Menghapus dengan np.delete()

Bagaimana jika penelitian pohon Anda hanya berfokus pada pohon hidup di blok kota milik publik? Akan berguna untuk menghapus beberapa data yang tidak diperlukan seperti kolom diameter tunggul dan beberapa pohon yang berada di blok privat.

Anda telah mempelajari bahwa fungsi np.delete() milik NumPy menerima tiga argumen: array asli, indeks atau beberapa indeks yang akan dihapus, dan sumbu (axis) tempat penghapusan dilakukan. Jika Anda tidak mengetahui indeks dari elemen array yang ingin dihapus, ingat bahwa saat hanya diberi satu argumen, np.where() mengembalikan array indeks tempat suatu kondisi terpenuhi!

numpy sudah dimuat sebagai np, dan array 2D tree_census tersedia. Kolom-kolom secara berurutan merepresentasikan ID pohon, nomor blok, diameter batang, dan diameter tunggul.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Delete the stump diameter column from tree_census
tree_census_no_stumps = ____

# Save the indices of the trees on block 313879
private_block_indices = ____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar NumPy

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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: Pengindeksan dan pengirisan array Exercise 2: Melakukan slicing dan indexing pada pohon Exercise 3: Melangkah ke 2D Exercise 4: Mengurutkan pohon Exercise 5: Menyaring array Exercise 6: Memfilter dengan mask Exercise 7: Fancy indexing vs. np.where()Exercise 8: Membuat array dari kondisi Exercise 9: Menambah dan menghapus data Exercise 10: Kompatibel atau tidak?Exercise 11: Menambahkan baris Exercise 12: Menambahkan kolom Exercise 13: Menghapus dengan np.delete()

Latihan Saat Ini

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!