[amp_mcq option1=”size, value” option2=”semantic, size” option3=”value, size” option4=”none of the mentioned” correct=”option3″]<\/p>\n
The correct answer is C. value, size.<\/p>\n
All pandas data structures are mutable in terms of their values, but not always in terms of their size. This means that the values in a pandas data structure can be changed, but the number of rows or columns in the data structure cannot always be changed.<\/p>\n
For example, a pandas DataFrame is a two-dimensional data structure with rows and columns. The values in a DataFrame can be changed by assigning new values to the cells in the DataFrame. However, the number of rows or columns in a DataFrame cannot be changed after the DataFrame has been created.<\/p>\n
Another example is a pandas Series, which is a one-dimensional data structure. The values in a Series can be changed by assigning new values to the elements in the Series. However, the length of a Series cannot be changed after the Series has been created.<\/p>\n
In general, pandas data structures are mutable in terms of their values, but not always in terms of their size. This is because pandas data structures are designed to be efficient for storing and manipulating data. If the size of a pandas data structure could be changed after it had been created, this would make the data structure less efficient.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”size, value” option2=”semantic, size” option3=”value, size” option4=”none of the mentioned” correct=”option3″]<\/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-57546","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\n