Data Visualization in Python with seaborn and Altair Literacy (Beginner Level)

  • 16m
  • 16 questions
The Data Visualization in Python with seaborn and Altair Literacy (Beginner Level) benchmark measures your ability to use Seaborn to build univariate and bivariate histograms and kernel density estimation (KDE) curves and plots, as well as box, boxen, and violin plots. You will be evaluated on your ability to recognize the types of data that can be visualized in Altair and plot some of the basic charts available in this tool. A learner who scores high on this benchmark demonstrates that they have the skills to visualize and analyze data using seaborn and Altair.

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 pandas DataFrame and an Altair Data object and generate a bar chart
  • create histograms for univariate data
  • create univariate KDE curves and cumulative distributions
  • customize scatter plots with multiple variables and visualize categorical data
  • distinguish between wide form and long form data and use these formats to plot Altair charts
  • implement bar charts, KDE curves, and rug plots
  • implement figure-level and axis-level scatter plots
  • import in data to a DataFrame and create basic univariate histograms
  • remove limits on dataset size set by Altair by default
  • represent bivariate visualizations with color coding and grouped charts
  • use the distplot() function for customizing histograms
  • use the figure-level catplot() and axis-level violinplot()
  • verify the correct Python version is installed on your system, run Jupyter notebook, and install Altair
  • visualize bivariate histograms and KDE curves