in data visualization, we often need to create a histogram to display the distribution of a numerical…

in data visualization, we often need to create a histogram to display the distribution of a numerical variable. suppose we have a dataset called students that contains an age variable representing the ages of students.\nin r, the students dataset can be defined as a data frame with the age variable as follows:\n# r code to define the students dataset\nstudents <- data.frame(age = c(20, 21, 19, 22, 20, 23))\nin python, using the pandas library, the students dataset can be defined as a dataframe with the age variable as follows:\n# python code to define the students dataset\nimport pandas as pd\nstudents = pd.dataframe({age: 20, 21, 19, 22, 20, 23})\nwhich of the following correctly identifies the code to create a histogram of the age variable from the students dataset in r and python (assuming you have already imported the matplotlib.pyplot library as plt)?\na. hist(students$age) (in r); plt.hist(studentsage) (in python)\nb. hist(age$students) (in r); plt.hist(agestudents) (in python)\nc. barplot(students$age) (in r); plt.bar(studentsage) (in python)\nd. histstudentsage (in r); plt.histstudentsage (in python)

in data visualization, we often need to create a histogram to display the distribution of a numerical variable. suppose we have a dataset called students that contains an age variable representing the ages of students.\nin r, the students dataset can be defined as a data frame with the age variable as follows:\n# r code to define the students dataset\nstudents <- data.frame(age = c(20, 21, 19, 22, 20, 23))\nin python, using the pandas library, the students dataset can be defined as a dataframe with the age variable as follows:\n# python code to define the students dataset\nimport pandas as pd\nstudents = pd.dataframe({age: 20, 21, 19, 22, 20, 23})\nwhich of the following correctly identifies the code to create a histogram of the age variable from the students dataset in r and python (assuming you have already imported the matplotlib.pyplot library as plt)?\na. hist(students$age) (in r); plt.hist(studentsage) (in python)\nb. hist(age$students) (in r); plt.hist(agestudents) (in python)\nc. barplot(students$age) (in r); plt.bar(studentsage) (in python)\nd. histstudentsage (in r); plt.histstudentsage (in python)

Answer

Brief Explanations:

In R, to create a histogram of a variable in a data - frame, the correct syntax is hist(dataframe$variable). Here, the data - frame is students and the variable is age, so it's hist(students$age). In Python, with matplotlib.pyplot imported as plt and a pandas DataFrame students, the correct way to create a histogram of the age variable is plt.hist(students['age']).

Answer:

A. hist(students$age) (in R); plt.hist(students['age']) (in Python)