Menangani terlalu banyak kategori

Terkadang Anda kekurangan ruang gambar dan perlu menampilkan banyak data sekaligus. Di sini Anda ingin menampilkan lintasan sepanjang tahun untuk setiap polutan di setiap kota dalam himpunan data pollution. Setiap lintasan polutan akan diplot sebagai garis dengan nilai y yang sesuai dengan simpangan baku dari rata-rata tahunan. Ini berarti akan ada banyak garis sekaligus pada plot Anda — jauh lebih banyak daripada yang bisa dibedakan jelas hanya dengan warna.

Untuk mengatasinya, Anda memutuskan menyoroti sebagian kecil kombinasi kota–polutan (wanted_combos). Subkumpulan ini paling penting bagi Anda, dan lintasan lainnya akan memberikan konteks berharga untuk perbandingan. Untuk memfokuskan perhatian, Anda akan menyetel semua garis lintasan yang tidak disorot ke warna 'lainnya' yang sama.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Petunjuk latihan

Ubah list comprehension untuk mengisolasi kombinasi kota dan polutan yang diinginkan (wanted_combos).
Beri tahu line plot untuk mewarnai garis berdasarkan kolom color_cats yang baru dibuat di DataFrame Anda.
Gunakan argumen units untuk menentukan bagaimana, yaitu dari kolom mana, titik data harus dihubungkan untuk membentuk setiap garis.
Nonaktifkan pembinningan titik dengan argumen estimator.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Choose the combos that get distinct colors
wanted_combos = ['Vandenberg Air Force Base NO2', 'Long Beach CO', 'Cincinnati SO2']

# Assign a new column to DataFrame for isolating the desired combos
city_pol_month['color_cats'] = [x if x in ____ else 'other' for x in city_pol_month['city_pol']]

# Plot lines with color driven by new column and lines driven by original categories
sns.lineplot(x = "month",
             y = "value",
             hue = '____',
             units = '____',
             estimator = ____,
             palette = 'Set2',
             data = city_pol_month)
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

Latihan Saat Ini

Exercise 11: Mewarnai kategori ordinal 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!