Broadcasting across columns

Recall that when broadcasting across columns, NumPy requires you to be explicit that it should broadcast a vertical array, and horizontal and vertical 1D arrays do not exist in NumPy. Instead, you must first create a 2D array to declare that you have vertical data. Then, NumPy creates a copy of this 2D vertical array for each column and applies the desired operation.

A Python list called monthly_growth_rate with len() of 12 is available. This list represents monthly year-over-year expected growth for the economy; your task is to use broadcasting to multiply this list by each column in the monthly_sales array. The monthly_sales array is loaded, along with numpy as np.

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Introduction to NumPy

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Kursu Görüntüle

Egzersiz talimatları

Convert monthly_growth_rate, currently a Python list, into a one-dimensional NumPy array called monthly_growth_1D.
Reshape monthly_growth_1D so that it is broadcastable column-wise across monthly_sales; call the new array monthly_growth_2D.
Using broadcasting, multiply each column in monthly_sales by monthly_growth_2D.

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Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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(____)

Kodu Düzenle ve Çalıştır

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Introduction to NumPy

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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

Geçerli Egzersiz

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!