Bokeh, Plotly, and Tableau, interactive data visualization tools

Bokeh, Plotly, and Tableau are various tools and libraries used for data visualization and are used in a variety of applications as interactive data visualization tools. The following are examples of applications of each of these tools.

1. application examples of Bokeh:

Scientific Data Visualization: Bokeh is widely used in scientific research and engineering projects for data visualization and analysis. It can be used to visualize, for example, weather data, experimental data, and simulation results.
Data Dashboards: Bokeh can be used to build interactive data dashboards. This allows for real-time updating of data and monitoring of trends over time.
Web Applications: Bokeh can be integrated as part of a web application to allow users to interactively manipulate data.

2. Plotly Application Examples:

Business Dashboards: Plotly is well suited for business intelligence applications, helping companies build data dashboards. It is used for sales analysis, KPI visualization, and executive reporting.
Data sharing and collaboration: Plotly also offers a cloud-based platform that is ideal for data analysts and teams to share data and collaborate on visualization projects.
Scientific data visualization: Plotly is also used in the sciences to help visualize data in areas such as molecular modeling, bioinformatics, and astronomy.

3. Tableau Applications:

Business Intelligence: Tableau is an excellent tool for business users and data analysts to provide data insights and is widely used for business intelligence applications such as sales analysis, customer segmentation, and dashboard creation.
Data Briefing and Reporting: Tableau is well suited for visual reporting of data and is used to incorporate data analysis results into reports and presentations.
Geographic Information System (GIS) Data Visualization: Tableau is also powerful for visualizing map data and is used to analyze location data and explore geographic patterns.

Each of these tools is described below.

Bokeh

<Overview>

Bokeh will be a data visualization library based on the Python programming language, designed to create interactive graphs and plots. Bokeh is described below.

1. interactive data visualization:

Bokeh is a tool for creating interactive data visualizations that runs within a web browser, allowing users to interactively manipulate graphs by zooming in and out, panning, clicking, etc.

2. Python-based:

Bokeh is developed in Python and features easy integration with Python programs. It is especially easy to integrate with Jupyter Notebook, making it suitable for data science projects.

3. customizable:

Bokeh is highly customizable, allowing users to create many different types of plots and graphs. It is possible to customize graph styles, colors, axis labels, legends, etc.

4. interactive widgets:

Bokeh also includes the ability to integrate interactive widgets within the web application. Widgets such as sliders, buttons, and checkboxes can be used to refine and filter data.

5. multiple output formats:

Plots created in Bokeh can be exported in a variety of formats, including HTML, PNG, SVG, and JSON. This allows the visualization results to be incorporated into web pages, reports, and documents.

6. community and documentation:

Bokeh has an active open source community that provides extensive documentation and examples. This makes it easy for users to solve problems.

Bokeh can be used for a variety of purposes, including data analysis, data visualization, scientific projects, and dashboard creation, making it a very useful choice as an interactive data visualization tool for Python developers and data scientists. It also allows for interactive exploration and analysis of data using Bokeh’s widget feature.

<How to use Bokeh>

This section describes the basic steps for interactive data visualization using Bokeh. Follow the steps below to get started using Bokeh.

Install Bokeh: To use Bokeh, you must first install the Bokeh library in your Python environment.

pip install bokeh

Basic Bokeh Components: The main components of Bokeh include Plot, Figure, Glyph, and Widget. These components are combined to create visualizations.

Data Preparation: Before visualizing data using Bokeh, the data must be properly prepared. Typically, a data structure is needed to feed data into Bokeh, such as a Pandas data frame or NumPy array.

Creating a Graph: Create a graph using Bokeh. The following is an example of creating a simple scatter plot.

from bokeh.plotting import figure, show
from bokeh.io import output_notebook

# To display in Jupyter Notebook, call output_notebook()
output_notebook()

# Prepare data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 9]

# Create Plot
p = figure(title="Simple Scatter Plot")
p.circle(x, y, size=10, color="blue")

# Show Plot
show(p)

The code creates a figure object and plots data into it. Finally, the plot is displayed using the show() function.

Customization: To customize the graph, the attributes of the figure object can be changed. They allow setting axis labels, titles, legends, colors, and other elements.

Add interactive elements (optional): Bokeh allows the use of widgets (sliders, buttons, checkboxes, etc.) to add user interaction. This allows for dynamic updating of graphs and refinement of data.

The official Bokeh documentation provides a wealth of detailed tutorials and examples to help you effectively utilize Bokeh. It can also be run in environments such as Jupyter Notebook to interactively review the results.

Plotly

<Overview>

Plotly will be an open source library and cloud platform for data visualization that is used to intuitively understand data and create compelling interactive graphs and charts. The following will be key features about Plotly

1. interactive visualization:

Plotly is well suited for creating interactive graphs and plots. Users can interactively manipulate charts by zooming in and out, panning, clicking, etc. This facilitates detailed analysis of data and exploration of results.

2. a wide variety of chart types:

Plotly supports a variety of chart types, allowing users to create line charts, scatter plots, bar charts, heat maps, 3D plots, maps, and more, allowing them to choose the best visualization for the characteristics of their data.

3. available in multiple programming languages:

Plotly can be used with a variety of programming languages, including Python, R, and JavaScript. This allows data analysts and data scientists to use it according to their own preferences and skills.

4. cloud platform:

Plotly also offers a cloud-based platform that allows users to store, share, and collaborate on data in the cloud. It facilitates collaborative analysis and visualization of data.

5. business intelligence and dashboards:

Plotly is also well suited for creating dashboards for business intelligence (BI), used to analyze sales, visualize KPIs, and track critical corporate data metrics.

6. documentation and community:

Plotly provides extensive documentation, tutorials, and sample code to help users use it effectively. There is also an active open source community, solving problems and developing new features.

Plotly is widely used in a variety of fields, including data analysis, data visualization, business intelligence, scientific research, education, and web applications, making Plotly a powerful tool for projects and data analysis situations where interactive visualization is required.

<How to use Plotly>

The following examples are based on Python, although Plotly can be used with Python, R, JavaScript, etc.

Plotly Installation: To use Plotly, you must first install the Plotly library. This can be installed using the following command

pip install plotly

Importing libraries: Import the modules needed to use Plotly.

import plotly.express as px

Data Preparation: Properly prepare the data to be visualized. Data can be Pandas data frames, NumPy arrays, or other data structures.

Creating a Graph: Create a graph using Plotly Express. The following is an example of creating a scatter plot.

import plotly.express as px

# Prepare data
data = px.data.iris()

# Create scatter plots
fig = px.scatter(data, x="sepal_width", y="sepal_length", color="species", title="Iris Data")

# Display Graphs
fig.show()

In the above example, the scatter plot is created using the scatter function of Plotly Express and displayed using the show method. x and y specify the data columns to be used for the x and y axes, and color is used to color-code the data.

Customization: Various options are available to customize the graph. Specifically, title, axis labels, legend, marker style, etc.

fig.update_layout(
    title="Customized Iris Data Scatter Plot",
    xaxis_title="Sepal Width",
    yaxis_title="Sepal Length",
    legend_title="Species",
    markers=dict(size=8, opacity=0.7),
)

Add interactive elements (optional): Plotly is also suitable for adding interactive elements, allowing you to dynamically update your graphs and filter data using sliders and buttons.

Save your graph (optional): You can also export your graph to a file or share it on the web.

By following these steps, you can use Plotly to visualize your data and create interactive graphs; Plotly is a very flexible and powerful data visualization tool that offers a wide variety of chart types and customization options. The official documentation provides a variety of examples and detailed information.

Tableau

<Overview>

Tableau is a data visualization and business intelligence (BI) tool that is used to intuitively understand data and create interactive dashboards and reports. The following are key features of Tableau

1. visual data analysis:

Tableau emphasizes visual data analysis, which is a powerful tool for visually understanding data. Users can visualize data using graphs, charts, and maps to explore trends and patterns.

2. simple interface:

Tableau provides an intuitive drag-and-drop interface that is easy to use, even for users without programming skills. These make it easy to extract, transform, and visualize data.

3. data merging and linking:

Tableau can work with a variety of data sources for data merging, cleansing, and transformation. Data can be integrated from databases, Excel, cloud data storage, and more.

4. interactive dashboards:

Tableau can be used to create interactive dashboards. Users can click, filter, and drill down on graphs to explore data according to their needs.

5. report creation and sharing:

Tableau allows users to create professional reports and dashboards. The content you create can be easily shared and accessed online and on mobile devices.

6. business intelligence

Tableau is particularly well suited for business intelligence applications, helping companies analyze data, track KPIs, and make data-driven decisions. They contribute to business strategy development and performance evaluation.

7. cloud platform:

Tableau offers not only on-premise use, but also a cloud-based platform called Tableau Online, which simplifies data sharing and collaboration.

Tableau is widely used in a variety of industries and is valued as a data analysis and visualization tool in many fields, including business, education, government, and healthcare. In addition, many training resources and community support are available to hone your skills.

<How to use Tableau>

This section describes the basic steps to visualize data and create dashboards and reports using Tableau, which provides an intuitive interface and is easy to use even for users without programming skills.

1. importing data:

Start Tableau and import data. Data can be imported from a variety of formats, including Excel, CSV, databases, and cloud data sources.

2. data integration and merging:

Combine data from multiple data sources or link data together. Data merging allows users to combine and analyze multiple data sets.

3. worksheet creation:

Create worksheets to visualize data. Select the required dimensions (categorical data) and measures (numerical data) from the Data Sources window, and drag and drop them into the worksheet.

4. create a graph:

Create a graph on the worksheet. Select dimensions and measures, choose the graph type (bar, scatter, line, etc.), and intuitively set up the graph.

5. add interactive elements:

Add filters and parameters to create an interactive dashboard. This allows users to select data and have it dynamically reflected within the dashboard.

6. creating dashboards:

Combine worksheets to create dashboards. Dashboards can be populated with graphs, filters, text, etc., to tailor the dashboard and tell the story of the data.

7. interactive dashboard sharing:

Dashboards you create can be saved and shared online; using Tableau Server or Tableau Online, you can share and collaborate on dashboards with other users.

8. report export (optional):

Reports can be exported in PDF, image, Excel, or other formats for incorporation into reports.

Following these steps will enable you to visualize data and create dashboards and reports using Tableau, a flexible and powerful tool that makes data analysis and visualization easy. More information and training resources are available from the official Tableau website and documentation.

Reference Information and Reference Books

For further information, see “User Interface and Data Visualization Technologies.

Refernce book is “Data Visualization: A Practical Introduction“

“Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures“

“Data Visualisation: A Handbook for Data Driven Design“

“Data Visualization: Clear Introduction to Data Visualization with Python. Proper Guide for Data Scientist“

Bokeh.
1. ‘Interactive Data Visualisation with Bokeh’
– Author: Kevin Jolly
– Description: covers the basics and applications of Bokeh, detailing data visualisation techniques using Python.

2.‘Mastering Python Data Visualisation’.
– Author: Kirthi Raman
– Description: Covers a wide range of data visualisation techniques, with examples of applications using Bokeh and integration with other tools.

3. ‘Python Data Science Handbook’.
– Author: Jake VanderPlas
– Description: Covers all aspects of Python data science, including basic usage of Bokeh.

Plotly.
1. ‘Interactive Dashboards and Data Apps with Plotly and Dash’.
– Author: Elias Dabbas
– Description: Focuses on how to create interactive data applications using Dash and also details the use of Plotly.

2. ‘Plotly Dash: User Guide’
– Author: Plotly team (official documentation)
– Description: Learn more about how to get the most out of Dash and Plotly.

3. ‘Data Visualisation with Python and JavaScript’.
– Author: Kyran Dale
– Description: Covers the basics of data visualisation using Python and JavaScript, including Plotly applications.

Tableau
1. ‘The Tableau Workshop’
– Author(s): Sumit Gupta, Jen Stirrup
– Description: An introduction to data visualisation using Tableau, in a practical workshop format.

2. ‘Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software’.
– Author: Daniel G. Murray
– Description: Suitable for beginners and intermediate users, covering the basics and advanced features of Tableau.

3. ‘Learning Tableau’
– Author: Joshua N. Milligan
– Description: Covers everything from basic Tableau operations to advanced techniques. Also covers data storytelling methods.