Membuat confidence band

Vandenberg Air Force Base sering digunakan sebagai lokasi peluncuran roket ke luar angkasa. Anda memiliki teori bahwa peningkatan laju peluncuran roket baru-baru ini dapat merusak kualitas udara di wilayah sekitarnya. Untuk menelaah hal ini, Anda memplot garis rata-rata bergerak 25 hari dari pengukuran NO₂ atmosfer. Untuk membantu menentukan apakah pola yang diamati merupakan kebisingan acak atau bukan, Anda memutuskan untuk menambahkan confidence band 99% di sekitar mean bergerak Anda. Menambahkan confidence band pada garis tren dapat membantu menjelaskan stabilitas tren yang terlihat. Ini dapat meningkatkan atau menurunkan keyakinan terhadap tren yang ditemukan.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Petunjuk latihan

Bangun pita interval 99% atas dan bawah dengan menambahkan dan mengurangkan 2.58 galat baku dari mean.
Jadikan garis taksiran titik berwarna putih.
Jadikan garis taksiran titik semi-transparan dengan menyetel alpha ke 0.4.
Beri tahu plt.fill_between() nilai mana yang harus diisi di antara keduanya untuk setiap hari.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Draw 99% inverval bands for average NO2
vandenberg_NO2['lower'] = vandenberg_NO2['mean'] ____ ____*vandenberg_NO2['std_err']
vandenberg_NO2['upper'] = vandenberg_NO2['mean'] ____ ____*vandenberg_NO2['std_err']

# Plot mean estimate as a white semi-transparent line
plt.plot('day', 'mean', data = vandenberg_NO2,
         color = '____', alpha = ____)

# Fill between the upper and lower confidence band values
plt.fill_between(x = 'day', 
                 ____ = 'lower', ____ = 'upper', 
                 data = vandenberg_NO2)

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

Latihan Saat Ini

Exercise 6: Memisahkan banyak pita 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!