Question 4 Not complete Marked out of 5.00 Flag question Sometimes data gets corrupted, and for us, it will be indicated

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: Question 4 Not Complete Marked Out Of 5 00 Flag Question Sometimes Data Gets Corrupted And For Us It Will Be Indicated 1 (138.74 KiB) Viewed 15 times

Question 4 Not complete Marked out of 5.00 Flag question Sometimes data gets corrupted, and for us, it will be indicated by value np.nan (or simply, nan). You are given a code that randomly introduces the nan value into your current dataset for testing (in your skeleton code). For our analysis, we will convert nan values to be the mean value of the rest of the data. For example, if we have data [1, 2, nan, 4, 5, nan], then those nan values will be replaced by (1 + 2 + 4 + 5)/4 = 3 -> [1, 2, 3, 4, 5, 3]. Write a function replace_nan (data) where you replace all nan values in the columns 'valence_intensity', 'anger_intensity', 'fear_intensity', 'sadness_intensity', and 'joy_intensity' to be the mean value of each column. Note: using appropriate NumPy functions and methods, this can be solved in less than 10 lines. Note: don't round your answer. For example: Test Result nan data = unstructured_to_structured (load_metrics ("covid_sentiment_metrics.csv"), [0, 1, 7, 8]) data[:]['created_at'] converting_timestamps (data[:] ['created_at']) = 0.44 np.random.seed (33) #from the skeleton code, randomly adding nan dropout = 0.1 intensity = ['valence_intensity', 'anger_intensity', 'fear_intensity', 'sadness_intensity', 'joy_intensity'] for val in intensity: data[:] [val][np.random.choice(data[:][val].size, int(dropout * data[:][val].size), replace=False)] = np.nan print (data[:]['valence_intensity'][12]) data = replace_nan(data) print (round(data[:]['valence_intensity'][12], 2))

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