De juiste variabele kiezen om met kleur te encoderen

Je gaat vervuilingswaarden voor Long Beach en nabijgelegen steden door de tijd heen visualiseren. De meegeleverde code maakt onderstaand (moeilijk leesbaar) plot, met maximale vervuilingswaarden (gegeven als max_pollutant_values) waarbij de balken zijn ingekleurd per stad.

Mutlicolor and busy bar plots with four rows corresponding to the four pollutants in dataset

Met een paar aanpassingen kun je dit snel verbeteren. Door de getoonde steden te beperken tot alleen die in de westelijke helft van het land voorkom je rommel. Vervolgens kun je door de kleur-encodering van city naar year om te zetten een ordinaal palet gebruiken, zodat de lezer niet steeds naar de legenda hoeft te kijken om te zien welke kleur bij welke stad hoort.

Deze oefening maakt deel uit van de cursus

Je datavisualisaties verbeteren in Python

Cursus bekijken

Oefeninstructies

Verwijder 'Indianapolis', 'Des Moines', 'Cincinnati', 'Houston' uit de vector cities.
Wissel de encoderingen van de variabelen city en year om.
Gebruik het ColorBrewer-palet 'BuGn' om je kleuren passend te mappen op de nieuw ordinale variabele.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Reduce to just cities in the western half of US
cities = ['Fairbanks', 'Long Beach', 'Vandenberg Air Force Base', 'Denver', 
          'Indianapolis', 'Des Moines', 'Cincinnati', 'Houston']

# Filter data to desired cities
city_maxes = max_pollutant_values[max_pollutant_values.city.isin(cities)]

# Swap city and year encodings
sns.catplot(x = 'year', hue = 'city',
              y = 'value', row = 'pollutant',    
              # Change palette to one appropriate for ordinal categories
              data = city_maxes, palette = 'muted',
              sharey = False, kind = 'bar')
plt.show()

Code bewerken en uitvoeren

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: Highlighting data Exercise 2: Hardcoding a highlight Exercise 3: Programmatically creating a highlight Exercise 4: Comparing groups Exercise 5: Comparing with two KDEs Exercise 6: Improving your KDEs Exercise 7: Beeswarms Exercise 8: Annotations Exercise 9: A basic text annotation Exercise 10: Arrow annotations Exercise 11: Combining annotations and color

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: Kleur in visualisaties Exercise 2: Onnodige kleur weghalen Exercise 3: Seaborns staafdiagrammen verbeteren Exercise 4: Doorlopende kleurenpaletten Exercise 5: Een aangepast continu palet maken Exercise 6: Een heatmap met een divergerend palet aanpassen Exercise 7: Je palet aanpassen aan de context Exercise 8: Categorische paletten Exercise 9: Een aangepast categorisch palet gebruiken Exercise 10: Omgaan met te veel categorieën Exercise 11: Ordinale categorieën inkleuren Exercise 12: De juiste variabele kiezen om met kleur te encoderen

Huidige oefening

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!