Apakah garis lintang berkaitan dengan bulan buka?

Saat menjelajahi himpunan data pasar tani dengan matriks sebar, Anda melihat hubungan yang tampaknya menarik antara garis lintang suatu pasar dan jumlah bulan pasar tersebut tetap buka. Untuk menggali hubungan ini lebih jauh, Anda memutuskan menggunakan plot regresi Seaborn untuk melihat apakah pola tersebut memang kuat atau hanya karena tumpang tindih titik yang berat sehingga menipu penglihatan Anda.

Agar garis regresi lebih menonjol, Anda perlu menurunkan opasitas titik latar belakang yang saling tumpang tindih dan mewarnainya abu-abu lembut. Karena Anda tidak akan membuat inferensi formal dan ingin menelaah pola dengan cepat, Anda dapat mematikan pita ketidakpastian bawaan.

Latihan ini adalah bagian dari kursus

Meningkatkan Visualisasi Data Anda di Python

Petunjuk latihan

Atur opasitas titik pada scatter plot menjadi 10% dan warnai 'gray'.
Nonaktifkan pita interval kepercayaan bawaan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

sns.regplot(x = 'lat', 
            y = 'months_open', 
            # Set scatter point opacity & color
            ____ = {'alpha':____, 'color':'____'}, 
            # Disable confidence band
            ____ = ____, 
            data = markets)

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: 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: Eksplorasi pertama Exercise 2: Melihat data pasar tani Exercise 3: Scatter matrix untuk kolom numerik Exercise 4: Mendalami dengan transformasi dasar Exercise 5: Menjelajahi pola Exercise 6: Apakah garis lintang berkaitan dengan bulan buka?

Latihan Saat Ini

Exercise 7: Negara bagian mana yang paling ramah pasar?Exercise 8: Popularitas komoditas yang dijual per negara bagian Exercise 9: Membuat visualisasi Anda efisien Exercise 10: Menumpuk plot untuk menemukan tren Exercise 11: Menggunakan plot sebagai legenda Exercise 12: Menyetel plot Anda Exercise 13: Merapikan latar belakang Exercise 14: Menyusun ulang sebuah plot Exercise 15: Meningkatkan keterbacaan Exercise 16: Selamat!