Membuat array dari kondisi

Saat ini, nilai diameter tunggul (stump) dan diameter batang (trunk) pada tree_census berada di dua kolom yang berbeda. Pohon hidup memiliki diameter tunggul nol, sedangkan tunggul memiliki diameter batang nol. Jika Anda ingin memasukkan baik pohon hidup maupun tunggul dalam perhitungan riset tertentu, akan bermanfaat jika diameter keduanya digabungkan dalam satu kolom saja.

numpy telah dimuat sebagai np, dan array tree_census tersedia. Sebagai pengingat, urutan kolom dalam sensus pohon merujuk pada ID pohon, ID bloknya, diameter batang, dan diameter tunggul.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Petunjuk latihan

Buat dan cetak array 1D bernama trunk_stump_diameters, yang mengganti diameter batang suatu pohon dengan diameter tunggulnya jika diameter batang bernilai nol.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create and print a 1D array of tree and stump diameters
trunk_stump_diameters = ____
print(trunk_stump_diameters)

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

Latihan Saat Ini

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()

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!