{"id":9664,"date":"2025-10-11T21:51:11","date_gmt":"2025-10-11T16:51:11","guid":{"rendered":"https:\/\/rfaqs.com\/?p=9664"},"modified":"2025-10-11T21:54:10","modified_gmt":"2025-10-11T16:54:10","slug":"statistics-using-python-mcqs-quiz-16","status":"publish","type":"post","link":"https:\/\/rfaqs.com\/python-quizzes\/statistics-using-python-mcqs-quiz-16\/","title":{"rendered":"Statistics using Python MCQs 16"},"content":{"rendered":"\n<p>Test your skills with this 20-question quiz on <strong>Statistics using Python MCQs<\/strong>. Master key concepts like pandas <code>describe()<\/code>, data normalization, LinearRegression, and Pearson Correlation. Perfect for <a href=\"https:\/\/gmstat.com\/data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">data science<\/a> interviews and beginners for the preparation of <a href=\"https:\/\/rfaqs.com\/python-quizzes\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python Programming<\/a>. Topics include handling missing values, <code>get_dummies()<\/code>, <code>groupby()<\/code>, <a href=\"https:\/\/itfeature.com\/corr-ana\/\" target=\"_blank\" rel=\"noreferrer noopener\">correlation<\/a>, and <a href=\"https:\/\/itfeature.com\/regression\/\" target=\"_blank\" rel=\"noreferrer noopener\">regression<\/a>. Let us start with the Statistics using Python MCQs now.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/rfaqs.com\/wp-content\/uploads\/2025\/10\/Statistics-using-Python-MCQs.jpg\"><img decoding=\"async\" width=\"649\" height=\"54\" src=\"https:\/\/rfaqs.com\/wp-content\/uploads\/2025\/10\/Statistics-using-Python-MCQs.jpg\" alt=\"Online Statistics using Python MCQs with Answers\" class=\"wp-image-9666\" srcset=\"https:\/\/rfaqs.com\/wp-content\/uploads\/2025\/10\/Statistics-using-Python-MCQs.jpg 649w, https:\/\/rfaqs.com\/wp-content\/uploads\/2025\/10\/Statistics-using-Python-MCQs-300x25.jpg 300w\" sizes=\"(max-width: 649px) 100vw, 649px\" \/><\/a><\/figure>\n<\/div>\n\n\n<script>\n  window.fbAsyncInit = function() {\n    FB.init({\n      appId            : '428441357660992',\n      autoLogAppEvents : true,\n      xfbml            : true,\n      version          : 'v3.1'\n    });\n  };\n\n  (function(d, s, id){\n     var js, fjs = d.getElementsByTagName(s)[0];\n     if (d.getElementById(id)) {return;}\n     js = d.createElement(s); js.id = id;\n     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n     fjs.parentNode.insertBefore(js, fjs);\n   }(document, 'script', 'facebook-jssdk'));\n<\/script><div id=\"watu_quiz\" class=\"quiz-area \">\n<p><p>Only Multiple Choice Questions about Data Analysis in Python Programming<\/p>\n<\/p><form action=\"\" method=\"post\" class=\"quiz-form \" id=\"quiz-50\" onsubmit=\"return Watu.submitResult(this)\">\n<div class='watu-question' id='question-1'><div class='question-content'><p><span class='watu_num'>1. <\/span>What is the maximum value of $R^2$ that you can obtain?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='980' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4224' \/><div class='watu-question-choice'><input type='radio' name='answer-980[]' id='answer-id-4224' class='answer answer-1  answerof-980' value='4224' \/>&nbsp;<label for='answer-id-4224' id='answer-label-4224' class=' answer label-1'><span class='answer'>Any positive number<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4222' \/><div class='watu-question-choice'><input type='radio' name='answer-980[]' id='answer-id-4222' class='answer answer-1  answerof-980' value='4222' \/>&nbsp;<label for='answer-id-4222' id='answer-label-4222' class=' answer label-1'><span class='answer'>10<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4223' \/><div class='watu-question-choice'><input type='radio' name='answer-980[]' id='answer-id-4223' class='answer answer-1  answerof-980' value='4223' \/>&nbsp;<label for='answer-id-4223' id='answer-label-4223' class=' answer label-1'><span class='answer'>0<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4225' \/><div class='watu-question-choice'><input type='radio' name='answer-980[]' id='answer-id-4225' class='answer answer-1  answerof-980' value='4225' \/>&nbsp;<label for='answer-id-4225' id='answer-label-4225' class=' answer label-1'><span class='answer'>1<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType1' value='radio' class=''><\/div><div class='watu-question' id='question-2'><div class='question-content'><p><span class='watu_num'>2. <\/span>Which of the following is the primary purpose of the <code>get_dummies()<\/code> method?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='965' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4155' \/><div class='watu-question-choice'><input type='radio' name='answer-965[]' id='answer-id-4155' class='answer answer-2  answerof-965' value='4155' \/>&nbsp;<label for='answer-id-4155' id='answer-label-4155' class=' answer label-2'><span class='answer'>Converts numerical values into categorical ones<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4156' \/><div class='watu-question-choice'><input type='radio' name='answer-965[]' id='answer-id-4156' class='answer answer-2  answerof-965' value='4156' \/>&nbsp;<label for='answer-id-4156' id='answer-label-4156' class=' answer label-2'><span class='answer'>To help you group your data into bins<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4158' \/><div class='watu-question-choice'><input type='radio' name='answer-965[]' id='answer-id-4158' class='answer answer-2  answerof-965' value='4158' \/>&nbsp;<label for='answer-id-4158' id='answer-label-4158' class=' answer label-2'><span class='answer'>Converts categorical values into numerical one<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4157' \/><div class='watu-question-choice'><input type='radio' name='answer-965[]' id='answer-id-4157' class='answer answer-2  answerof-965' value='4157' \/>&nbsp;<label for='answer-id-4157' id='answer-label-4157' class=' answer label-2'><span class='answer'>Converts the data&#8217;s data type<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType2' value='radio' class=''><\/div><div class='watu-question' id='question-3'><div class='question-content'><p><span class='watu_num'>3. <\/span>Which of the following steps are generally involved in performing exploratory data analysis (EDA)?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='977' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4209' \/><div class='watu-question-choice'><input type='checkbox' name='answer-977[]' id='answer-id-4209' class='answer answer-3  answerof-977' value='4209' \/>&nbsp;<label for='answer-id-4209' id='answer-label-4209' class=' answer label-3'><span class='answer'>Obtaining the shape of the data<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4213' \/><div class='watu-question-choice'><input type='checkbox' name='answer-977[]' id='answer-id-4213' class='answer answer-3  answerof-977' value='4213' \/>&nbsp;<label for='answer-id-4213' id='answer-label-4213' class=' answer label-3'><span class='answer'>Visualizing data distributions<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4210' \/><div class='watu-question-choice'><input type='checkbox' name='answer-977[]' id='answer-id-4210' class='answer answer-3  answerof-977' value='4210' \/>&nbsp;<label for='answer-id-4210' id='answer-label-4210' class=' answer label-3'><span class='answer'>Preparing data for a sequential model<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4212' \/><div class='watu-question-choice'><input type='checkbox' name='answer-977[]' id='answer-id-4212' class='answer answer-3  answerof-977' value='4212' \/>&nbsp;<label for='answer-id-4212' id='answer-label-4212' class=' answer label-3'><span class='answer'>Generating descriptive statistics<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4211' \/><div class='watu-question-choice'><input type='checkbox' name='answer-977[]' id='answer-id-4211' class='answer answer-3  answerof-977' value='4211' \/>&nbsp;<label for='answer-id-4211' id='answer-label-4211' class=' answer label-3'><span class='answer'>Running a full model training process<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType3' value='checkbox' class=''><\/div><div class='watu-question' id='question-4'><div class='question-content'><p><span class='watu_num'>4. <\/span>What is the primary purpose of data normalization?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='964' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4152' \/><div class='watu-question-choice'><input type='radio' name='answer-964[]' id='answer-id-4152' class='answer answer-4  answerof-964' value='4152' \/>&nbsp;<label for='answer-id-4152' id='answer-label-4152' class=' answer label-4'><span class='answer'>To ensure features have similar ranges for fair comparison<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4153' \/><div class='watu-question-choice'><input type='radio' name='answer-964[]' id='answer-id-4153' class='answer answer-4  answerof-964' value='4153' \/>&nbsp;<label for='answer-id-4153' id='answer-label-4153' class=' answer label-4'><span class='answer'>To eliminate outliers and incorrect values<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4154' \/><div class='watu-question-choice'><input type='radio' name='answer-964[]' id='answer-id-4154' class='answer answer-4  answerof-964' value='4154' \/>&nbsp;<label for='answer-id-4154' id='answer-label-4154' class=' answer label-4'><span class='answer'>To remove missing values from the dataset<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4151' \/><div class='watu-question-choice'><input type='radio' name='answer-964[]' id='answer-id-4151' class='answer answer-4  answerof-964' value='4151' \/>&nbsp;<label for='answer-id-4151' id='answer-label-4151' class=' answer label-4'><span class='answer'>To make all features identical in value<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType4' value='radio' class=''><\/div><div class='watu-question' id='question-5'><div class='question-content'><p><span class='watu_num'>5. <\/span>What is the function of the MinMaxScaler when applied to a column of data?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='974' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4197' \/><div class='watu-question-choice'><input type='radio' name='answer-974[]' id='answer-id-4197' class='answer answer-5  answerof-974' value='4197' \/>&nbsp;<label for='answer-id-4197' id='answer-label-4197' class=' answer label-5'><span class='answer'>Normalizes data to have a mean of 0<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4196' \/><div class='watu-question-choice'><input type='radio' name='answer-974[]' id='answer-id-4196' class='answer answer-5  answerof-974' value='4196' \/>&nbsp;<label for='answer-id-4196' id='answer-label-4196' class=' answer label-5'><span class='answer'>Replaces NaN values in the column<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4199' \/><div class='watu-question-choice'><input type='radio' name='answer-974[]' id='answer-id-4199' class='answer answer-5  answerof-974' value='4199' \/>&nbsp;<label for='answer-id-4199' id='answer-label-4199' class=' answer label-5'><span class='answer'>Scales data to be between 0 and 1<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4198' \/><div class='watu-question-choice'><input type='radio' name='answer-974[]' id='answer-id-4198' class='answer answer-5  answerof-974' value='4198' \/>&nbsp;<label for='answer-id-4198' id='answer-label-4198' class=' answer label-5'><span class='answer'>Drops duplicate values in the column<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType5' value='radio' class=''><\/div><div class='watu-question' id='question-6'><div class='question-content'><p><span class='watu_num'>6. <\/span>Which of the following methods is used to calculate the average delivery time in a pandas DataFrame containing time delta columns?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='971' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4185' \/><div class='watu-question-choice'><input type='radio' name='answer-971[]' id='answer-id-4185' class='answer answer-6  answerof-971' value='4185' \/>&nbsp;<label for='answer-id-4185' id='answer-label-4185' class=' answer label-6'><span class='answer'>max()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4184' \/><div class='watu-question-choice'><input type='radio' name='answer-971[]' id='answer-id-4184' class='answer answer-6  answerof-971' value='4184' \/>&nbsp;<label for='answer-id-4184' id='answer-label-4184' class=' answer label-6'><span class='answer'>mean()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4186' \/><div class='watu-question-choice'><input type='radio' name='answer-971[]' id='answer-id-4186' class='answer answer-6  answerof-971' value='4186' \/>&nbsp;<label for='answer-id-4186' id='answer-label-4186' class=' answer label-6'><span class='answer'>sum()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4183' \/><div class='watu-question-choice'><input type='radio' name='answer-971[]' id='answer-id-4183' class='answer answer-6  answerof-971' value='4183' \/>&nbsp;<label for='answer-id-4183' id='answer-label-4183' class=' answer label-6'><span class='answer'>min()<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType6' value='radio' class=''><\/div><div class='watu-question' id='question-7'><div class='question-content'><p><span class='watu_num'>7. <\/span>Which function provides summary statistics, including mean and count, for a numeric column in a DataFrame?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='973' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4193' \/><div class='watu-question-choice'><input type='radio' name='answer-973[]' id='answer-id-4193' class='answer answer-7  answerof-973' value='4193' \/>&nbsp;<label for='answer-id-4193' id='answer-label-4193' class=' answer label-7'><span class='answer'>df.isnull()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4195' \/><div class='watu-question-choice'><input type='radio' name='answer-973[]' id='answer-id-4195' class='answer answer-7  answerof-973' value='4195' \/>&nbsp;<label for='answer-id-4195' id='answer-label-4195' class=' answer label-7'><span class='answer'>df.describe()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4192' \/><div class='watu-question-choice'><input type='radio' name='answer-973[]' id='answer-id-4192' class='answer answer-7  answerof-973' value='4192' \/>&nbsp;<label for='answer-id-4192' id='answer-label-4192' class=' answer label-7'><span class='answer'>df.fillna()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4194' \/><div class='watu-question-choice'><input type='radio' name='answer-973[]' id='answer-id-4194' class='answer answer-7  answerof-973' value='4194' \/>&nbsp;<label for='answer-id-4194' id='answer-label-4194' class=' answer label-7'><span class='answer'>df.dropna()<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType7' value='radio' class=''><\/div><div class='watu-question' id='question-8'><div class='question-content'><p><span class='watu_num'>8. <\/span>What does the following line of code do? <code>lm = LinearRegression()<\/code><\/p>\n<\/div><input type='hidden' name='question_id[]' value='970' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4182' \/><div class='watu-question-choice'><input type='radio' name='answer-970[]' id='answer-id-4182' class='answer answer-8  answerof-970' value='4182' \/>&nbsp;<label for='answer-id-4182' id='answer-label-4182' class=' answer label-8'><span class='answer'>Fits a regression object to the variable lm<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4181' \/><div class='watu-question-choice'><input type='radio' name='answer-970[]' id='answer-id-4181' class='answer answer-8  answerof-970' value='4181' \/>&nbsp;<label for='answer-id-4181' id='answer-label-4181' class=' answer label-8'><span class='answer'>Creates a linear regression object and stores it in the lm variable<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4180' \/><div class='watu-question-choice'><input type='radio' name='answer-970[]' id='answer-id-4180' class='answer answer-8  answerof-970' value='4180' \/>&nbsp;<label for='answer-id-4180' id='answer-label-4180' class=' answer label-8'><span class='answer'>Assigns a linear regression model to the lm variable<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4179' \/><div class='watu-question-choice'><input type='radio' name='answer-970[]' id='answer-id-4179' class='answer answer-8  answerof-970' value='4179' \/>&nbsp;<label for='answer-id-4179' id='answer-label-4179' class=' answer label-8'><span class='answer'>Predicts output values of a linear regression object<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType8' value='radio' class=''><\/div><div class='watu-question' id='question-9'><div class='question-content'><p><span class='watu_num'>9. <\/span>Consider the following data frame: <code>df_test = df[['body-style,' 'price']]<\/code>. The following operation is applied:<br \/>\n<code>df_grp = df_test.groupby(['body-style'], as_index=False).mean()<\/code><br \/>\nWhat are the resulting values of: <code>df_grp['price']<\/code>?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='968' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4169' \/><div class='watu-question-choice'><input type='radio' name='answer-968[]' id='answer-id-4169' class='answer answer-9  answerof-968' value='4169' \/>&nbsp;<label for='answer-id-4169' id='answer-label-4169' class=' answer label-9'><span class='answer'>It averages the body-style variable data values<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4167' \/><div class='watu-question-choice'><input type='radio' name='answer-968[]' id='answer-id-4167' class='answer answer-9  answerof-968' value='4167' \/>&nbsp;<label for='answer-id-4167' id='answer-label-4167' class=' answer label-9'><span class='answer'>It writes the mean value of each body style price to the data frame<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4168' \/><div class='watu-question-choice'><input type='radio' name='answer-968[]' id='answer-id-4168' class='answer answer-9  answerof-968' value='4168' \/>&nbsp;<label for='answer-id-4168' id='answer-label-4168' class=' answer label-9'><span class='answer'>The average price<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4170' \/><div class='watu-question-choice'><input type='radio' name='answer-968[]' id='answer-id-4170' class='answer answer-9  answerof-968' value='4170' \/>&nbsp;<label for='answer-id-4170' id='answer-label-4170' class=' answer label-9'><span class='answer'>It averages the price for each body style<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType9' value='radio' class=''><\/div><div class='watu-question' id='question-10'><div class='question-content'><p><span class='watu_num'>10. <\/span>Which of the following are valid aggregation methods that can be applied to groups in a pandas DataFrame?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='972' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4188' \/><div class='watu-question-choice'><input type='checkbox' name='answer-972[]' id='answer-id-4188' class='answer answer-10  answerof-972' value='4188' \/>&nbsp;<label for='answer-id-4188' id='answer-label-4188' class=' answer label-10'><span class='answer'>average<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4187' \/><div class='watu-question-choice'><input type='checkbox' name='answer-972[]' id='answer-id-4187' class='answer answer-10  answerof-972' value='4187' \/>&nbsp;<label for='answer-id-4187' id='answer-label-4187' class=' answer label-10'><span class='answer'>min<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4190' \/><div class='watu-question-choice'><input type='checkbox' name='answer-972[]' id='answer-id-4190' class='answer answer-10  answerof-972' value='4190' \/>&nbsp;<label for='answer-id-4190' id='answer-label-4190' class=' answer label-10'><span class='answer'>max<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4189' \/><div class='watu-question-choice'><input type='checkbox' name='answer-972[]' id='answer-id-4189' class='answer answer-10  answerof-972' value='4189' \/>&nbsp;<label for='answer-id-4189' id='answer-label-4189' class=' answer label-10'><span class='answer'>sum<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4191' \/><div class='watu-question-choice'><input type='checkbox' name='answer-972[]' id='answer-id-4191' class='answer answer-10  answerof-972' value='4191' \/>&nbsp;<label for='answer-id-4191' id='answer-label-4191' class=' answer label-10'><span class='answer'>collect<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType10' value='checkbox' class=''><\/div><div class='watu-question' id='question-11'><div class='question-content'><p><span class='watu_num'>11. <\/span>When compiling a regression model, which of the following elements must be defined? Select all that apply.<\/p>\n<\/div><input type='hidden' name='question_id[]' value='975' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4204' \/><div class='watu-question-choice'><input type='checkbox' name='answer-975[]' id='answer-id-4204' class='answer answer-11  answerof-975' value='4204' \/>&nbsp;<label for='answer-id-4204' id='answer-label-4204' class=' answer label-11'><span class='answer'>Model architecture<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4200' \/><div class='watu-question-choice'><input type='checkbox' name='answer-975[]' id='answer-id-4200' class='answer answer-11  answerof-975' value='4200' \/>&nbsp;<label for='answer-id-4200' id='answer-label-4200' class=' answer label-11'><span class='answer'>Loss function<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4201' \/><div class='watu-question-choice'><input type='checkbox' name='answer-975[]' id='answer-id-4201' class='answer answer-11  answerof-975' value='4201' \/>&nbsp;<label for='answer-id-4201' id='answer-label-4201' class=' answer label-11'><span class='answer'>Optimizer<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4203' \/><div class='watu-question-choice'><input type='checkbox' name='answer-975[]' id='answer-id-4203' class='answer answer-11  answerof-975' value='4203' \/>&nbsp;<label for='answer-id-4203' id='answer-label-4203' class=' answer label-11'><span class='answer'>Batch size<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4202' \/><div class='watu-question-choice'><input type='checkbox' name='answer-975[]' id='answer-id-4202' class='answer answer-11  answerof-975' value='4202' \/>&nbsp;<label for='answer-id-4202' id='answer-label-4202' class=' answer label-11'><span class='answer'>Validation metric<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType11' value='checkbox' class=''><\/div><div class='watu-question' id='question-12'><div class='question-content'><p><span class='watu_num'>12. <\/span>What range of Pearson Coefficient \u2018p\u2019 is considered too high to support any certainty about the correlation of variables?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='978' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4214' \/><div class='watu-question-choice'><input type='radio' name='answer-978[]' id='answer-id-4214' class='answer answer-12  answerof-978' value='4214' \/>&nbsp;<label for='answer-id-4214' id='answer-label-4214' class=' answer label-12'><span class='answer'>0.05 &lt; p &lt; 0.1<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4216' \/><div class='watu-question-choice'><input type='radio' name='answer-978[]' id='answer-id-4216' class='answer answer-12  answerof-978' value='4216' \/>&nbsp;<label for='answer-id-4216' id='answer-label-4216' class=' answer label-12'><span class='answer'>p &lt; 0.001<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4217' \/><div class='watu-question-choice'><input type='radio' name='answer-978[]' id='answer-id-4217' class='answer answer-12  answerof-978' value='4217' \/>&nbsp;<label for='answer-id-4217' id='answer-label-4217' class=' answer label-12'><span class='answer'>0.001 &lt; p &lt; 0.05<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4215' \/><div class='watu-question-choice'><input type='radio' name='answer-978[]' id='answer-id-4215' class='answer answer-12  answerof-978' value='4215' \/>&nbsp;<label for='answer-id-4215' id='answer-label-4215' class=' answer label-12'><span class='answer'>p &gt; 0.1<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType12' value='radio' class=''><\/div><div class='watu-question' id='question-13'><div class='question-content'><p><span class='watu_num'>13. <\/span>How do you generate descriptive statistics for all the columns of the data frame df?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='962' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4145' \/><div class='watu-question-choice'><input type='radio' name='answer-962[]' id='answer-id-4145' class='answer answer-13  answerof-962' value='4145' \/>&nbsp;<label for='answer-id-4145' id='answer-label-4145' class=' answer label-13'><span class='answer'>df.describe(include = &#8220;all&#8221;)<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4144' \/><div class='watu-question-choice'><input type='radio' name='answer-962[]' id='answer-id-4144' class='answer answer-13  answerof-962' value='4144' \/>&nbsp;<label for='answer-id-4144' id='answer-label-4144' class=' answer label-13'><span class='answer'>df.describe()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4143' \/><div class='watu-question-choice'><input type='radio' name='answer-962[]' id='answer-id-4143' class='answer answer-13  answerof-962' value='4143' \/>&nbsp;<label for='answer-id-4143' id='answer-label-4143' class=' answer label-13'><span class='answer'>df.statistics(include = &#8220;all&#8221;)<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4146' \/><div class='watu-question-choice'><input type='radio' name='answer-962[]' id='answer-id-4146' class='answer answer-13  answerof-962' value='4146' \/>&nbsp;<label for='answer-id-4146' id='answer-label-4146' class=' answer label-13'><span class='answer'>df.info<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType13' value='radio' class=''><\/div><div class='watu-question' id='question-14'><div class='question-content'><p><span class='watu_num'>14. <\/span>Consider the following lines of code. What value does the variable out contain?<br \/>\n<code>lm = LinearRegression()<br \/>\nX = df[['highway-mpg']]<br \/>\nY = df['price']<br \/>\nlm.fit(X, Y)<br \/>\nout=lm.score(X,Y)<\/code><\/p>\n<\/div><input type='hidden' name='question_id[]' value='969' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4171' \/><div class='watu-question-choice'><input type='radio' name='answer-969[]' id='answer-id-4171' class='answer answer-14  answerof-969' value='4171' \/>&nbsp;<label for='answer-id-4171' id='answer-label-4171' class=' answer label-14'><span class='answer'>A multiple linear regression<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4174' \/><div class='watu-question-choice'><input type='radio' name='answer-969[]' id='answer-id-4174' class='answer answer-14  answerof-969' value='4174' \/>&nbsp;<label for='answer-id-4174' id='answer-label-4174' class=' answer label-14'><span class='answer'>Mean Square Error with respect to y<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4172' \/><div class='watu-question-choice'><input type='radio' name='answer-969[]' id='answer-id-4172' class='answer answer-14  answerof-969' value='4172' \/>&nbsp;<label for='answer-id-4172' id='answer-label-4172' class=' answer label-14'><span class='answer'>The Coefficient of Determination<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4173' \/><div class='watu-question-choice'><input type='radio' name='answer-969[]' id='answer-id-4173' class='answer answer-14  answerof-969' value='4173' \/>&nbsp;<label for='answer-id-4173' id='answer-label-4173' class=' answer label-14'><span class='answer'>Mean Squared Error with respect to X<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType14' value='radio' class=''><\/div><div class='watu-question' id='question-15'><div class='question-content'><p><span class='watu_num'>15. <\/span>What steps are involved in preparing a dataset for exploratory analysis?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='976' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4206' \/><div class='watu-question-choice'><input type='checkbox' name='answer-976[]' id='answer-id-4206' class='answer answer-15  answerof-976' value='4206' \/>&nbsp;<label for='answer-id-4206' id='answer-label-4206' class=' answer label-15'><span class='answer'>Hyperparameter tuning<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4208' \/><div class='watu-question-choice'><input type='checkbox' name='answer-976[]' id='answer-id-4208' class='answer answer-15  answerof-976' value='4208' \/>&nbsp;<label for='answer-id-4208' id='answer-label-4208' class=' answer label-15'><span class='answer'>Data transformation<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4207' \/><div class='watu-question-choice'><input type='checkbox' name='answer-976[]' id='answer-id-4207' class='answer answer-15  answerof-976' value='4207' \/>&nbsp;<label for='answer-id-4207' id='answer-label-4207' class=' answer label-15'><span class='answer'>Model training<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4205' \/><div class='watu-question-choice'><input type='checkbox' name='answer-976[]' id='answer-id-4205' class='answer answer-15  answerof-976' value='4205' \/>&nbsp;<label for='answer-id-4205' id='answer-label-4205' class=' answer label-15'><span class='answer'>Data Cleaning<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType15' value='checkbox' class=''><\/div><div class='watu-question' id='question-16'><div class='question-content'><p><span class='watu_num'>16. <\/span>What method provides summary statistics of a data frame?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='966' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4161' \/><div class='watu-question-choice'><input type='radio' name='answer-966[]' id='answer-id-4161' class='answer answer-16  answerof-966' value='4161' \/>&nbsp;<label for='answer-id-4161' id='answer-label-4161' class=' answer label-16'><span class='answer'>summary()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4159' \/><div class='watu-question-choice'><input type='radio' name='answer-966[]' id='answer-id-4159' class='answer answer-16  answerof-966' value='4159' \/>&nbsp;<label for='answer-id-4159' id='answer-label-4159' class=' answer label-16'><span class='answer'>tail()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4162' \/><div class='watu-question-choice'><input type='radio' name='answer-966[]' id='answer-id-4162' class='answer answer-16  answerof-966' value='4162' \/>&nbsp;<label for='answer-id-4162' id='answer-label-4162' class=' answer label-16'><span class='answer'>head()<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4160' \/><div class='watu-question-choice'><input type='radio' name='answer-966[]' id='answer-id-4160' class='answer answer-16  answerof-966' value='4160' \/>&nbsp;<label for='answer-id-4160' id='answer-label-4160' class=' answer label-16'><span class='answer'>describe()<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType16' value='radio' class=''><\/div><div class='watu-question' id='question-17'><div class='question-content'><p><span class='watu_num'>17. <\/span>Which of the following methods should you use to replace a missing value of an attribute with continuous values?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='963' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4149' \/><div class='watu-question-choice'><input type='radio' name='answer-963[]' id='answer-id-4149' class='answer answer-17  answerof-963' value='4149' \/>&nbsp;<label for='answer-id-4149' id='answer-label-4149' class=' answer label-17'><span class='answer'>Use the difference between the minimum and maximum values of the other data in the column<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4150' \/><div class='watu-question-choice'><input type='radio' name='answer-963[]' id='answer-id-4150' class='answer answer-17  answerof-963' value='4150' \/>&nbsp;<label for='answer-id-4150' id='answer-label-4150' class=' answer label-17'><span class='answer'>Use an educated guess<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4147' \/><div class='watu-question-choice'><input type='radio' name='answer-963[]' id='answer-id-4147' class='answer answer-17  answerof-963' value='4147' \/>&nbsp;<label for='answer-id-4147' id='answer-label-4147' class=' answer label-17'><span class='answer'>Use the average of the other values in the column<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4148' \/><div class='watu-question-choice'><input type='radio' name='answer-963[]' id='answer-id-4148' class='answer answer-17  answerof-963' value='4148' \/>&nbsp;<label for='answer-id-4148' id='answer-label-4148' class=' answer label-17'><span class='answer'>Use the mean square error of the other data in the column<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType17' value='radio' class=''><\/div><div class='watu-question' id='question-18'><div class='question-content'><p><span class='watu_num'>18. <\/span>As the Pearson Correlation value nears zero, then<\/p>\n<\/div><input type='hidden' name='question_id[]' value='967' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4166' \/><div class='watu-question-choice'><input type='radio' name='answer-967[]' id='answer-id-4166' class='answer answer-18  answerof-967' value='4166' \/>&nbsp;<label for='answer-id-4166' id='answer-label-4166' class=' answer label-18'><span class='answer'>It indicates uncertainty about the correlation between two variables<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4165' \/><div class='watu-question-choice'><input type='radio' name='answer-967[]' id='answer-id-4165' class='answer answer-18  answerof-967' value='4165' \/>&nbsp;<label for='answer-id-4165' id='answer-label-4165' class=' answer label-18'><span class='answer'>It indicates minimal deviation in a variable&#8217;s values from the mean<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4164' \/><div class='watu-question-choice'><input type='radio' name='answer-967[]' id='answer-id-4164' class='answer answer-18  answerof-967' value='4164' \/>&nbsp;<label for='answer-id-4164' id='answer-label-4164' class=' answer label-18'><span class='answer'>It indicates the mean of the data is near zero<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4163' \/><div class='watu-question-choice'><input type='radio' name='answer-967[]' id='answer-id-4163' class='answer answer-18  answerof-967' value='4163' \/>&nbsp;<label for='answer-id-4163' id='answer-label-4163' class=' answer label-18'><span class='answer'>It indicates that two variables are not correlated<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType18' value='radio' class=''><\/div><div class='watu-question' id='question-19'><div class='question-content'><p><span class='watu_num'>19. <\/span>What is the Pearson Correlation between two variables if the input variable is equal to the output variable?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='979' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4218' \/><div class='watu-question-choice'><input type='radio' name='answer-979[]' id='answer-id-4218' class='answer answer-19  answerof-979' value='4218' \/>&nbsp;<label for='answer-id-4218' id='answer-label-4218' class=' answer label-19'><span class='answer'>Between 0 and 1<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4219' \/><div class='watu-question-choice'><input type='radio' name='answer-979[]' id='answer-id-4219' class='answer answer-19  answerof-979' value='4219' \/>&nbsp;<label for='answer-id-4219' id='answer-label-4219' class=' answer label-19'><span class='answer'>-1<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4221' \/><div class='watu-question-choice'><input type='radio' name='answer-979[]' id='answer-id-4221' class='answer answer-19  answerof-979' value='4221' \/>&nbsp;<label for='answer-id-4221' id='answer-label-4221' class=' answer label-19'><span class='answer'>1<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4220' \/><div class='watu-question-choice'><input type='radio' name='answer-979[]' id='answer-id-4220' class='answer answer-19  answerof-979' value='4220' \/>&nbsp;<label for='answer-id-4220' id='answer-label-4220' class=' answer label-19'><span class='answer'>Between -1 and 0<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType19' value='radio' class=''><\/div><div class='watu-question' id='question-20'><div class='question-content'><p><span class='watu_num'>20. <\/span>How would you use the describe() method with a data frame df to get a statistical summary of all the columns in the data frame?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='961' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4140' \/><div class='watu-question-choice'><input type='radio' name='answer-961[]' id='answer-id-4140' class='answer answer-20  answerof-961' value='4140' \/>&nbsp;<label for='answer-id-4140' id='answer-label-4140' class=' answer label-20'><span class='answer'>df.describe(include=\u201ccolumns\u201d)<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4141' \/><div class='watu-question-choice'><input type='radio' name='answer-961[]' id='answer-id-4141' class='answer answer-20  answerof-961' value='4141' \/>&nbsp;<label for='answer-id-4141' id='answer-label-4141' class=' answer label-20'><span class='answer'>df.describe(include=\u201csummary\u201d)<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4142' \/><div class='watu-question-choice'><input type='radio' name='answer-961[]' id='answer-id-4142' class='answer answer-20  answerof-961' value='4142' \/>&nbsp;<label for='answer-id-4142' id='answer-label-4142' class=' answer label-20'><span class='answer'>df.describe(include=&#8221;all&#8221;)<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='4139' \/><div class='watu-question-choice'><input type='radio' name='answer-961[]' id='answer-id-4139' class='answer answer-20  answerof-961' value='4139' \/>&nbsp;<label for='answer-id-4139' id='answer-label-4139' class=' answer label-20'><span class='answer'>df.describe(include=\u201cNone\u201d)<\/span><\/label><\/div>\n<\/div><input type='hidden' id='questionType20' value='radio' class=''><\/div><div style='display:none' id='question-21'><br \/><div class='question-content'><img decoding=\"async\" src=\"https:\/\/rfaqs.com\/wp-content\/plugins\/watu\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading ...\" title=\"Loading ...\" \/>&nbsp;Loading &#8230;<\/div><\/div><br \/>\n\t<p>Question <span id='numQ'>1<\/span> of 20<\/p>\n\t\t\t<input type=\"button\" id=\"prev-question\" value=\"&lt; Previous\" onclick=\"Watu.nextQuestion(event, 'prev');\" style=\"display:none;\" \/>\n\t\t<input type=\"button\" id=\"next-question\" value=\"Next &gt;\"  \/>\n\t<input type=\"submit\" name=\"submit_no_ajax\" id=\"action-button\" class=\"watu-submit-button\" value=\"Submit\" \/>\n<input type=\"hidden\" name=\"no_ajax\" value=\"1\"><input type=\"hidden\" name=\"do\" value=\"show_exam_result\">\n<input type=\"hidden\" name=\"post_id\" value=\"9664\">\n<input type=\"hidden\" name=\"quiz_id\" value=\"50\" \/>\n<input type=\"hidden\" id=\"watuStartTime\" name=\"start_time\" value=\"2026-04-21 19:05:45\" \/>\n<\/form>\n<\/div>\n<div id=\"watu-loading-result\" style=\"display:none;\">\n\t<p align=\"center\"><img decoding=\"async\" src=\"https:\/\/rfaqs.com\/wp-content\/plugins\/watu\/loading.gif\" 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replace a missing value of an attribute with continuous values?<\/li>\n\n\n\n<li>What is the primary purpose of data normalization?<\/li>\n\n\n\n<li>Which of the following is the primary purpose of the get_dummies() method?<\/li>\n\n\n\n<li>What method provides summary statistics of a data frame?<\/li>\n\n\n\n<li>As the Pearson Correlation value nears zero, then<\/li>\n\n\n\n<li>Consider the following data frame: df_test = df[[&#8216;body-style,&#8217; &#8216;price&#8217;]]. The following operation is applied: <br>df_grp = df_test.groupby([&#8216;body-style&#8217;], as_index=False).mean() <br>What are the resulting values of: df_grp[&#8216;price&#8217;]?<\/li>\n\n\n\n<li>Consider the following lines of code. What value does the variable out contain? <br>lm = LinearRegression() <br>X = df[[&#8216;highway-mpg&#8217;]] <br>Y = df[&#8216;price&#8217;] lm.fit(X, Y) <br>out=lm.score(X,Y)<\/li>\n\n\n\n<li>What does the following line of code do? lm = LinearRegression()<\/li>\n\n\n\n<li>Which of the following methods is used to calculate the average delivery time in a pandas DataFrame containing time delta columns?<\/li>\n\n\n\n<li>Which of the following are valid aggregation methods that can be applied to groups in a pandas DataFrame?<\/li>\n\n\n\n<li>Which function provides summary statistics, including mean and count, for a numeric column in a DataFrame?<\/li>\n\n\n\n<li>What is the function of the MinMaxScaler when applied to a column of data?<\/li>\n\n\n\n<li>When compiling a regression model, which of the following elements must be defined? Select all that apply.<\/li>\n\n\n\n<li>What steps are involved in preparing a dataset for exploratory analysis?<\/li>\n\n\n\n<li>Which of the following steps are generally involved in performing exploratory data analysis (EDA)?<\/li>\n\n\n\n<li>What range of Pearson Coefficient \u2018p\u2019 is considered too high to support any certainty about the correlation of variables?<\/li>\n\n\n\n<li>What is the Pearson Correlation between two variables if the input variable is equal to the output variable?<\/li>\n\n\n\n<li>What is the maximum value of $R^2$ that you can obtain?<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center\"><a href=\"https:\/\/gmstat.com\/gre\/analogies\/\" target=\"_blank\" rel=\"noreferrer noopener\">Online GRE Analogies Quizzes<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Test your skills with this 20-question quiz on Statistics using Python MCQs. Master key concepts like pandas describe(), data normalization, LinearRegression, and Pearson Correlation. Perfect for data science interviews and beginners for the preparation of Python Programming. Topics include handling &#8230; <a title=\"Statistics using Python MCQs 16\" class=\"read-more\" href=\"https:\/\/rfaqs.com\/python-quizzes\/statistics-using-python-mcqs-quiz-16\/\" aria-label=\"Read more about Statistics using Python MCQs 16\">Read more<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_publicize_message":"Test your skills with this 20-question quiz on Statistics using Python MCQs. Master key concepts like pandas describe(), data normalization, LinearRegression, and Pearson Correlation. Perfect for data science interviews and beginners.\n#Pythonprogrammingquiz #pythonquiz #pythonmcqs #pythonpandasquiz","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[159],"tags":[],"class_list":["post-9664","post","type-post","status-publish","format-standard","hentry","category-python-quizzes"],"jetpack_publicize_connections":[],"_links":{"self":[{"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/posts\/9664","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/comments?post=9664"}],"version-history":[{"count":0,"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/posts\/9664\/revisions"}],"wp:attachment":[{"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/media?parent=9664"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/categories?post=9664"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rfaqs.com\/wp-json\/wp\/v2\/tags?post=9664"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}