Güncelle ve kaydet

Diyelim ki okyanus sahnelerini tanıyacak bir Machine Learning modeli eğitiyorsun. Modelin okyanusları sadece parlak, yazlık renklerle ilişkilendirmemesini istiyorsun; bu yüzden kötü hava koşullarında ya da akşam ışığında çekilmiş okyanus fotoğraflarını da özellikle ekliyorsun. Veriyi dengelemek için bazı görselleri elle dönüştürmen gerekebilir; bu görevinde Monet’nin okyanus sahnesi rgb_array dizisini karartacaksın.

Videodan hatırla: beyaz, maksimum RGB değeri olan 255 ile ilişkilidir; daha koyu renkler ise daha düşük değerlerle. numpy np takma adıyla yüklü ve önceki egzersizde yüklediğin 3B Monet rgb_array dizisi hazır.

Bu egzersiz

NumPy’ye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Reduce every value in rgb_array by 50 percent
darker_rgb_array = ____

Kodu Düzenle ve Çalıştır

Bu egzersiz

NumPy’ye Giriş

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

1185 reviews

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: Indexing and slicing arrays Exercise 2: Slicing and indexing trees Exercise 3: Stepping into 2D Exercise 4: Sorting trees Exercise 5: Filtering arrays Exercise 6: Filtering with masks Exercise 7: Fancy indexing vs. np.where()Exercise 8: Creating arrays from conditions Exercise 9: Adding and removing data Exercise 10: Compatible or not?Exercise 11: Adding rows Exercise 12: Adding columns Exercise 13: Deleting with np.delete()

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: Dizileri kaydetme ve yükleme Exercise 2: `.npy` dosyalarını yükleme Exercise 3: Yardım alma Exercise 4: Güncelle ve kaydet

Geçerli Egzersiz

Exercise 5: Dizi akrobasisi Exercise 6: Monet'yi Zenginleştirme Exercise 7: Şaheserini dönüştür: Transpose Exercise 8: Yığma ve bölme Exercise 9: 2Böl ve yığın Exercise 10: RGB verilerini bölme Exercise 11: RGB verilerini istifleme Exercise 12: Tebrikler!