Memvektorisasi .upper()

Ada banyak situasi ketika Anda ingin menggunakan metode dan fungsi Python pada elemen-elemen array di NumPy. Anda selalu bisa menulis for loop untuk melakukan ini, tetapi operasi tervektorisasi jauh lebih cepat dan efisien, jadi pertimbangkan untuk menggunakan np.vectorize()!

Anda memiliki sebuah array bernama names yang berisi nama depan dan nama belakang:

names = np.array([["Izzy", "Monica", "Marvin"],
                  ["Weber", "Patel", "Hernandez"]])

Anda ingin menggunakan salah satu metode Python yang Anda pelajari di DataCamp, .upper(), untuk membuat semua huruf dari setiap nama dalam array menjadi huruf besar. Sebagai pengingat, .upper() adalah metode string, artinya harus dipanggil pada sebuah instance string: str.upper().

Tugas Anda adalah memvektorisasi metode Python ini. numpy telah dimuat sebagai np, dan array names sudah tersedia.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Petunjuk latihan

Buat fungsi tervektorisasi bernama vectorized_upper dari metode string Python .upper().
Terapkan vectorized_upper() pada array names dan simpan array hasilnya sebagai uppercase_names.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Vectorize the .upper() string method
vectorized_upper = ____

# Apply vectorized_upper to the names array
uppercase_names = ____
print(uppercase_names)

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()

Latihan Saat Ini

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!