1
Understanding NumPy Arrays
Free
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.
2
Selecting and Updating Data
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.
3
Array Mathematics!
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.
4
Array Transformations
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.

Initializing

A range array

np.arange() has especially useful applications in graphing. Your task is to create a scatter plot with the values from doubling_array on the y-axis.

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

Recall that a scatter plot can be created using the following code:

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

With doubling_array on the y-axis, you now need values for the x-axis, which you can create with np.arange()!

numpy is loaded for you as np, and matplotlib.pyplot is imported as plt.

Using np.arange(), create a 1D array called one_to_ten which holds all integers from one to ten (inclusive).
Create a scatterplot with doubling_array as the y values and one_to_ten as the x values.