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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

Analyze movie ratings

*** =video_link //player.vimeo.com/video/154783078

--- type:MultipleChoiceExercise lang:python xp:50 skills:1

A really bad movie

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])

--- type:MultipleChoiceExercise lang:python xp:50 skills:1

A really bad movie

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 (option 4 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])

--- type:NormalExercise lang:python xp:100 skills:1

Plot the movies yourself

Do you remember the plot of the last exercise? Let's make an even cooler plot!

A dataset of movies, movies, is available in the workspace. lalala

*** =instructions

  • The first function, np.unique(), uses the unique() function of the numpy package to get integer values for the movie genres. You don't have to change this code, just have a look!
  • Import pyplot in the matplotlib package. Set an alias for this import: plt.
  • Use plt.scatter() to plot movies.runtime onto the x-axis, movies.rating onto the y-axis and use ints for the color of the dots. You should use the first and second positional argument, and the c keyword.
  • Show the plot using plt.show().

*** =hint

  • You don't have to program anything for the first instruction, just take a look at the first line of code.
  • Use import ___ as ___ to import matplotlib.pyplot as plt.
  • Use plt.scatter(___, ___, c = ___) for the third instruction.
  • You'll always have to type in plt.show() to show the plot you created.

*** =pre_exercise_code

# The pre exercise code runs code to initialize the user's workspace. You can use it for several things:

# 1. 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.
import pandas as pd
movies = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/introduction_to_r/movies.csv")

# 2. Preload a package
import numpy as np

*** =sample_code

# Get integer values for genres
_, ints = np.unique(movies.genre, return_inverse = True)

# Import matplotlib.pyplot


# Make a scatter plot: runtime on  x-axis, rating on y-axis and set c to ints


# Show the plot

*** =solution

# Get integer values for genres
_, ints = np.unique(movies.genre, return_inverse = True)

# Import matplotlib.pyplot
import matplotlib.pyplot as plt

# Make a scatter plot: runtime on  x-axis, rating on y-axis and set c to ints
plt.scatter(movies.runtime, movies.rating, c=ints)

# Show the plot
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. Documentation can also be found at github.com/datacamp/pythonwhat/wiki

# Check if the student changed the np.unique() call
# If it's not called, we know the student removed the call.
# If it's called incorrectly, we know the student changed the call.
test_function("numpy.unique",
              not_called_msg = "Don't remove the call of `np.unique` to define `ints`.",
              incorrect_msg = "Don't change the call of `np.unique` to define `ints`.")
# Check if the student removed the ints object
test_object("ints",
            undefined_msg = "Don't remove the definition of the predefined `ints` object.",
            incorrect_msg = "Don't change the definition of the predefined `ints` object.")

# Check if the student imported matplotlib.pyplot like the solution
# Let automatic feedback message generation handle the feedback messages
test_import("matplotlib.pyplot", same_as = True)

# Check whether the student used the scatter() function correctly
# If it's used, but incorrectly, tell them to check the instructions again
test_function("matplotlib.pyplot.scatter",
              incorrect_msg = "You didn't use `plt.scatter()` correctly, have another look at the instructions.")

# Check if the student called the show() function
# Let automatic feedback message generation handle all feedback messages
test_function("matplotlib.pyplot.show")

success_msg("Great work!")