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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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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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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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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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Shown below is a DNA duplex sequence, including both the coding (top) and non-coding (bottom) strands.

Which one of the following pairs of oligonucleotide primers would be most suitable for amplifying (by PCR) the region of this duplex shaded in yellow?

[Note: You are not expected to estimate Tm values for these primers. If they have the correct sequence to anneal in the correct position, you should assume that they will.]

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Forward primer: 5'-CTCATTAGGGTACCCTTCGC-3'

Reverse primer: 5'-ATCATGTGCCCGGCTCCGTC-3'

❌

Forward primer: 5'-GAGTAATCCCATGGGAAGCG-3'

Reverse primer: 5'-ATCATGTGCCCGGCTCCGTC-3'

Forward primer: 5'-CTCATTAGGGTACCCTTCGC-3'

Reverse primer: 5'-TAGTACACGGGCCGAGGCAG-3'

Forward primer: 5'-CTCATTAGGGTACCCTTCGC-3'

Reverse primer: 5'-GACGGAGCCGGGCACATGAT-3'

Forward primer: 5'-GAGTAATCCCATGGGAAGCG-3'

Reverse primer: 5'-GACGGAGCCGGGCACATGAT-3'