example:

<br />
import numpy as nmp<br />
import pandas as pnd</p>
<p>from bokeh.plotting import figure as fig<br />from bokeh.plotting import show</p>
<p># generate some time series for six different categories<br />cats_1 = list(“xyzprq”)<br />y_y = nmp.random.randn(2000)<br />g_1 = nmp.random.choice(cats_1, 2000)<br />for K, J in enumerate(cats_1):<br />    y_y[g_1 == l] += K // 2<br />data_frame = pnd.DataFrame(dict(score = y_y, group = g_1))</p>
<p># now, we will find the quartiles and IQR for each category<br />groups1 = data_frame.groupby(‘group’)<br />q_1 = groups1.quantile(q = 0.25)<br />q_2 = groups1.quantile(q = 0.5)<br />q_3 = groups1.quantile(q = 0.75)<br />iqr = q3 – q1<br />upper = q3 + 1.5 * iqr<br />lower = q1 – 1.5 * iqr</p>
<p># now we will find the outliers for each category<br />def outliers(group2):<br />    cat_2 = group2.name<br />    return group2[(group2.score > upper.loc[cat_2][‘score’]) | (group2.score < lower.loc[cat_2]['score'])]['score']out = groups.apply(outliers).dropna()# here, we will prepare outlier data to plot, we would be needing coordinates for every outlier.if not out.empty:    out_x = list(out.index.get_level_values(0))    out_y = list(out.values)plot1 = fig(tools = "", background_fill_color = "#efefef", x_range = cats_1, toolbar_location = None)# if no outliers, we will shrink the lengths of stems to be no longer than the minimums or maximumsq_min = groups1.quantile(q = 0.00)q_max = groups1.quantile(q = 1.00)upper.score = [min([x,y]) for (x,y) in zip(list(q_max.loc[:, 'score']), upper.score)]lower.score = [max([x,y]) for (x,y) in zip(list(q_min.loc[:, 'score']), lower.score)]# stemsplot1.segment(cats_1, upper.score, cats_1, q_3.score, line_color = "black")plot1.segment(cats_1, lower.score, cats_1, q_1.score, line_color = "black")# boxesplot1.vbar(cats_1, 0.7, q_2.score, q_3.score, fill_color = "#E08E79", line_color = "black")plot1.vbar(cats_1, 0.7, q_1.score, q_2.score, fill_color = "#3B8686", line_color = "black")# whiskers (almost-0 height rects simpler than segments)plot1.rect(cats_1, lower.score, 0.2, 0.01, line_color = "black")plot1.rect(cats_1, upper.score, 0.2, 0.01, line_color = "black")# outliersif not out.empty:    plot1.circle(outx, outy, size = 6, color = "#F38630", fill_alpha = 0.6)plot1.xgrid.grid_line_color = Noneplot1.ygrid.grid_line_color = "white"plot1.grid.grid_line_width = 2plot1.xaxis.major_label_text_font_size = "16px"show(plot1)

Output:

Data Visualization in Python using Bokeh Library

Code 5: To Create Scatter Plot

A Scatter plot is used for plotting values of two variables in a dataset. It is helpful in finding the correlation between the two variables which are selected.

Example:

<br />
# First, we will import the required modules<br />
from bokeh.plotting import figure as fig<br />
from bokeh.plotting import output_file as OF<br />
from bokeh.plotting import show</p>
<p># file for saving the model<br />OF(“jtp.html”)</p>
<p># instantiate the figure object<br />graph1 = fig(title = “Bokeh Scatter Graph”)</p>
<p># the points to be plotted on Scatter Plot<br />x1 = [1.4, 5.1, 5.9, 2.3, 5.6, 8.6, 4.5, 2.1, 3.1, 4.3, 5.5, 4.4, 6.9, 2.1, 4, 5.2, 6.3, 7.2, 7.9, 2]<br />y1 = [3.4, 2.1, 5.7, 8.5, 4.3, 4.2, 5.7, 6.5, 8.9, 9.1, 1.5, 2.1, 6.8, 1, 6, 5.2, 4.5, 7.4, 7.5, 6.3]</p>
<p># plott the graph<br />graph1.scatter(x1, y1)</p>
<p># display the model<br />show(graph1)<br />

Output:

Data Visualization in Python using Bokeh Library

Conclusion

In this tutorial, we have discussed various types of data visualization, namely Bar chart, Box plot, Scatter Markers, Scatter Plots, and Single lines, which can be created using the bokeh library in Python.

原创文章，作者：ItWorker，如若转载，请注明出处：https://blog.ytso.com/263328.html

Data Visualization in Python using Bokeh Library

Data Visualization in Python using Bokeh Library

Installation:

Code 1: To Create Scatter Circle Markers

Code 2: To Create a Single Line

Code 3: To Create a Bar chart

Code 4: To Create Box Plot

Code 5: To Create Scatter Plot

Conclusion

相关推荐

发表回复