Python Data Visualization Competency

  • 30m
  • 20 questions
The Python Data Visualization benchmark will measure your ability to apply data visualization techniques in Python using Python statistical plots, Python with Altair, and Dash Python frameworks. You will be evaluated on your ability to recognize the visual and analytical features of Python. A learner who scores high on this benchmark demonstrates that they have the skills to develop interactive Python applications with visual representations of plots, graphs, and charts.

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