[amp_mcq option1=”Aggregate data” option2=”Sort data” option3=”Combine DataFrames” option4=”Group data” correct=”option3″]<\/p>\n
The correct answer is: C. Combine DataFrames.<\/p>\n
The The The The Here is an example of how to use the “` df1 = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]}) df = df1.merge(df2, on=’A’)<\/p>\n print(df)<\/p>\n A B C D In this example, the [amp_mcq option1=”Aggregate data” option2=”Sort data” option3=”Combine DataFrames” option4=”Group data” 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-51752","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\nmerge<\/code> function in Pandas is used to combine two or more DataFrames. It can be used to join DataFrames on a common column, or to concatenate them.<\/p>\naggregate<\/code> function in Pandas is used to perform aggregate operations on a DataFrame. It can be used to calculate the mean, sum, or count of a column, or to group the DataFrame by a column and calculate the aggregate values for each group.<\/p>\nsort<\/code> function in Pandas is used to sort a DataFrame by a column. It can be sorted in ascending or descending order.<\/p>\ngroup<\/code> function in Pandas is used to group a DataFrame by a column. It can be used to calculate the mean, sum, or count of a column for each group, or to create a new DataFrame with the grouped data.<\/p>\nmerge<\/code> function:<\/p>\n
\nimport pandas as pd<\/p>\n
\ndf2 = pd.DataFrame({‘C’: [7, 8, 9], ‘D’: [10, 11, 12]})<\/p>\n
\n0 1 4 7
\n1 2 5 8
\n2 3 6 9
\n“`<\/p>\nmerge<\/code> function has been used to combine the df1<\/code> and df2<\/code> DataFrames on the A<\/code> column. The resulting DataFrame, df<\/code>, has three columns: A<\/code>, B<\/code>, and C<\/code>. The B<\/code> and C<\/code> columns come from the df1<\/code> and df2<\/code> DataFrames, respectively.<\/p>\n","protected":false},"excerpt":{"rendered":"