Menggabungkan anotasi dan warna

Anda yakin bahwa Long Beach, California memiliki masalah kabut asap (smog). Dengan menggunakan data polusi, Anda akan mencoba mengajukan argumen untuk meningkatkan regulasi pengurangan smog melalui keahlian visualisasi data Anda. Secara khusus, Anda ingin menyoroti hubungan antara kadar CO dan O₃ selama tahun 2014 dalam sebuah rapat dewan kota.

Untuk menekankan bagaimana posisi Long Beach dibandingkan dengan sekumpulan kota sepadan, Anda memutuskan untuk menyorot Long Beach dan menarik perhatian pada satu hari yang sangat buruk ketika kadar CO adalah 1.6 dan O₃ adalah 0.072 menggunakan sebuah anotasi.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Menyoroti data Exercise 2: Meng-hardcode sorotan Exercise 3: Membuat highlight secara terprogram Exercise 4: Membandingkan kelompok Exercise 5: Membandingkan dengan dua KDE Exercise 6: Meningkatkan KDE Anda Exercise 7: Beeswarm Exercise 8: Anotasi Exercise 9: Anotasi teks dasar Exercise 10: Anotasi panah Exercise 11: Menggabungkan anotasi dan warna

Latihan Saat Ini

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!