Python Data Visualization Competency (Intermediate Level)

Topics covered

• augment a histogram with a rule marking the mean for the distribution and enable interaction with your chart
• contrast box plots and boxen plots
• create a map of the United States and plot state-specific information using markers and choropleth maps
• create an ordinary bar chart using the Plotly Express library
• 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
• create custom heatmaps to visualize correlation matrices
• customize callbacks for more complex interactivity
• customize scatter plots with multiple variables and visualize categorical data
• customize various aspects of a chart such as the axis ticks, legend, and title using various functions such as configure_title() and configure_legend()
• distinguish between wide form and long form data and use these formats to plot Altair charts
• enhance bar charts by adding rules representing the mean or median of a distribution, conditional formatting, and creating stacked bar charts
• implement a Dash app with a complex layout
• link Dash components, such as gauges, sliders, and LED Fields
• link the date picker with charts
• perform filter operations on Dash DataTables
• produce Gantt charts to visualize activities, tasks, or events against time
• use the distplot() function for customizing histograms
• use the hue, col, and row input arguments to categorize regression plots
• use the Plotly graph objects library
• visualize time series data using figure-level and axis-level line charts