Total penjualan

Himpunan data yang akan Anda gunakan pada bab ini berisi data penjualan selama satu tahun per bulan untuk tiga industri berbeda. Setiap baris dalam array monthly_sales mewakili bulan dari Januari hingga Desember. Kolom pertama berisi data penjualan bulanan untuk toko minuman beralkohol, kolom kedua berisi data untuk restoran, dan kolom terakhir mencatat penjualan untuk department store.

array([[ 4134, 23925,  8657],
       [ 4116, 23875,  9142],
       [ 4673, 27197, 10645],
       [ 4580, 25637, 10456],
       [ 5109, 27995, 11299],
       [ 5011, 27419, 10625],
       [ 5245, 27305, 10630],
       [ 5270, 27760, 11550],
       [ 4680, 24988,  9762],
       [ 4913, 25802, 10456],
       [ 5312, 25405, 13401],
       [ 6630, 27797, 18403]])

Tugas Anda adalah membuat sebuah array yang memuat semua informasi dari monthly_sales serta kolom keempat yang menjumlahkan penjualan bulanan lintas industri untuk setiap bulan.

numpy sudah 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.

# Create a 2D array of total monthly sales across industries
monthly_industry_sales = ____
print(monthly_industry_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: Meringkas data Exercise 2: Total penjualan

Latihan Saat Ini

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 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!