Ağaçları sıralama

Bazen veriyi, en çok ilgilendiğin değere göre sıralanmış halde görmek en anlaşılır yoldur. Yeni araştırma görevin, New York City ağaç sayımındaki gövde çaplarını, küçükten büyüğe sıralanmış şekilde içeren bir dizi oluşturmak.

numpy np olarak yüklendi ve tree_census 2B dizisi hazır.

Bu egzersiz

NumPy’ye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

tree_census içinden yalnızca gövde çapı sütununu seçip, en küçük gövde çapları dizinin başında, en büyükler sonunda olacak şekilde sıralayan sorted_trunk_diameters adlı bir dizi oluştur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Extract trunk diameters information and sort from smallest to largest
sorted_trunk_diameters = ____
print(sorted_trunk_diameters)

Kodu Düzenle ve Çalıştır

Bu egzersiz

NumPy’ye Giriş

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

Meet the incredible NumPy array! Learn how to create and change array shapes to suit your needs. Finally, discover NumPy's many data types and how they contribute to speedy array operations.

Exercise 1: Introducing arrays Exercise 2: Your first NumPy array Exercise 3: Creating arrays from scratch Exercise 4: A range array Exercise 5: Array dimensionality Exercise 6: 3D array creation Exercise 7: The fourth dimension Exercise 8: Flattening and reshaping Exercise 9: NumPy data types Exercise 10: The dtype argument Exercise 11: Anticipating data types Exercise 12: A smaller sudoku game

Sharpen your NumPy data wrangling skills by slicing, filtering, and sorting New York City’s tree census data. Create new arrays by pulling data based on conditional statements, and add and remove data along any dimension to suit your purpose. Along the way, you’ll learn the shape and dimension compatibility principles to prepare for super-fast array math.

Exercise 1: Dizilerde indeksleme ve dilimleme Exercise 2: Ağaçları dilimleme ve indeksleme Exercise 3: 2B'ye Adım Atmak Exercise 4: Ağaçları sıralama

Geçerli Egzersiz

Exercise 5: Dizileri filtreleme Exercise 6: Maskelerle filtreleme Exercise 7: Süslü indeksleme ve np.where()Exercise 8: Koşullardan diziler oluşturma Exercise 9: Veri ekleme ve kaldırma Exercise 10: Uyumlu mu değil mi?Exercise 11: Satır ekleme Exercise 12: Sütun ekleme Exercise 13: np.delete() ile silme

Leverage NumPy’s speedy vectorized operations to gather summary insights on sales data for American liquor stores, restaurants, and department stores. Vectorize Python functions for use in your NumPy code. Finally, use broadcasting logic to perform mathematical operations between arrays of different sizes.

Exercise 1: Summarizing data Exercise 2: Sales totals Exercise 3: Plotting averages Exercise 4: Cumulative sales Exercise 5: Vectorized operations Exercise 6: Tax calculations Exercise 7: Projecting sales Exercise 8: Vectorizing .upper()Exercise 9: Broadcasting Exercise 10: Broadcastable or not?Exercise 11: Broadcasting across columns Exercise 12: Broadcasting across rows

NumPy meets the art world in this final chapter as we use image data from a Monet masterpiece to explore how you can use to augment image data. You’ll use flipping and transposing functionality to quickly transform our masterpiece. Next, you’ll pull the Monet array apart, make changes, and reconstruct it using array stacking to see the results.

Exercise 1: Saving and loading arrays Exercise 2: Loading .npy files Exercise 3: Getting help Exercise 4: Update and save Exercise 5: Array acrobatics Exercise 6: Augmenting Monet Exercise 7: Transposing your masterpiece Exercise 8: Stacking and splitting Exercise 9: 2D split and stack Exercise 10: Splitting RGB data Exercise 11: Stacking RGB data Exercise 12: Congratulations!