Memisahkan banyak pita

Cukup mudah untuk memplot banyak garis tren bertumpuk untuk perbandingan yang cepat dan presisi. Namun, jika Anda perlu menambahkan pita ketidakpastian di sekitar garis-garis tersebut, plot menjadi sangat sulit dibaca. Menentukan apakah sebuah garis sesuai dengan bagian atas pita suatu kelas atau bagian bawah kelas lain dapat sulit karena tumpang tindih pita. Untungnya di Seaborn, tidak sulit untuk memecah pita yang saling tumpang tindih ke dalam plot bertajuk (faceted) terpisah.

Untuk melihat ini, jelajahi tren kadar SO₂ untuk beberapa kota di paruh timur AS. Jika Anda memplot tren dan pita kepercayaannya dalam satu plot—hasilnya berantakan. Untuk memperbaikinya, gunakan fungsi FacetGrid() dari Seaborn untuk menyebarkan interval kepercayaan ke beberapa panel agar lebih mudah diperiksa.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Petunjuk latihan

Siapkan facet grid untuk memisahkan plot berdasarkan kolom city di eastern_SO2.
Kirim fungsi pemetaan interval kepercayaan ke map().
Warnai interval kepercayaan dengan 'coral'.
Bantu garis mean yang ditumpuk yang digambar dengan g.map(plt.plot,...) agar menonjol dibanding pita kepercayaan dengan mewarnainya putih.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Setup a grid of plots with columns divided by location
g = sns.FacetGrid(eastern_SO2, col = '____', col_wrap = 2)

# Map interval plots to each cities data with corol colored ribbons
g.map(plt.____, 'day', 'lower', 'upper', ____ = 'coral')

# Map overlaid mean plots with white line
g.map(plt.plot, 'day', 'mean', ____ = '____')

plt.show()

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: 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: 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: Interval estimasi titik Exercise 2: Interval kepercayaan dasar Exercise 3: Memberi anotasi pada interval kepercayaan Exercise 4: Pita kepercayaan Exercise 5: Membuat confidence band Exercise 6: Memisahkan banyak pita

Latihan Saat Ini

Exercise 7: Merapikan pita untuk area yang tumpang tindih Exercise 8: Melampaui 95%Exercise 9: Interval 90%, 95%, dan 99%Exercise 10: Pita 90 dan 95%Exercise 11: Menggunakan ketebalan pita alih-alih pewarnaan Exercise 12: Memvisualisasikan bootstrap Exercise 13: Histogram bootstrap Exercise 14: Regresi bootstrap Exercise 15: Banyak bootstrap dengan beeswarm

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!