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  3. Improving Your Data Visualizations in Python
  • 1

    Highlighting your data

    Free

    How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.

  • 2

    Using color in your visualizations

    Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this chapter, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing.

  • 3

    Showing uncertainty

    Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly.

  • 4

    Visualization in the data science workflow

    Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow.

Initializing

Exercise

90, 95, and 99% intervals

You are a data scientist for an outdoor adventure company in Fairbanks, Alaska. Recently, customers have been having issues with SO2 pollution, leading to costly cancellations. The company has sensors for CO, NO2, and O3 but not SO2 levels.

You've built a model that predicts SO2 values based on the values of pollutants with sensors (loaded as pollution_model, a statsmodels object). You want to investigate which pollutant's value has the largest effect on your model's SO2 prediction. This will help you know which pollutant's values to pay most attention to when planning outdoor tours. To maximize the amount of information in your report, show multiple levels of uncertainty for the model estimates.

Instructions

100 XP
  • Fill in the appropriate interval width percents (from 90,95, and 99%) according to the values list in alpha.
  • In the for loop, color the interval by its assigned color.
  • Pass the loop's width percentage value to plt.hlines() to label the legend.