Annotaties en kleur combineren

Je vermoedt dat Long Beach, Californië een smogprobleem heeft. Met behulp van de vervuilingsdata probeer je, met je datavisualisatie-skills, te pleiten voor strengere regels om smog terug te dringen. Je wilt je specifiek richten op de relatie tussen CO en O₃-niveaus in 2014 tijdens een raadsvergadering.

Om te benadrukken hoe Long Beach zich verhoudt tot vergelijkbare steden, heb je besloten Long Beach uit te lichten en met een annotatie de aandacht te vestigen op een bijzonder slechte dag waarop het CO-niveau 1.6 was en O₃ 0.072.

Deze oefening maakt deel uit van de cursus

Je datavisualisaties verbeteren in Python

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Make a vector where Long Beach is orangered; else lightgray
is_lb = ['____' if city  ==  '____' else '____' for city in pollution['city']]

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Deze oefening maakt deel uit van de cursus

Je datavisualisaties verbeteren in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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.

Exercise 1: Data highlighten Exercise 2: Een highlight hardcoden Exercise 3: Programmeerbaar een highlight maken Exercise 4: Groepen vergelijken Exercise 5: Vergelijken met twee KDE's Exercise 6: Je KDE's verbeteren Exercise 7: Beeswarms Exercise 8: Annotaties Exercise 9: Een eenvoudige tekstaanduiding Exercise 10: Pijlaanmerkingen Exercise 11: Annotaties en kleur combineren

Huidige oefening

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.

Exercise 1: Color in visualizations Exercise 2: Getting rid of unnecessary color Exercise 3: Fixing Seaborn's bar charts Exercise 4: Continuous color palettes Exercise 5: Making a custom continuous palette Exercise 6: Customizing a diverging palette heatmap Exercise 7: Adjusting your palette according to context Exercise 8: Categorical palettes Exercise 9: Using a custom categorical palette Exercise 10: Dealing with too many categories Exercise 11: Coloring ordinal categories Exercise 12: Choosing the right variable to encode with color

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.

Exercise 1: Point estimate intervals Exercise 2: Basic confidence intervals Exercise 3: Annotating confidence intervals Exercise 4: Confidence bands Exercise 5: Making a confidence band Exercise 6: Separating a lot of bands Exercise 7: Cleaning up bands for overlaps Exercise 8: Beyond 95%Exercise 9: 90, 95, and 99% intervals Exercise 10: 90 and 95% bands Exercise 11: Using band thickness instead of coloring Exercise 12: Visualizing the bootstrap Exercise 13: The bootstrap histogram Exercise 14: Bootstrapped regressions Exercise 15: Lots of bootstraps with beeswarms

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.

Exercise 1: First explorations Exercise 2: Looking at the farmers market data Exercise 3: Scatter matrix of numeric columns Exercise 4: Digging in with basic transforms Exercise 5: Exploring the patterns Exercise 6: Is latitude related to months open?Exercise 7: What state is the most market-friendly?Exercise 8: Popularity of goods sold by state Exercise 9: Making your visualizations efficient Exercise 10: Stacking to find trends Exercise 11: Using a plot as a legend Exercise 12: Tweaking your plots Exercise 13: Cleaning up the background Exercise 14: Remixing a plot Exercise 15: Enhancing legibility Exercise 16: Congrats!