Pemisahan dan penumpukan 2D

Keterampilan memisah dan menumpuk tidak hanya berguna untuk array RGB 3D: keterampilan ini sangat baik untuk melakukan subset dan mengatur data dari tipe dan dimensi apa pun!

Sekarang Anda akan kembali sejenak ke masa lalu untuk menata ulang array monthly_sales menjadi array 3D. Ingat bahwa dimensi pertama monthly_sales adalah baris yang mewakili penjualan satu bulan di tiga industri, dan dimensi keduanya adalah kolom data penjualan bulanan untuk satu industri.

Tugas Anda adalah memisahkan data ini menjadi data penjualan per kuartal dan menumpuk data penjualan per kuartal sehingga dimensi ketiga yang baru merepresentasikan empat array 2D penjualan per kuartal. numpy telah dimuat sebagai np, dan array monthly_sales tersedia.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Split monthly_sales into quarterly data
q1_sales, q2_sales, q3_sales, q4_sales = ____
print(q1_sales)

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: 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: Menyimpan dan memuat array Exercise 2: Memuat berkas .npy Exercise 3: Mendapatkan bantuan Exercise 4: Perbarui dan simpan Exercise 5: Akrobat array Exercise 6: Augmentasi Monet Exercise 7: Melakukan transpose pada mahakarya Anda Exercise 8: Stacking dan splitting Exercise 9: Pemisahan dan penumpukan 2D

Latihan Saat Ini

Exercise 10: Memisahkan data RGB Exercise 11: Menumpuk data RGB Exercise 12: Selamat!