Beeswarm

Buat plot beeswarm menggunakan sns.swarmplot() untuk melihat kadar Ozone di semua kota pada data pollution untuk bulan Maret. Agar beeswarm lebih mudah dibaca, kecilkan ukuran titik untuk menghindari penumpukan akibat banyaknya titik pada layar. Terakhir, karena Anda telah melakukan beberapa manipulasi data untuk membuat plot ini, berikan judul agar pembaca memahami konteks apa yang mereka lihat.

Latihan ini merupakan bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Instruksi latihan

Subset data pollution agar hanya mencakup observasi pada bulan Maret.
Plot kadar O3 sebagai nilai kontinu di swarmplot().
Kecilkan ukuran titik menjadi 3 untuk menghindari penumpukan titik.
Beri judul plot 'March Ozone levels by city'.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Filter data to just March
pollution_mar = pollution[pollution.month == ____]

# Plot beeswarm with x as O3
sns.swarmplot(y = "city",
              x = '____', 
              data = pollution_mar, 
              # Decrease the size of the points to avoid crowding 
              size = ____)

# Give a descriptive title
plt.____('____')
plt.show()

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

SkillTag.level.intermediateSkillTag.label

4.7+

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

Latihan Saat Ini

Exercise 8: Anotasi Exercise 9: Anotasi teks dasar Exercise 10: Anotasi panah Exercise 11: Menggabungkan anotasi dan warna

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!