[amp_mcq option1=”describe()” option2=”plt.plot()” option3=”df.apply()” option4=”np.add()” correct=”option4″]<\/p>\n
The correct answer is D. np.add().<\/p>\n
np.add() is a Python function that is used to perform element-wise mathematical operations on Numpy arrays. It takes two Numpy arrays as input and returns a new Numpy array with the elements of the two input arrays added together.<\/p>\n
For example, if you have two Numpy arrays, “`<\/p>\n arr1 = np.array([1, 2, 3]) The “`<\/p>\n arr1 = np.array([1, 2, 3]) The The other options are not correct because they are not used to perform element-wise mathematical operations on Numpy arrays.<\/p>\n [amp_mcq option1=”describe()” option2=”plt.plot()” option3=”df.apply()” option4=”np.add()” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[737],"tags":[],"class_list":["post-59745","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\narr1<\/code> and arr2<\/code>, you can use np.add()<\/code> to add them together like this:<\/p>\n\n
\n
\n
\narr2 = np.array([4, 5, 6])
\nnp.add(arr1, arr2)
\narray([5, 7, 9])
\n“`<\/p>\n<\/blockquote>\n<\/blockquote>\n<\/blockquote>\nnp.add()<\/code> function can also be used to add a scalar value to a Numpy array. For example, if you have a Numpy array arr1<\/code> and you want to add the number 5 to each element of the array, you can use np.add(arr1, 5)<\/code> like this:<\/p>\n\n
\n
\n
\nnp.add(arr1, 5)
\narray([6, 7, 8])
\n“`<\/p>\n<\/blockquote>\n<\/blockquote>\n<\/blockquote>\nnp.add()<\/code> function is a very versatile function that can be used to perform a variety of element-wise mathematical operations on Numpy arrays.<\/p>\n\n
describe()<\/code> is a function that is used to get a summary of the statistics of a Numpy array.<\/li>\nplt.plot()<\/code> is a function that is used to plot a Numpy array.<\/li>\ndf.apply()<\/code> is a function that is used to apply a function to each row of a DataFrame.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"