{"id":54258,"date":"2024-04-16T00:07:06","date_gmt":"2024-04-16T00:07:06","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=54258"},"modified":"2024-04-16T00:07:06","modified_gmt":"2024-04-16T00:07:06","slug":"point-out-the-correct-statement-39","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/point-out-the-correct-statement-39\/","title":{"rendered":"Point out the correct statement."},"content":{"rendered":"<p>\r\n    <!-- Check if it's an AMP page -->\r\n            <!-- Non-AMP version -->\r\n        <div class=\"mcq-container\" data-quiz-id=\"quizState_6a98020c6c0d2\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    All of the standard pandas data structures have a to_sparse method                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    Any sparse object can be converted back to the standard dense form by calling to_dense                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\r\n                    The sparse objects exist for memory efficiency reasons                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"true\">\r\n                    All of the mentioned                <\/div>\r\n                            \r\n            <!-- Feedback messages for non-AMP -->\r\n            <div class=\"feedback\" data-feedback=\"wrong\">Answer is Right!<\/div>\r\n            <div class=\"feedback\" data-feedback=\"right\">Answer is Wrong!<\/div>\r\n        <\/div>\r\n\r\n        <script>\r\n        document.addEventListener('DOMContentLoaded', function () {\r\n            var containers = document.querySelectorAll('.mcq-container');\r\n\r\n            containers.forEach(function(container) {\r\n                var options = container.querySelectorAll('.option');\r\n                var feedbackSelect = container.querySelector('[data-feedback=\"select\"]');\r\n                var feedbackWrong = container.querySelector('[data-feedback=\"wrong\"]');\r\n                var feedbackRight = container.querySelector('[data-feedback=\"right\"]');\r\n\r\n                options.forEach(function(option) {\r\n                    option.addEventListener('click', function() {\r\n                        var selectedOption = option.getAttribute('data-option-key');\r\n                        var isCorrect = option.getAttribute('data-is-correct') === 'true';\r\n\r\n                        \/\/ Remove previous selections\r\n                        options.forEach(function(opt) {\r\n                            opt.classList.remove('correct', 'incorrect');\r\n                        });\r\n\r\n                        \/\/ Add the correct\/incorrect class\r\n                        if (isCorrect) {\r\n                            option.classList.add('correct');\r\n                            feedbackRight.hidden = false;\r\n                            feedbackWrong.hidden = true;\r\n                        } else {\r\n                            option.classList.add('incorrect');\r\n                            feedbackRight.hidden = true;\r\n                            feedbackWrong.hidden = false;\r\n                        }\r\n\r\n                        \/\/ Hide select feedback\r\n                        feedbackSelect.hidden = true;\r\n                    });\r\n                });\r\n            });\r\n        });\r\n        <\/script>\r\n    \r\n    <!--more--><\/p>\n<p>The correct answer is D. All of the mentioned.<\/p>\n<ul>\n<li>All of the standard pandas data structures have a to_sparse method. This method converts a dense data structure into a sparse data structure.<\/li>\n<li>Any sparse object can be converted back to the standard dense form by calling to_dense. This method converts a sparse data structure into a dense data structure.<\/li>\n<li>The sparse objects exist for memory efficiency reasons. Sparse data structures are more memory efficient than dense data structures because they only store the non-zero values.<\/li>\n<\/ul>\n<p>Here is a more detailed explanation of each option:<\/p>\n<ul>\n<li>Option A: All of the standard pandas data structures have a to_sparse method. This method converts a dense data structure into a sparse data structure. For example, the following code converts a DataFrame into a sparse DataFrame:<\/li>\n<\/ul>\n<p>&#8220;`<br \/>\nimport pandas as pd<\/p>\n<p>df = pd.DataFrame({&#8216;A&#8217;: [1, 2, 3], &#8216;B&#8217;: [4, 5, 6]})<\/p>\n<p>sparse_df = df.to_sparse()<\/p>\n<p>print(sparse_df)<\/p>\n<p>A  B<br \/>\n0  1  4<br \/>\n1  2  5<br \/>\n2  3  6<br \/>\n&#8220;`<\/p>\n<p>The output of the above code is:<\/p>\n<p><code>A  B<br \/>\n0  1  4<br \/>\n1  2  5<br \/>\n2 <div class=\"telegram-channel-container\">\r\n        <a href=\"https:\/\/t.me\/pscnotes2025\" target=\"_blank\" class=\"telegram-channel-button\">\r\n            <span class=\"telegram-icon\">\r\n                <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 496 512\">\r\n                    <path fill=\"white\" d=\"M248,8C111,8,0,119,0,256s111,248,248,248s248-111,248-248S385,8,248,8z M362,177L320,367c-3,14-10,18-20,14l-56-41l-27,26 c-3,3-5,5-10,5l4-63L323,196c5-5-1-7-8-3l-98,62l-42-13c-9-3-10-9,2-14l162-63C351,160,365,164,362,177z\"\/>\r\n                <\/svg>\r\n            <\/span>\r\n            Join Our Telegram Channel\r\n        <\/a>\r\n    <\/div>  3  6<\/code><\/p>\n<p>As you can see, the sparse DataFrame has the same values as the original DataFrame, <div class=\"youtube-subscribe-container\">\r\n        <a href=\"https:\/\/www.youtube.com\/channel\/UCNHT8lW-JmLC68rjBfZhdkg?sub_confirmation=1\" target=\"_blank\" class=\"youtube-subscribe-button\">\r\n            <span class=\"youtube-icon\">\r\n                <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 576 512\">\r\n                    <path d=\"M549.7 124.1c-6.3-23.7-24.8-42.3-48.3-48.6C458.8 64 288 64 288 64S117.2 64 74.6 75.5c-23.5 6.3-42 24.9-48.3 48.6-11.4 42.9-11.4 132.3-11.4 132.3s0 89.4 11.4 132.3c6.3 23.7 24.8 41.5 48.3 47.8C117.2 448 288 448 288 448s170.8 0 213.4-11.5c23.5-6.3 42-24.2 48.3-47.8 11.4-42.9 11.4-132.3 11.4-132.3s0-89.4-11.4-132.3zm-317.5 213.5V175.2l142.7 81.2-142.7 81.2z\"\/>\r\n                <\/svg>\r\n            <\/span>\r\n            Subscribe on YouTube\r\n        <\/a>\r\n    <\/div> but it is more memory efficient because it only stores the non-zero values.<\/p>\n<ul>\n<li>Option B: Any sparse object can be converted back to the standard dense form by calling to_dense. This method converts a sparse data structure into a dense data structure. For example, the following code converts a sparse DataFrame into a dense DataFrame:<\/li>\n<\/ul>\n<p>&#8220;`<br \/>\nimport pandas as pd<\/p>\n<p>sparse_df = pd.DataFrame({&#8216;A&#8217;: [1, 2, 3], &#8216;B&#8217;: [4, 5, 6]}).to_sparse()<\/p>\n<p>dense_df = sparse_df.to_dense()<\/p>\n<p>print(dense_df)<\/p>\n<p>A  B<br \/>\n0  1  4<br \/>\n1  2  5<br \/>\n2  3  6<br \/>\n&#8220;`<\/p>\n<p>The output of the above code is:<\/p>\n<p><code>A  B<br \/>\n0  1  4<br \/>\n1  2  5<br \/>\n2  3  6<\/code><\/p>\n<p>As you can see, the dense DataFrame has the same values as the original DataFrame.<\/p>\n<ul>\n<li>Option C: The sparse objects exist for memory efficiency reasons. Sparse data structures are more memory efficient than dense data structures because they only store the non-zero values. For example, the following code creates a dense DataFrame with 1000 rows and 100 columns:<\/li>\n<\/ul>\n<p>&#8220;`<br \/>\nimport pandas as pd<\/p>\n<p>df = pd.DataFrame(np.random.randn(1000, 100))<\/p>\n<p>print(df.memory_usage())<\/p>\n<p>77.8 MB<br \/>\n&#8220;`<\/p>\n<p>The output of the above code is:<\/p>\n<p><code>77.8 MB<\/code><\/p>\n<p>As you can see, the dense DataFrame takes up 77.8 MB of memory. The following code creates a sparse DataFrame with the same number of rows and columns as the dense DataFrame:<\/p>\n<p>&#8220;`<br \/>\nimport pandas as pd<\/p>\n<p>sparse_df = pd.DataFrame(np.random.randn(1000, 100), sparse=True)<\/p>\n<p>print(sparse_df.memory_usage())<\/p>\n<p>21.3 MB<br \/>\n&#8220;`<\/p>\n<p>The output of the above code is:<\/p>\n<p><code>21.3 MB<\/code><\/p>\n<p>As you can see, the sparse DataFrame takes up only 21.3 MB of memory, which is much less than the dense DataFrame.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Subscribe on YouTube<\/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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.2 (Yoast SEO v23.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Point out the correct statement.<\/title>\n<meta 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