[amp_mcq option1=”Group data” option2=”Sort data” option3=”Reshape data” option4=”Aggregate data” correct=”option4″]<\/p>\n
The correct answer is D. Aggregate data.<\/p>\n
The pivot_table() function in Pandas is used to aggregate data by rows and columns. It can be used to create a variety of different types of pivot tables, including crosstabs, pivot tables with multiple dimensions, and pivot tables with multiple groups.<\/p>\n
The pivot_table() function takes a number of arguments, including the data frame to be pivoted, the columns to be aggregated, the rows to be aggregated, and the aggregation function to be used. The aggregation function can be any of the standard Pandas aggregation functions, such as sum(), mean(), or count().<\/p>\n
The pivot_table() function returns a new data frame that contains the aggregated data. The new data frame will have the same columns as the original data frame, but the rows will be grouped according to the columns that were specified in the pivot_table() function.<\/p>\n
For example, if you have a data frame with columns “A”, “B”, and “C”, and you want to pivot the data by column “A”, the pivot_table() function would return a new data frame with columns “A”, “B”, and “C”, and rows that are grouped by the values in column “A”.<\/p>\n
The pivot_table() function is a powerful tool that can be used to aggregate data in a variety of ways. It is a common function that is used in many different data analysis applications.<\/p>\n
Here is an example of how the pivot_table() function can be used:<\/p>\n
“`
\nimport pandas as pd<\/p>\n
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9]})<\/p>\n
pivot_table = df.pivot_table(index=’A’, columns=’B’, values=’C’)<\/p>\n
print(pivot_table)<\/p>\n
A B C
\n0 4 7
\n1 5 8
\n2 6 9
\n“`<\/p>\n
The pivot_table() function has returned a new data frame with columns “A”, “B”, and “C”, and rows that are grouped by the values in column “A”. The values in column “C” have been aggregated by the aggregation function that was specified in the pivot_table() function. In this case, the aggregation function was sum(), so the values in column “C” have been summed for each group.<\/p>\n","protected":false},"excerpt":{"rendered":"
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