[amp_mcq option1=”Apply a function element-wise” option2=”Sort a DataFrame” option3=”Reshape data” option4=”Group data” correct=”option1″]<\/p>\n
The correct answer is: A. Apply a function element-wise<\/p>\n
The The The The Here is an example of how to use the “` df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})<\/p>\n df[‘C’] = df[‘A’].applymap(lambda x: x * 2)<\/p>\n print(df)<\/p>\n A B C In this example, the [amp_mcq option1=”Apply a function element-wise” option2=”Sort a DataFrame” option3=”Reshape data” option4=”Group data” correct=”option1″]<\/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-56637","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\napplymap<\/code> method in Pandas applies a function to each element of a DataFrame. This can be useful for performing operations such as converting data types, filtering rows, or calculating statistics.<\/p>\nsort<\/code> method in Pandas sorts a DataFrame by a specified column or columns. This can be useful for reordering the rows of a DataFrame or for finding the maximum or minimum values in a column.<\/p>\nreshape<\/code> method in Pandas reshapes a DataFrame into a different shape. This can be useful for converting a DataFrame from a long format to a wide format or vice versa.<\/p>\ngroupby<\/code> method in Pandas groups rows of a DataFrame by a specified column or columns. This can be useful for performing operations such as calculating statistics on groups of rows or for filtering rows based on their group.<\/p>\napplymap<\/code> method:<\/p>\n
\nimport pandas as pd<\/p>\n
\n0 1 2
\n1 2 4
\n2 3 6
\n“`<\/p>\napplymap<\/code> method is used to multiply each element in the A<\/code> column by 2. The result is a new column, C<\/code>, which contains the doubled values.<\/p>\n","protected":false},"excerpt":{"rendered":"