title : Insert the chapter title here description : Insert the chapter description here attachments : slides_link : https://s3.amazonaws.com/assets.datacamp.com/course/teach/slides_example.pdf
--- type:VideoExercise lang:python xp:50 skills:1 key:c0c04e66db7f0aa1e18225622a4caa89979e9a0a
*** =video_link //player.vimeo.com/video/154783078
--- type:MultipleChoiceExercise lang:python xp:50 skills:1 key:6833ea607d
Have a look at the plot that showed up in the viewer to the right. Which type of movies have the worst rating assigned to them?
*** =instructions
- Long movies, clearly
- Short movies, clearly
- Long movies, but the correlation seems weak
- Short movies, but the correlation seems weak
*** =hint Have a look at the plot. Do you see a trend in the dots?
*** =pre_exercise_code
# The pre exercise code runs code to initialize the user's workspace. You can use it for several things:
# 1. Pre-load packages, so that users don't have to do this manually.
import pandas as pd
import matplotlib.pyplot as plt
# 2. Preload a dataset. The code below will read the csv that is stored at the URL's location.
# The movies variable will be available in the user's console.
movies = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/introduction_to_r/movies.csv")
# 3. Create a plot in the viewer, that students can check out while reading the exercise
plt.scatter(movies.runtime, movies.rating)
plt.show()
*** =sct
# The sct section defines the Submission Correctness Tests (SCTs) used to
# evaluate the student's response. All functions used here are defined in the
# pythonwhat Python package
msg_bad = "That is not correct!"
msg_success = "Exactly! The correlation is very weak though."
# Use test_mc() to grade multiple choice exercises.
# Pass the correct option (Action, option 2 in the instructions) to correct.
# Pass the feedback messages, both positive and negative, to feedback_msgs in the appropriate order.
test_mc(4, [msg_bad, msg_bad, msg_bad, msg_success])