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Loops

When you need to do a repetitive task, like working through all elements in a list to find a prime or searching a map until you've found all of the gold, loops are a go-to tool.

List comprehension

     

Use list comprehension to generate the list of all perfect numbers less than 10,000, \(\{P_1,P_2,\ldots,P_N\}\).

What is their sum \(\sum_i P_i\)?

Assumptions and Details

  • A perfect number is any integer which is equal to the sum of its proper divisors (all its divisors except for itself).
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by Brilliant Staff

Suppose you're working on code for a molecular dynamics visualization. You need to represent various matrices, like the 3d rotation matrix, using Python lists. We can do so by using lists of lists.

For example, the matrix

\[A= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ \end{bmatrix} \]

is represented by

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A = [[1,0,0], [0,1,0], [0,0,1]]

and the matrix

\[B= \begin{bmatrix} 1 \\ 2 \\ 3 \\ \end{bmatrix} \]

would be

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B = [[1],[2],[3]]

The fragments below are designed to perform operations on matrices. Your task is to match the function bodies with the correct function definitions.

A:

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2
def getcolumn(matrix, i):
    # Given a matrix named 'matrix' this method should return the ith column

B:

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2
3
def add(A, B):
    # Given two matrices (A and B) of the same dimensions,
    # this should return the sum of the two matrices

C:

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2
3
def mult1D(row, col):
    # Given a row matrix[a1,a2..an] and a column[[r1],[r2]..[rn]]
    # matrix, this should return the 0D product [a1*r1 + a2*r2 .. an*rn]

The function bodies are given by the following

1:

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[[A[j][i] + B[j][i] for i in range(len(A[0]))] 
                    for j in range(0, len(A))]

2:

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[sum([row[i] * col[i][0] for i in range(len(row))])]

3:

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[[r[i]] for r in matrix]
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by Brilliant Staff

In scientific computing, it is a common task to take the transpose of a matrix. The transpose of a matrix \(A\), \(A^T\), is found by turning all the rows of a matrix into columns and vice versa. More formally the transpose of a matrix \(A\) is found by reflecting \(A\) over its main diagonal.

For example, the transpose of

\[ A = \begin{bmatrix} 1 & 2 & 3 \\ 4 & 1 & 9 \\ 8 & 0 & 1 \\ \end{bmatrix} \]

is given by

\[ A^T = \begin{bmatrix} 1 & 4 & 8 \\ 2 & 1 & 0 \\ 3 & 9 & 1 \\ \end{bmatrix} \]

Which of the following snippets of code would correctly transpose the \(30 \times 20\) matrix \(A\)?

A:

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transpose = [[a[i][j] for i in range(0, 30 + 1)] 
                      for j in range(0, 20 + 1)]

B:

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transpose = [[a[i][j] for j in range(0, 30)] 
                      for i in range(0, 20)]

C::

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transpose = [[a[j][i] for j in range(0, 30)] 
                      for i in range(0, 20)]

D::

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2
transpose = [[a[i][j] for i in range(0, 30+1)] 
                      for j in range(0, 20+1)]
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