Classwork 13

Group Operation I

Direction

The dataset ,cereals_oatmeal.csv,(with its pathname https://bcdanl.github.io/data/cereals_oatmeal.csv) is a listing of 77 popular breakfast cereals and oatmeal.

cereal = pd.read_csv('https://bcdanl.github.io/data/cereals_oatmeal.csv')

Question 1

Group the data by Manufacturer, and determine the number of groups and the number of cereals per group.

Answer:

Question 2

Calculate the mean of the Calories, Fiber, and Sugars for every manufacturer.

Answer:

Question 3

Create a new DataFrame that includes the maximum Sugars and the minimum Fiber per manufacturer.

Answer:

Question 4

Add a Normalized_Sugars variable to the cereal DataFrame that standardizes each cereal’s sugar content within its manufacturer group.

\[ \text{Normalized\_Sugars} = \frac{\text{Sugars} - \text{mean(Sugars)}}{\text{std(Sugars)}} \]

This formula adjusts the sugar content of each product by subtracting the mean sugar content of its manufacturer and then dividing by the standard deviation of the sugar content within its manufacturer.

Answer:

Question 5

Put the two highest-sugar cereals for every manufacturer in a new DataFrame.

Answer:

Question 6

• Compute the correlation between Calories and Sugars for each manufacturer.
  • corr() calculates the correlation between SERIES_1 and SERIES_2.

cereal["Calories"].corr( cereal["Sugars"] ) # returns a correlation value
cereal[["Calories", "Sugars"]].corr() # returns a DataFrame of correlation matrix
cereal[["Calories", "Sugars", "Fiber"]].corr() # returns a DataFrame of correlation matrix

Answer:

Discussion

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