Broadcasting lintas kolom

Ingat bahwa saat melakukan broadcasting lintas kolom, NumPy mengharuskan Anda menyatakan secara eksplisit bahwa yang harus dibroadcast adalah array vertikal, dan array 1D horizontal maupun vertikal tidak ada di NumPy. Sebagai gantinya, Anda harus terlebih dulu membuat array 2D untuk menyatakan bahwa data Anda bersifat vertikal. Lalu, NumPy akan membuat salinan array 2D vertikal ini untuk setiap kolom dan menerapkan operasi yang diinginkan.

Sebuah list Python bernama monthly_growth_rate dengan len() sebesar 12 tersedia. List ini merepresentasikan pertumbuhan tahunan-bulan-ke-bulan yang diharapkan untuk perekonomian; tugas Anda adalah menggunakan broadcasting untuk mengalikan list ini dengan setiap kolom dalam array monthly_sales. Array monthly_sales sudah dimuat, bersama dengan numpy sebagai np.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Petunjuk latihan

Konversi monthly_growth_rate, yang saat ini berupa list Python, menjadi array NumPy satu dimensi bernama monthly_growth_1D.
Ubah bentuk monthly_growth_1D agar dapat dibroadcast per kolom pada monthly_sales; beri nama array baru tersebut monthly_growth_2D.
Dengan menggunakan broadcasting, kalikan setiap kolom dalam monthly_sales dengan monthly_growth_2D.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Convert monthly_growth_rate into a NumPy array
monthly_growth_1D = ____

# Reshape monthly_growth_1D
monthly_growth_2D = monthly_growth_1D.____

# Multiply each column in monthly_sales by monthly_growth_2D
print(____)

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: 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: Meringkas data Exercise 2: Total penjualan Exercise 3: Memplot rata-rata Exercise 4: Penjualan kumulatif Exercise 5: Operasi tervektorisasi Exercise 6: Perhitungan pajak Exercise 7: Memproyeksikan penjualan Exercise 8: Memvektorisasi .upper()Exercise 9: Broadcasting Exercise 10: Bisa dibroadcast atau tidak?Exercise 11: Broadcasting lintas kolom

Latihan Saat Ini

Exercise 12: Broadcasting antarbaris

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!