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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:MultipleChoiceExercise lang:python xp:50 skills:1 key:4800e25739

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 to load packages, initialize datasets and draw a plot in the viewer

import pandas as pd
import matplotlib.pyplot as plt

movies = pd.read_csv("http://s3.amazonaws.com/assets.datacamp.com/course/introduction_to_r/movies.csv")

plt.scatter(movies.runtime, movies.rating)
plt.show()

*** =sct

# SCT written with pythonwhat: https://github.com/datacamp/pythonwhat/wiki

msg_bad = "That is not correct!"
msg_success = "Exactly! The correlation is very weak though."
test_mc(4, [msg_bad, msg_bad, msg_bad, msg_success])

--- type:NormalExercise lang:python xp:100 skills:1 key:cf255d39f0

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.

*** =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

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

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

# SCT written with pythonwhat: https://github.com/datacamp/pythonwhat/wiki

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`.")

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.")

test_import("matplotlib.pyplot", same_as = True)

test_function("matplotlib.pyplot.scatter",
              incorrect_msg = "You didn't use `plt.scatter()` correctly, have another look at the instructions.")

test_function("matplotlib.pyplot.show")

success_msg("Great work!")

--- type:BokehServerExercise lang:python xp:100 skills:1 key:a9e9f99d6a

Bokeh Server

Blabla bokeh Server

*** =instructions Plot the plot with bokeh server

*** =hint

  • plot
  • plot again

*** =pre_exercise_code

#

*** =sample_code

import numpy as np
from numpy import pi
from bokeh.driving import cosine
from bokeh.plotting import figure, curdoc

x = np.linspace(0, 4*pi, 80)
y = np.sin(x)

p = figure()
r1 = p.line([0, 4*pi], [-1, 1], color="firebrick")
r2 = p.line(x, y, color="navy", line_width=4)

curdoc().add_root(p)

@cosine(w=0.03)
def update(step):
    # updating a single column of the the *same length* is OK
    r2.data_source.data["y"] = y * step
    r2.glyph.line_alpha = 1 - 0.8 * abs(step)

curdoc().add_periodic_callback(update, 50)

*** =solution

#

*** =sct

#