# Data Visualization: Getting Started with Plotly

Plotly 4.14    |    Beginner
• 9 videos | 1h 8m 59s
• Includes Assessment
Rating 4.5 of 22 users (22)
Plotly is Python's browser-based graphing library, which provides users with online graphing, analytics, and statistics tools. In this course, you'll explore how to use Plotly's declarative APIs to build interactive graphs and visualizations. You'll start this course by getting familiar with the components of the Plotly library. You'll identify the role of the high-level library (plotly.express) in creating visualizations and the low-level library (plotly.graph_objects) in creating granular customizations of your charts. Next, you'll investigate the use of box plots in visualizing the statistical properties of a continuous data series. You'll also discover how to represent additional categorical data by creating separate box plots and customizing their color. Finally, you'll examine how to implement a candlestick chart to reflect the trend of stock price performance over a period of time and visualize sequential data in a linear process using funnel charts.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course Install and import plotly using jupyter notebooks Identify the components of a plotly graph Visualize statistical data using box-and-whisker plots Visualize categorical data using box plots and strip plots
• Create colored and notched box plots Recognize how to plot financial data using candlestick charts Use funnel charts to visualize sequential data Summarize the key concepts covered in this course

## IN THIS COURSE

• In this video, you’ll learn more about the course and your instructor. In this course, you’ll learn the basic components that make up the Plotly library. You’ll learn about Plotly Express and plotly.graph objects. First, you’ll create a box and whisker plot using Plotly. Next, you’ll learn how to use candlestick charts. Finally, you’ll learn to create and visualize funnel charts.
• In this video, you’ll watch a demo. You’ll learn more about using Plotly. You’ll start at a terminal window. You’ll begin by typing out python --version. In this demo, you’ll use a Jupyter notebook. Next, you’ll launch the Jupyter notebook server. You’ll copy a URL into the browser to bring up the user interface of Jupyter. You’ll see there are various tabs associated with the files running programs and clusters.
• 3.  Components of Plotly Graphs
In this video, you’ll watch a demo. In this demo, you’ll learn to use simple Plotly visualizations. You’ll start by performing a simple import statement. This is the import of plotly.express as px. You’ll learn Plotly Express is a high-level library. It’s part of the Plotly package and can be used to directly create figure objects. You’ll discover Plotly Express APIs allow you to create figures quickly and efficiently.
• 4.  Creating Box Plots in Plotly
In this video, you’ll watch a demo. In this demo, you’ll learn to work with box plots in Plotly. Box plots. These are commonly referred to as box and whisker plots. You’ll learn they’re a great visual way to get a quick sense of the statistical properties of a distribution. At a glance, you’ll be able to tell the median, the 25th and 75th percentiles, and any outliers.
• 5.  Plotting Categorical Data with Box and Strip Plots
In this video, you’ll watch a demo. In this demo, you’ll work more with box plots. You’ll learn a more common use of box plots is to plot different box plots for x and y variables. The x variable is usually a categorical one, and you have box plots corresponding to values of the y variable for each value of the x variable. You’ll see an example of this onscreen.
• 6.  Customizing Plotly Box Plots
In this video, you’ll watch a demo. In this demo, you’ll work with box plots again. You’ll see onscreen the last box plot from the previous demo. There, you constructed a box plot with a categorical variable on the x-axis and a continuous variable on the y-axis. You’ll continue to build on this, and add two features to the next box plot. First, you’ll add color. Onscreen, you’ll see code to build this.
• 7.  Visualizing Financial Data Using Candlestick Charts
In this video, you’ll watch a demo. In this demo, you’ll learn about specialized visualizations known as candlestick charts, which are great for displaying financial data, specifically stock market information. You’ll start with the import statements. These include pandas as pd, graph_objects as go, and you’ll also import datetime. You’ll find this is in a file called GE.csv. Next, you’ll read it into a pandas data frame. You’ll invoke the head method.
• 8.  Visualizing Data Using Plotly Funnel Charts
In this video, you’ll watch a demo. In this demo, you’ll learn to build a funnel chart. You’ll see the funnel chart onscreen now. It represents the rates of conversion at various points in a sales funnel. You’ll the number of leads at the top. The funnel chart shows how many of those leads have been translated into sales calls and how many have generated follow up inquiries.
• 9.  Course Summary
In this video, you’ll summarize what you’ve learned in the course. You’ve learned to install Plotly using the PIP package manager. You learned Plotly Express is the high-level library for creating visualizations, while plotly.graphobjects is a relatively low-level library with JSON representations of figures. You learned how to create a basic Plotly Express figure object to create a graph. You also learned to use box and whisker plots for visualizing and representing different statistical properties.

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