Array rentang

np.arange() sangat berguna untuk pembuatan grafik. Tugas Anda adalah membuat diagram pencar dengan nilai dari doubling_array pada sumbu-y.

doubling_array = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

Ingat bahwa diagram pencar dapat dibuat dengan kode berikut:

plt.scatter(x_values, y_values)
plt.show()

Dengan doubling_array pada sumbu-y, sekarang Anda memerlukan nilai untuk sumbu-x, yang dapat Anda buat dengan np.arange()!

numpy telah dimuat untuk Anda sebagai np, dan matplotlib.pyplot telah diimpor sebagai plt.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Petunjuk latihan

Dengan menggunakan np.arange(), buat array 1D bernama one_to_ten yang berisi semua bilangan bulat dari satu hingga sepuluh (inklusif).
Buat diagram pencar dengan doubling_array sebagai nilai y dan one_to_ten sebagai nilai x.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create an array of integers from one to ten
one_to_ten = ____

# Create your scatterplot
____
plt.show()

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: Mengenal array Exercise 2: Array NumPy pertama Anda Exercise 3: Membuat array dari awal Exercise 4: Array rentang

Latihan Saat Ini

Exercise 5: Dimensi array Exercise 6: Pembuatan array 3D Exercise 7: Dimensi keempat Exercise 8: Meratakan dan mengubah bentuk Exercise 9: Tipe data NumPy Exercise 10: Argumen dtype Exercise 11: Menantisipasi tipe data Exercise 12: Gim sudoku yang lebih kecil

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: 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!