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Short Exercises: Numpy

Arrays

Exercise #1

File: compute_matching.py

Complete the function compute_matching, which takes two lists of equal length and returns a list of the same length where the ith element is True if the ith elements of the two lists are equal. For example, given the arrays np.array([10, 20, 30]) and np.array([10, 30, 30]), the function would return np.array([True, False, True]).

Exercise #2

File: compute_matching_indices.py

Complete the function compute_matching_indices, which takes two arrays of equal length and returns an array of the indices where the elements of the two arrays are equal. For example, given the arrays np.array([10, 20, 30]) and np.array([10, 30, 30]), the function would return np.array([0, 2]).

Arrays and scalars

Exercise #1

File: powers.py

Complete the function powers(N, p), which computes the first N powers of p. For example, powers(5,2) would return the array np.array([1, 2, 4, 8, 16]).

Masking array values

Exercise #1

File: clip_values.py

Complete the function clip_values, which takes in an n-dimensional array and returns a new array with its values clipped between min_val and max_val. For example, clip_values(np.array([1, 2, 3]), min_val=2) would return np.array([2, 2, 3] and clip_values(np.array([1, 2, 3]), max_val=2) would return np.array([1, 2, 2]. Remember to return a new array and to not modify the input array.

Indexing

Exercise #1

File: find_closest_value.py

Complete the function find_closest_value which will find the entry and the value in an one-dimensional array that is closest to the mean of the array. For example find_closest_value(np.array([1.0, 2.0, 3.0])) would return (1, 2.0) and find_closest_value(np.array([5.0, 1.0, 8.0])) would return (0, 5.0).

Exercise #2

File: select_row_col.py

Complete the function select_row_col(x, row_idx, col_idx) that takes in a 2-dimensional array x and returns a subset of rows or columns or sub-array specified by row_idx and col_idx. If you specify row_idx as a list and col_idx as None, you will return a subset of rows. Similarly, if you specify row_idx as None and col_idx as a list, you will return a subset of columns. If you specify row_idx as a list and col_idx as a list, you will return a sub-array specified by the given rows and columns. If you specify both row_idx and col_idx as None, you will return the array itself. For example,

In [1]: x = np.array([[0, 1, 2],
                      [3, 4, 5],
                      [6, 7, 8]])

In [2]: se5.select_row_col(x, [1, 2], None)
Out[2]:
array([[3, 4, 5],
       [6, 7, 8]])

In [3]: se5.select_row_col(x, None, [1, 2])
Out[3]:
array([[1, 2],
       [4, 5],
       [7, 8]])

In [4]: se5.select_row_col(x, [1, 2], [0, 2])
Out[4]:
array([[3, 5],
       [6, 8]])