Python with Altair: An Introduction to Altair

Python with Altair: An Introduction to Altair

• discover the key concepts covered in this course
• verify the correct Python version is installed on your system, run Jupyter notebook, and install Altair
• create a pandas DataFrame and an Altair Data object and generate a bar chart
• distinguish between wide form and long form data and use these formats to plot Altair charts
• import in data to a DataFrame and create basic univariate histograms
• augment a histogram with a rule marking the mean for the distribution and enable interaction with your chart
• remove limits on dataset size set by Altair by default
• summarize the key concepts covered in this course

This course will get you familiar with the building blocks of Altair visualizations and some of the important chart settings. You will touch upon some of the fundamentals of plotting graphs in Altair. You'll start off by learning about the basic data structures that can form the basis of Altair visualizations, including JSON data and Pandas DataFrames in both wide-form and long-form. You'll then move on to plotting one of the simpler graphs, histograms, to visualize the distribution of values for a quantitative field in your dataset. While doing so, you'll get to explore the different ways in which Altair graphs can be customized including augmenting your chart with text, layering histograms to view two distributions together, and making histograms interactive.

Python with Altair: Plotting Fundamental Graphs

Python with Altair: Plotting Fundamental Graphs

• discover the key concepts covered in this course
• generate a variety of box plots such as plain box plots, box plots with categorical color bars, and box plots with continuous color bars
• define violin plots such as basic violins, trellis violin plots, and violin plots with color scales
• produce basic bar charts such as bar charts with labels and bar charts with the bars sorted in an ascending or descending order
• enhance bar charts by adding rules representing the mean or median of a distribution, conditional formatting, and creating stacked bar charts
• visualize data using line charts and customize various aspects of the chart such as the interpolation and by adding rules to the chart
• perform various customizations on a line chart such as adding a range slider, visualizing multiple variables in a single chart, and adding a new axis for the second column
• recognize the use of area charts as an extension of line charts to sum up all the values in a data set and create an area chart with a gradient in the color of the chart
• create various customized area charts such as area charts with multiple categories, streamgraphs, and trellis area charts
• visualize data and identify the relationships between variables using scatter plots and create a scatter plot where the color of the data points represent a variable
• create various custom scatter plots such as scatter plots where the size of the points represent a variable, scatter plots binned into categories, scatter plots with hollow points, and scatter plots with text labels
• create a scatter plot that is linked to a histogram so that the histogram visualizes all the points in the scatter plot that are selected by a brush
• summarize the key concepts covered in this course

Python with Altair: Working with Specialized Graphs

Python with Altair: Working with Specialized Graphs

• discover the key concepts covered in this course
• produce world maps using data in the topo JSON format and plot points on the map by specifying the latitude and longitude coordinates
• create a map of the United States and plot state-specific information using markers and choropleth maps
• generate heat maps to visualize data in the form of a grid
• represent data with one dot per value using dot plots and visualize a range of data in a sequence using ranged dot plots
• customize various aspects of a chart such as the axis ticks, legend, and title using various functions such as configure_title() and configure_legend()
• create a scatter chart where specific points can be selected based on a brush in another linked chart or form element
• analyze financial data using candlestick charts
• define an Isotype Visualization that represents data by default using dots and can be customized to use emojis to create a pictograph
• produce Gantt charts to visualize activities, tasks, or events against time
• visualize individual data points in your dataset using strip plots
• access and visualize the top k data points based on a variable from a dataset
• summarize the key concepts covered in this course