Question

in r, the movies vector is defined as:

r code to define the movies vector movies <- c(\action\, \comedy\, \action\, \drama\, \comedy\, \sci - fi\)

in python, using the pandas library, the movies vector is defined as:

python code to define the movies vector

import pandas as pd
movies = \action\, \comedy\, \action\, \drama\, \comedy\, \sc
which of the following correctly identifies the code to create a bar chart of the category frequencies in the movies vector in r and python (assuming we have already imported: import matplotlib.pyplot as plt)
a. barplot(table(movies)) (in r); plt.bar(pd.series(movies).value_counts().index, pd.sr
b. barplot$movies (in r); plt.barmovies (in python)
c. tablemovies_barplot (in r); pd.seriesmovies_barplot (in python)
d. barplot(movies) (in r); plt.bar(movies) (in python)

Explanation:

Step1: Analyze R - code

In R, to create a bar - plot of category frequencies, we first need to count the frequencies using table() function and then use barplot() on the result. So barplot(table(Movies)) is correct for R.

Step2: Analyze Python - code

In Python, using pandas and matplotlib, we first convert the Movies list to a pandas.Series, then use value_counts() to get the frequencies of each category. The index of the resulting Series gives the unique categories and the values are the frequencies. Then we use plt.bar() to create the bar - chart. So plt.bar(pd.Series(Movies).value_counts().index, pd.Series(Movies).value_counts()) is correct for Python.

Answer:

A. barplot(table(Movies)) (in R); plt.bar(pd.Series(Movies).value_counts().index, pd.Series(Movies).value_counts()) (in Python)