Editing Subsets

Editing Subsets#

Sometimes we want to modify a part of a matrix. For example, suppose we were working with our survey data, and we want to multiply all the income values by 1.02 to adjust for inflation that has occurred since the survey. Obviously, if we just multiplied the matrix by 1.02, we’d also modify things like education and age:

import numpy as np


survey = np.array(
    [[20, 22_000, 12], [35, 65_000, 16], [55, 19_000, 11], [45, 35_000, 12]]
)

survey

array([[   20, 22000,    12],
       [   35, 65000,    16],
       [   55, 19000,    11],
       [   45, 35000,    12]])

survey * 1.02

array([[2.040e+01, 2.244e+04, 1.224e+01],
       [3.570e+01, 6.630e+04, 1.632e+01],
       [5.610e+01, 1.938e+04, 1.122e+01],
       [4.590e+01, 3.570e+04, 1.224e+01]])

What we can do instead is extract the column with income, modify it, then replace the old income column with our updated column:

income_column = survey[:, 1]  # Extract income
adjusted_income = income_column * 1.02  # Adjust income
survey[:, 1] = adjusted_income  # Replace income with new values!
survey

array([[   20, 22440,    12],
       [   35, 66300,    16],
       [   55, 19380,    11],
       [   45, 35700,    12]])

Or, if we wanted, we could actually do all this in one step:

# Re-make survey so it hasn't been adjusted for inflation
survey = np.array(
    [[20, 22_000, 12], [35, 65_000, 16], [55, 19_000, 11], [45, 35_000, 12]]
)

# Now adjust income in one step!
survey[:, 1] = survey[:, 1] * 1.02
survey

array([[   20, 22440,    12],
       [   35, 66300,    16],
       [   55, 19380,    11],
       [   45, 35700,    12]])

And this is especially powerful if we subset on BOTH rows and columns. Suppose, for example, we wanted to see what people’s incomes would look like if anyone who didn’t finish high school (education < 12) got a tax credit of 10,000 dollars:

survey[survey[:, 2] < 12, 1] = survey[survey[:, 2] < 12, 1] + 10000

survey

array([[   20, 22440,    12],
       [   35, 66300,    16],
       [   55, 29380,    11],
       [   45, 35700,    12]])

Views and Copies with Matrices#

When it comes to views and copies, the same rules apply to matrices as applied to vectors: if you create a subset through simple indexing, the result will be a view; if you create a subset by a different method, you get a copy!

And that’s it! Now you’re a matrix pro.

Exercises#

  1. Using np.arange, create a 3 x 5 matrix with all the numbers from 0 to 14. Assignment to the variable my_matrix.

  2. Subset the third and fourth columns of the matrix (the columns starting with 2 and 3) with simple indexing. Assign the subset to the variable m2.

  3. Change the top, left-most element of your new matrix m2 to -99.

  4. Without running any code, try and predict what my_matrix currently looks like.

  5. Now check my_matrix—does it look how you expected? Why or why not?

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