Mewarnai kategori ordinal

Anda bekerja untuk dewan kota Des Moines untuk menilai keterkaitan berbagai tingkat polutan di kota. Dua polutan terpenting adalah SO₂ dan NO₂, namun CO juga menarik untuk diteliti. Anda hanya diberi ruang untuk satu plot saja untuk bagian laporan Anda.

Anda memulai dengan scatter plot nilai SO₂ dan NO₂ karena keduanya paling penting, lalu memutuskan untuk menampilkan nilai CO menggunakan skala warna yang sesuai dengan kuartil CO. Dengan membagi nilai CO yang bersifat kontinu menjadi beberapa bin, Anda telah mengubah CO menjadi variabel ordinal yang dapat menyoroti pola besar tanpa mengharuskan penonton membandingkan perbedaan rona yang sangat halus.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Petunjuk latihan

Atur fungsi qcut() untuk membagi 'CO' ke dalam kuartil.
Petakan warna scatter plot Anda ke kolom kuartil yang baru.
Ubah palette ke palet ColorBrewer 'GnBu'.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Divide CO into quartiles
pollution['CO quartile'] = pd.qcut(pollution['CO'], q = ____, labels = False)

# Filter to just Des Moines
des_moines = pollution.query("city  ==  'Des Moines'")

# Color points with by quartile and use ColorBrewer palette
sns.scatterplot(x = 'SO2',
                y = 'NO2',
                ____ = '____', 
                  data = des_moines,
                palette = '____')
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: Warna dalam visualisasi Exercise 2: Menghapus warna yang tidak perlu Exercise 3: Memperbaiki bagan batang Seaborn Exercise 4: Palet warna kontinu Exercise 5: Membuat palet kontinu kustom Exercise 6: Menyesuaikan heatmap dengan palet divergen Exercise 7: Menyesuaikan palet sesuai konteks Exercise 8: Palet kategorikal Exercise 9: Menggunakan palet kategorikal kustom Exercise 10: Menangani terlalu banyak kategori Exercise 11: Mewarnai kategori ordinal

Latihan Saat Ini

Exercise 12: Memilih variabel yang tepat untuk dikodekan dengan warna

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!