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ENG1014 - Engineering Numerical Analysis - MUM S1 2025

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Which of the following is the correct syntax to save an array of numerical data, arr to a .txt file, output.txt, separated by commas, with no column headers, the first column as integers and the second rounded to 2 decimal places? Select all correct answers.

np.savetxt("output.txt", arr, delimiter=",", fmt="%.4f", comments = "")

np.savetxt("output.txt", arr, delimiter=",", fmt="%d", comments = "")

np.savetxt("output.txt", arr, delimiter=",", fmt="%.0f","%.2f", comments = "")

np.savetxt("output.txt", arr, delimiter=",", fmt=["%.0f","%.2f"], comments = "")

100%

np.savetxt("output.txt", arr, delimiter=",", fmt="%d","%.2f", comments = "")

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What is the required argument to import only the column headers from a file using np.genfromtxt()?

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A file which contains only one row of data is imported using np.genfromtxt() and is assigned to X. Which of the following is true?

X is a 1D array

✅

X is a 2D array

X is a nested list

X is a list

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Consider a CSV file, data.csv, that contains data about a grocery store's inventory. The dataset contains 3 attributes and many observations. A sample of this dataset is shown below:

Product	Stock	Unit Price
Sushi Rice	22	4.50
Arabica Coffee	45	20.00

What is the correct syntax in ENG1014 for importing this file using np.genfromtxt()?

np.genfromtxt("data.csv", delimiter = ",", dtype = str, encoding = "utf-8", skip_header = 1)

✅

np.genfromtxt("data.csv", delimiter = ",", dtype = float, encoding = "utf-8", skip_header = 1)

np.genfromtxt("data.csv", delimiter = ",", dtype = int, encoding = "utf-8", skip_header = 1)

np.genfromtxt("data.csv", delimiter = ",", dtype = [str, int, float], encoding = "utf-8", skip_header = 1)

❌

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Select all the required keyword arguments to import a file with the following characteristics:

file is separated by commas
all columns contain integers
file has column headers
import only columns 1 and 2

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Which of the following will filter an array (arr), creating a new array (arr_clean) that only contains positive numbers divisible by both 14 and 17? Select all correct answers.

arr_clean = arr[np.logical_or(arr > 0, np.logical_or(arr % 14 == 0, arr % 17 == 0))]

arr_clean = arr[np.logical_and(arr > 0, arr % 238 == 0)]

arr_clean = arr[np.logical_or(arr > 0, arr % 238 == 0)]

arr_clean = arr[np.logical_and(arr[(arr % 14 == 0)], arr[(arr % 17 == 0)])

arr_clean = arr[np.logical_and(arr > 0, np.logical_and(arr % 14 == 0, arr % 17 == 0))]

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Which of the following will filter an array (arr), creating a new array (arr_clean) that does not have any negative numbers, except for negative numbers that are divisible by 3 but not by 7.

arr_clean = arr[(arr <= 0) or (arr % 3 == 0) and (arr % 7 != 0)]

arr_clean = arr[np.logical_or(arr >= 0, np.logical_and(arr % 3 == 0, arr % 7 != 0))]

arr_clean = arr[(arr <= 0) or ((arr % 3 == 0) and (arr % 7 != 0))]

arr_clean = arr[(arr >= 0) or ((arr % 3 == 0) and (arr % 7 != 0))]

arr_clean = arr[np.logical_or(arr <= 0, np.logical_and(arr % 3 == 0, arr % 7 != 0))]

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Which of the following will adjust the array, arr, as follows:

round any positive values below 5 to 0
subtract 21 from any odd numbers but not those divisible by 19
double all numbers less than -1014

arr[np.logical_and(arr > 0, arr < 5)] = 0
arr[arr % 2 != 0] -= 21
arr[arr < -1014] *= 2

arr[np.logical_and(np.logical_and(np.logical_and(arr > 0, arr < 5), arr % 2 != 0), arr < -1014)] = {0, -=21, *2}

arr[np.logical_or(arr > 0, arr < 5)] = 0
arr[np.logical_or(arr % 2 != 0, arr % 19 != 0)] -= 21
arr[arr < -1014] *= 2

arr[np.logical_and(arr > 0, arr < 5)] = 0
arr[np.logical_and(arr % 2 != 0, arr % 19 != 0)] -= 21
arr[arr < -1014] *= 2

✅

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Consider the following arrays:

X = np.array([[8,0,9],[3,4,8],[8,3,7]])

Y = np.array([[3,7,4],[2,5,8],[2,4,8]])

What is the output of np.logical_or((X < 5), (Y > 5))?

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Consider an x-by-y array of booleans containing m False values and n True

values.

Additionally, consider a second

x-by-y

array. Which of the

following is true if the second array is indexed with the boolean array?

Output is a n-element 1D array

100%

Output is an x-by-y 2D array

Output is a m-element 1D array

Output is an x+y-element 1D array

Output is an m-by-n 2D array

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