Plotly is a free and open-source graphing library for Python. It makes interactive, publication-quality graphs online. With Plotly, you can create and customize your own graphs, as well as style and share your graphs with others.
Audience
This tutorial will provide a comprehensive overview of the Plotly library, which is a data visualization tool that can be used to create interactive charts, graphs, and maps. We will discuss all the features and capabilities of Plotly, as well as how to get started using it. Additionally, we will learn how to customize and manipulate your visualizations, and how to share them with others. Finally, we will explore some of the more advanced topics in Plotly, such as creating custom animations and integrating your visualizations with web applications. By the end of this tutorial, you will be comfortable creating and customizing your own Plotly visualizations.
Prerequisites
In this tutorial, we will be covering the basics of Plotly (also known as Plotly.js). We will be covering the following topics:
1. What is Plotly?
2. How to install Plotly
3. What are the different types of plotly graphs?
4. Creating a basic Plotly graph
5. Customizing Plotly graphs
6. Plotly’s advanced features
7. Plotly best practices
Plotly – Introduction
Plotly is an online data visualization platform that enables users to create interactive and engaging visualizations. It is designed to make complex data exploration and analysis accessible for everyone. Plotly provides an extensive library of chart types, including line, scatter, histogram, bar, pie, box, and bubble charts. It also offers a variety of tools for data exploration and analysis, such as brushing, filtering, and range selectors. Additionally, Plotly offers a range of features to customize the look and feel of visualizations, including fonts, colors, and other styling options. With Plotly, users can quickly create powerful data visualizations that help them understand and communicate their data in an effective way.
Plotly – Environment Setup
1) Install Plotly
Plotly can be installed using either Python’s pip package manager or Conda package manager. To install with pip, open a terminal window and type “pip install plotly”. To install with Conda, open a terminal window and type “conda install -c plotly plotly”.
2) Set Up an Account
To use Plotly, you must first set up an account. This can be done by visiting the Plotly homepage and clicking the “Create Account” button. You will then be asked to provide your name, email address, and password. Once you have completed the signup process, you will receive a confirmation email.
3) Install the Plotly Python Library
Once you have created your account, you can install the Plotly Python library by typing “pip install plotly” in a terminal window. Once the installation is completed, you will be able to access the Plotly library in your Python code.
4) Generate an API Key
Plotly requires you to generate an API key in order to use its services. This can be done by logging into your Plotly account and navigating to the “API Keys” tab. From there, click the “Generate New Key” button. This will generate a unique API key that must be used when making requests to the Plotly API.
5) Start Using Plotly
Once you have set up your Plotly account and installed the Plotly Python library, you are ready to start using Plotly. To do this, you will need to import the Plotly library into your Python code and use the API key you generated in Step 4. From there, you can start making calls to the Plotly API and creating beautiful interactive visualizations.
Plotly – Online and Offline Plotting
Plotly is a plotting library for both online and offline data visualization. It is available in both open source and commercial versions. It is used for creating interactive and publication-quality graphs and visualizations for web, desktop and mobile platforms. It supports various types of charts, including bar charts, scatter plots, line graphs, histograms, box plots, 3D plots, and more. It can be used to create interactive graphs, dashboards, and data visualizations. It also supports integration with other popular tools such as R, Python, MATLAB, and JavaScript.
Settings for online plotting
Plotly offers a variety of settings for online plotting, including the ability to customize the look of the plot, the type of plot, the data source, the data range, and the formatting of the axes.
Customizing the Look – Plotly allows you to customize the look of your plot by selecting a theme, choosing colors, and changing the size and shape of the plot.
Plot Type – You can select the type of plot you want to create, such as a line chart, bar chart, scatterplot, or histogram.
Data Source – You can select the data source you want to use, such as a CSV file, a URL, or a data frame.
Data Range – You can select the data range you want to use for your plot, such as all data points or a specific range.
Axis Formatting – You can format the axes of your plot, such as the font size, tick marks, and labels.
Plotly – Plotting Inline with Jupyter Notebook
Plotly is an open source library for creating interactive, publication-quality graphs and charts. It is used in a wide range of applications including data visualizations, scientific graphing libraries, and web-based graphing applications. Plotly is also highly integrated with Jupyter Notebook, allowing users to create interactive, publication-quality graphs and charts directly within the notebook environment.
Using Plotly with Jupyter Notebook, users can create interactive graphs and charts that can be embedded in other web pages. Plotly also offers a variety of customizations, including layout, style, and colors. Additionally, it is possible to export the graphs and charts created in Plotly into a variety of formats, including PDF, SVG, and PNG.
Plotly also makes it easy to share graphs and charts with other users, as well as embed them in other websites. With Plotly, users can also collaborate on graphs and charts by sharing the data and creating visualizations together.
Overall, Plotly is an easy-to-use and powerful plotting library that is well-integrated with Jupyter Notebook. It is a great tool for creating interactive graphs and charts that can be used in various applications.
Plotly – Package Structure
The Plotly package is composed of several distinct components. The top-level structure includes the Plotly JavaScript library, the Plotly Python client, and the Plotly Web Service.
1. Plotly JavaScript Library: The Plotly JavaScript library is a powerful library for creating interactive visualizations in web browsers. It includes functions for creating charts, heatmaps, histograms, and more.
2. Plotly Python Client: The Plotly Python Client is a library for creating interactive visualizations in Python. It provides wrappers for the Plotly JavaScript library and allows you to use Plotly tools directly from Python.
3. Plotly Web Service: The Plotly Web Service is a cloud-based platform for creating, sharing, and collaborating on interactive visualizations. It allows you to publish and share your visualizations with others.
Plotly – Exporting to Static Images
Plotly is a powerful data visualization library that can be used to create all kinds of charts, graphs, and maps. It also includes features that allow users to export their graphs to static images. This can be useful for sharing data with colleagues or clients who don’t have access to Plotly. Exporting to a static image also ensures that the image won’t change when the data is updated, making it a great way to share data in a consistent format.
To export a Plotly graph to a static image, select the “Download” button in the upper right-hand corner of the graph. Then, select the format that you would like to export the graph to. The options include PNG, SVG, and PDF. After selecting the format, click “Download” to save the image to your computer. You can then share the image with others or use it in presentations or documents.
Plotly also makes it easy to customize the look and feel of your graphs by changing the colors, fonts, and other graphical elements. To make these changes, select the “Edit Plot” button in the upper right-hand corner of the graph. This will open up the Plotly Editor where you can customize the graph to your liking. Once you are happy with the changes you have made, click “Save” to apply the changes and export the graph as a static image.
Plotly – Legends
Legends in Plotly are a way to label and describe elements of a graph. Legends are typically used to provide information about what each color or symbol on a graph represents. They can also be used to explain different parts of a chart, such as the axis labels or chart title. Legends can be customized to show only the information that is relevant to the graph and to make the graph easier to understand.
Plotly – Format Axis and Ticks
Formatting the axis and ticks in Plotly is quite simple. To format the axis, you can use the ‘layout’ object. To format the ticks, you can use the ‘tickformat’ property of the ‘xaxis’ or ‘yaxis’ object.
The ‘layout’ object contains properties such as ‘title’, ‘font’, ‘showlegend’, and ‘xaxis’ and ‘yaxis’ objects. The ‘xaxis’ and ‘yaxis’ objects contain the ‘tickformat’ property, which can be used to format the ticks.
To format the axis, you can set the ‘title’ property to the desired text, and the ‘font’ property to the desired font size. You can also set the ‘showlegend’ property to ‘True’ or ‘False’ to show or hide the legend.
To format the ticks, you can set the ‘tickformat’ property to the desired format. For example, you can set it to ‘%b %d’ to display the month and day, or ‘%H:%M:%S’ to display the hour, minute, and second. You can also set the ‘tickmode’ property to ‘linear’, ‘log’, or ‘date’ to set the type of tick mode.
Plotly – Subplots and Inset Plots
Plotly is an open-source graphing library for creating interactive, publication-quality graphs and charts. It is widely used for creating subplots and inset plots. Subplots are used to compare multiple sets of data or to display different views of a single set of data. Inset plots are used to display additional data in a smaller plot within the larger plot. Plotly makes it easy to create both types of plots using its intuitive API and interactive user interface.
Making Subplots
Plotly is a powerful data visualization tool that allows users to easily create interactive graphs and charts. It also features a library of subplots, which allow users to easily add multiple graphs to their main graph. Creating a subplot in Plotly is easy and straightforward.
To create a subplot in Plotly, first create your main graph. This can be done by selecting a graph type, such as a histogram, line graph, or scatter plot, from the Plotly menu. Once you’ve chosen a graph type, you can add data points by clicking and dragging them onto the graph.
Next, select ‘Add Subplot’ from the graph options menu. From here, you can choose which type of graph you want to add as a subplot. You can also customize the graph’s style and color, as well as the x and y axes. Finally, click ‘Create Plot’ to complete the process.
Once you’ve created your subplot, you can add more data points, customize the style, and adjust the axes. You can also change the color and appearance of the subplot by selecting different themes or adding a background image. Finally, you can move, resize, and delete subplots as needed.
Creating subplots in Plotly is a great way to compare multiple datasets and explore relationships between them. With its easy-to-use interface, Plotly makes it easy to quickly create and customize subplots for any visualization project.
Inset Plots
Inset plots are smaller plots inserted into a main plot. They are used to show additional information, such as zoomed in details, to supplement the main plot. In Plotly, inset plots can be created using the subplot() function. The subplot() function takes in the number of rows, columns, and the plot number of the inset plot. It then returns a subplot object which can be used to add traces and modify the inset plot.
Plotly – Bar Chart and Pie Chart
Bar chart:
A bar chart is a type of chart that displays the values of different categories of data using bars of different heights. It is used to compare and contrast different types of data. The length of the bars indicate the magnitude of the values they represent.
Pie chart:
A pie chart is a type of chart that displays data using a circle divided into slices. Each slice represents a category of data and the size of the slice indicates the relative size of the category compared to other categories. Pie charts are used to compare and contrast different types of data.
Scatter Plot, Scattergl Plot and Bubble Charts
Scatter plot: A scatter plot is a type of data visualization or graph that uses dots or symbols to represent the values of two different variables. It is used to show the relationship between two variables, such as the correlation between one variable and another.
Scattergl plot: A scattergl plot is an interactive scatter plot that uses WebGL technology to render the plot in the browser. It allows users to zoom in on specific areas of the plot and interact with the data points to gain insight into the data.
Bubble chart: A bubble chart is a type of chart that uses circles to represent data points. Each circle represents a data point, with the size of the circle representing the magnitude of the data point. Bubble charts are often used to show relationships between different variables, such as the relationship between price and demand.
Plotly – Dot Plots and Table
Dot Plots
Dot plots are used to quickly visualize the distribution of data. They show the frequency of a specific data point or range of data points. The dots can be arranged in a variety of ways, including in a bar chart or in a line graph, to help understand the distribution of the data.
Table
Table plots are used to quickly visualize the data in tabular form. They can show the frequency of a specific data point, or range of data points, across multiple categories or variables. Tables are useful for quickly understanding the relationships between different variables and for helping to identify patterns or trends in the data.
Plotly – Histogram
Plotly is a data visualization library for creating interactive histograms. Histograms are used to display the distribution of a dataset by visualizing the data points as bars. Plotly allows users to customize their histograms in a variety of ways, such as adjusting the bin size, changing the color, adding labels, and more. Plotly also provides features such as hover text and annotations to help users understand their data better.
Plotly – Box Plot Violin Plot and Contour Plot
Box Plots: A box plot is a graphical representation of statistical data that uses quartiles to display the distribution of a dataset. It consists of a rectangular box that is divided into four equal parts, with a line drawn through the middle of the box. The four parts of the box represent the lower quartile, the first quartile, the median, the third quartile and the upper quartile.
Violin Plots: A violin plot is a graphical representation of data that uses a combination of a box plot and a kernel density estimation to visualize the distribution of data. It is similar to a box plot in that it shows the quartiles of a dataset, but it also shows the probability density of the data at different values.
Contour Plots: A contour plot is a graphical representation of three-dimensional data in a two-dimensional graph. It shows the relationships among three variables by plotting contours of constant value, or isolines, for each variable. The contours are usually colored and labeled so that the values of the variables are easily identified.
Plotly – Distplots Density Plot and Error Bar Plot
Plotly is a powerful data visualization library that can be used to create a wide variety of graphs, including density plots and error bar plots. Density plots are a great way to visualize the distribution of data, while error bar plots show the variability of data from one point to the next.
Density plots are created by plotting the probability density of a given data set on a two-dimensional chart. The horizontal axis represents the value of the data points and the vertical axis represents their probability. For example, if a data set contains values from 1 to 10, then the probability density of the data set would be represented as a graph with the x-axis ranging from 1 to 10, and the y-axis ranging from 0 to 1.
Error bar plots are used to show the variability of data from one point to the next. The x-axis represents the data points and the y-axis represents the variability. A positive error bar indicates that the data is higher than the average, while a negative error bar indicates that the data is lower than the average. Error bars are often used to show the variation in data from one experiment to the next.
Plotly makes it easy to create both density plots and error bar plots. All you need to do is select the type of graph you want to create, upload your data, and then customize the graph with your preferred settings. You can also add labels, annotations, and other features to the graph to make it more informative.
Plotly – Heatmap
A heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. Heatmaps can be used to visualize a variety of different types of data, such as correlation matrices or clustering results. Plotly’s Heatmap is a type of plot that allows users to visualize data in a two-dimensional graph with color-coded data points. It is a powerful tool for quickly exploring and understanding the relationships between variables in a dataset.
Plotly – Polar Chart and Radar Chart
Plotly is a data visualization library that provides high quality interactive charts. It includes several types of charts, including polar charts and radar charts.
A polar chart is a type of chart where data points are plotted in relation to a center point or origin. The data points are then connected by lines or curves to form a polygon. Polar charts are often used to compare two or more variables in relation to each other.
A radar chart is a type of chart that displays data points in relation to a fixed center point. Data points are connected by lines and/or curves to form a polygon. Radar charts are often used to compare multiple variables in relation to each other.
OHLC Chart, Waterfall Chart and Funnel Chart
1. OHLC Chart: An OHLC chart (open-high-low-close chart) is a type of chart typically used to illustrate movements in the price of a financial instrument over time. It is a combination of a line-chart and a bar-chart, in that each bar represents the range of price movement over a given time interval.
2. Waterfall Chart: A waterfall chart is a type of chart that is used to visualize a series of values, typically over a period of time, in order to show the cumulative effect of each increment or decrement. This type of chart is also referred to as a cascade chart or a bridge chart.
3. Funnel Chart: A funnel chart is a type of chart used to visualize data in a funnel-like format. It is used to compare different stages of a process, such as sales, marketing, and customer support, to visualize where resources are being spent and how each stage contributes to overall performance.
Plotly – 3D Scatter and Surface Plot
Plotly is a powerful and interactive 3D plotting library that can be used to create a variety of 3D plots, such as 3D scatter plots, 3D surface plots, and 3D mesh plots. This library is great for creating interactive visualizations, such as 3D scatter and surface plots.
3D scatter plots are useful for visualizing the relationship between three variables. The 3D scatter plot allows us to easily view the relationship between all three variables at once. It also allows us to compare the relationship between different subsets of the data.
3D surface plots are useful for visualizing the relationship between two variables. The 3D surface plot allows us to easily view the relationship between two variables, while also allowing us to compare the relationship between different subsets of the data.
Plotly’s 3D plotting library is easy to use and provides a powerful and interactive way to explore and visualize data in 3D. It also allows us to create interactive visuals that can be shared and embedded into websites and applications.
Plotly – Adding Buttons Dropdown
The following code is an example of how to add a button dropdown to a Plotly chart.
// Create a new trace
var trace1 = {
x: [1,2,3],
y: [2,4,6],
type: ‘scatter’
};
// Create a layout containing the button dropdown
var layout = {
title: ‘Plotly Example’,
xaxis: {title: ‘X’},
yaxis: {title: ‘Y’},
updatemenus: [{
x: 0.1,
y: 0.5,
xanchor: ‘left’,
yanchor: ‘top’,
showactive: false,
direction: ‘left’,
type: ‘buttons’,
pad: {t: 87, r: 10},
buttons: [{
method: ‘restyle’,
args: [‘visible’, [true, false, true]],
label: ‘Show First Trace’
}, {
method: ‘restyle’,
args: [‘visible’, [false, true, false]],
label: ‘Show Second Trace’
}, {
method: ‘restyle’,
args: [‘visible’, [true, true, true]],
label: ‘Show Both Traces’
}]
}]
};
// Create the data array containing the traces
var data = [trace1];
// Render the chart
Plotly.newPlot(‘myDiv’, data, layout);
Plotly – Slider Control
The Plotly Slider control is a powerful tool for creating interactive, dynamic charts and graphs with a wide range of data. It allows users to customize their graphs by manipulating the data points or changing the scale or range of the graph. The Slider Control also lets users draw shapes and curves directly on the graph to make visual comparisons and observations. With the Slider Control, users can quickly make changes to the chart and easily compare different data sets side by side.
Plotly – FigureWidget Class
The Plotly FigureWidget is a class that is used to create interactive, web-based visualizations and plots with the Plotly library. It is designed to be used with the Jupyter Notebook and is used to create interactive, web-based plots that can be shared and embedded into other websites. The FigureWidget class takes data as input and creates a Plotly figure that can be manipulated and customized with various settings. The FigureWidget class is highly customizable and can be used to create a wide variety of plots, including line, bar, scatter, and pie charts. It also supports a variety of different plotly.js features such as zooming, panning, and hovering.
Plotly with Pandas and Cufflinks
Plotly with Pandas and Cufflinks is a powerful combination of tools that allow you to create interactive, web-based plots and graphs from your data. With this combination, you can quickly and easily create stunning visualizations from your data that can be shared with colleagues, friends, and family. Plotly with Pandas and Cufflinks provides a wide range of plotting capabilities, from basic scatter plots to more complex 3D plots. It is also simple to use and doesn’t require any coding experience. With this combination, you can quickly explore your data, identify patterns, and gain insights that would otherwise be difficult to find.
Pandas dataframes from databases
Pandas dataframes can be created from databases by using the read_sql_table or read_sql_query methods. The read_sql_table method is used to read an entire table from a database, while the read_sql_query method is used to read data from a database using an SQL query. Both of these methods require a connection to the database to be established first. Once the connection has been established, the methods can be used to create a pandas dataframe from the database.
Plotly with Matplotlib and Chart Studio
Plotly is a plotting library that enables users to create interactive and publication-quality plots, graphs, and charts using both Matplotlib and Chart Studio. It is fully integrated with both of these platforms and provides users with a wide variety of features and options to create stunning visuals. Additionally, Plotly is capable of producing 3D plots, which can be used to gain insights into data sets that may not be easily visible in traditional 2D plots.
Matplotlib
Matplotlib can be used in Plotly by using the Plotly Express library. This library provides a simple interface for creating data visualizations using Matplotlib. It provides a high-level interface for creating figures and axes, as well as a wide range of chart types and customization options. Additionally, it includes tools for manipulating and transforming data to create interactive plots and maps.
Chart Studio
Plotly’s Chart Studio is a web-based data visualization platform that allows users to create, collaborate, and share interactive data visualizations. It provides a wide variety of chart types for users to choose from, including bar and line plots, scatter plots, geographic maps, and more. Additionally, Chart Studio offers a variety of data manipulation and formatting tools, such as filtering, aggregation, sorting, and joining. Users can also access datasets from publicly available sources, like the World Bank and US Census Bureau, or upload their own data. Chart Studio works with both online and offline data sources, and its user-friendly interface makes it easy for anyone to create amazing visualizations.