Interactive dashboards and data apps with Plotly and Dash harness the power of a fully fledged frontend web framework in Python - no JavaScript required
Build web-based, mobile-friendly analytic apps and interactive dashboards with PythonKey FeaturesDevelop data apps and dashboards without any knowledge of JavaScriptMap different types of data such as integers, floats, and dates to bar charts, scatter plots, and moreCreate controls and visual elemen...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai :
Packt
[2021]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631712406719 |
Tabla de Contenidos:
- Cover
- Copyright
- Contributors
- Table of Contents
- Preface
- Section 1: Building a Dash App
- Chapter 1: Overview of the Dash Ecosystem
- Technical requirements
- Setting up your environment
- Exploring Dash and other supporting packages
- The different packages that Dash contains
- Understanding the general structure of a Dash app
- Creating and running the simplest app
- Adding HTML and other components to the app
- Adding HTML components to a Dash app
- Learning how to structure the layout and managing themes
- Themes
- Grid system and responsiveness
- Prebuilt components
- Encoded colors
- Adding Dash Bootstrap components to our app
- Summary
- Chapter 2: Exploring the Structure of a Dash App
- Technical requirements
- Using Jupyter Notebooks to run Dash apps
- Isolating functionality for better management and debugging
- Creating a standalone pure Python function
- The id parameter of Dash components
- Dash inputs and outputs
- Determining your inputs and outputs
- Specifying your callback function
- Implementing the callback
- Incorporating the function into the app
- Properties of Dash's callback functions
- Summary
- Chapter 3: Working with Plotly's Figure Objects
- Technical requirements
- Understanding the Figure object
- Getting to know the data attribute
- Getting to know the layout attribute
- Interactively exploring the Figure object
- Configuration options for the Figure object
- Exploring the different ways of converting figures
- Converting figures into HTML
- Converting figures into images
- Plotting using a real dataset
- Data manipulation as an essential part of the data visualization process
- Making the chart interactive with a callback function
- Adding the new functionality to our app
- Theming your figures
- Summary.
- Chapter 4: Data Manipulation and Preparation, Paving the Way to Plotly Express
- Technical requirements
- Understanding long format (tidy) data
- Plotly Express example chart
- Main attributes of long format (tidy) data
- Understanding the role of data manipulation skills
- Exploring the data files
- Melting DataFrames
- Pivoting DataFrames
- Merging DataFrames
- Learning Plotly Express
- Plotly Express and Figure objects
- Creating a Plotly Express chart using the dataset
- Adding new data and columns to our dataset
- Summary
- Section 2: Adding Functionality to Your App with Real Data
- Chapter 5: Interactively Comparing Values with Bar Charts and Dropdown Menus
- Technical requirements
- Plotting bar charts vertically and horizontally
- Creating vertical bar charts with many values
- Linking bar charts and dropdowns
- Exploring different ways of displaying multiple bar charts (stacked, grouped, overlaid, and relative)
- Creating the income share DataFrame
- Incorporating the functionality into our app
- Using facets to split charts into multiple sub-charts - horizontally, vertically, or wrapped
- Exploring additional features of dropdowns
- Adding placeholder text to dropdowns
- Modifying the app's theme
- Resizing components
- Summary
- Chapter 6: Exploring Variables with Scatter Plots and Filtering Subsets with Sliders
- Technical requirements
- Learning about the different ways of using scatter plots: markers, lines, and text
- Markers, lines, and text
- Creating multiple scatter traces in a single plot
- Mapping and setting colors with scatter plots
- Discrete and continuous variables
- Using color with continuous variables
- Manually creating color scales
- Using color with discrete variables
- Handling over-plotting and outlier values by managing opacity, symbols, and scales.
- Controlling the opacity and size of markers
- Using logarithmic scales
- Introducing sliders and range sliders
- Customizing the marks and values of sliders
- Summary
- Chapter 7: Exploring Map Plots and Enriching Your Dashboards with Markdown
- Technical requirements
- Exploring choropleth maps
- Utilizing animation frames to add a new layer to your plots
- Using callback functions with maps
- Creating a Markdown component
- Understanding map projections
- Using scatter map plots
- Exploring Mapbox maps
- Exploring other map options and tools
- Incorporating an interactive map into our app
- Summary
- Chapter 8: Calculating the Frequency of Your Data with Histograms and Building Interactive Tables
- Technical requirements
- Creating a histogram
- Customizing the histogram by modifying its bins and using multiple histograms
- Using color to further split the data
- Exploring other ways of displaying multiple bars in histograms
- Adding interactivity to histograms
- Creating a 2D histogram
- Creating a DataTable
- Controlling the look and feel of the table (cell width, height, text display, and more)
- Adding histograms and tables to the app
- Summary
- What we have covered so far
- Section 3: Taking Your App to the Next Level
- Chapter 9: Letting Your Data Speak for Itself with Machine Learning
- Technical requirements
- Understanding clustering
- Finding the optimal number of clusters
- Clustering countries by population
- Preparing data with scikit-learn
- Handling missing values
- Scaling data with scikit-learn
- Creating an interactive KMeans clustering app
- Summary
- Chapter 10: Turbo-charge Your Apps with Advanced Callbacks
- Technical requirements
- Understanding State
- Understanding the difference between Input and State
- Creating components that control other components.
- Allowing users to add dynamic components to the app
- Introducing pattern-matching callbacks
- Summary
- Chapter 11: URLs and Multi-Page Apps
- Technical requirements
- Getting to know the Location and Link components
- Getting to know the Link component
- Parsing URLs and using their components to modify parts of the app
- Restructuring your app to cater to multiple layouts
- Displaying content based on the URL
- Adding dynamically generated URLs to the app
- Incorporating the new URL interactivity into the app
- Summary
- Chapter 12: Deploying Your App
- Technical requirements
- Establishing the general development, deployment, and update workflow
- Creating a hosting account and virtual server
- Connecting to your server with SSH
- Running the app on the server
- Setting up and running the app with a WSGI
- Setting up and configuring the web server
- Managing maintenance and updates
- Fixing bugs and making changes
- Updating Python packages
- Maintaining your server
- Deploying and scaling Dash apps with Dash Enterprise
- Initializing the app
- Building your application (optional)
- Preparing your project folder
- Deploying your application on Dash Enterprise
- Summary
- Chapter 13: Next Steps
- Technical requirements
- Expanding your data manipulation and preparation skills
- Exploring more data visualization techniques
- Exploring other Dash components
- Creating your own Dash component
- Operationalizing and visualizing machine learning models
- Enhancing performance and using big data tools
- Going large scale with Dash Enterprise
- Dash Design Kit
- App Manager
- Snapshot Engine
- Better performance with Job Queue
- Corporate security
- Consulting services
- Summary
- Other Books You May Enjoy
- Index.
