Memilih variabel yang tepat untuk dikodekan dengan warna

Anda diminta memvisualisasikan nilai polusi untuk Long Beach dan kota-kota terdekat dari waktu ke waktu. Kode yang disediakan menghasilkan plot (sulit dibaca) di bawah ini, yang terdiri dari nilai polusi maksimum (disediakan sebagai max_pollutant_values) dengan batang diwarnai berdasarkan kota.

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

Anda dapat segera memperbaikinya dengan beberapa penyesuaian. Dengan memodifikasi kota yang ditampilkan hanya ke kota-kota di bagian barat negara, Anda akan mengurangi kekacauan. Selanjutnya, menukar pengodean warna dari city ke year memungkinkan Anda menggunakan palet ordinal, sehingga pembaca tidak perlu terus-menerus merujuk ke legenda untuk mengecek warna mana yang sesuai dengan kota mana.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Petunjuk latihan

Hapus 'Indianapolis', 'Des Moines', 'Cincinnati', 'Houston' dari vektor cities.
Tukar pengodean variabel city dan year.
Gunakan palet ColorBrewer 'BuGn' untuk memetakan warna Anda secara tepat untuk variabel ordinal yang baru.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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()

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 Exercise 12: Memilih variabel yang tepat untuk dikodekan dengan warna

Latihan Saat Ini

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!