years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]

gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]

# create a line chart, years on x-axis, gdp on y-axis

plt.plot(years, gdp, color='green', marker='o', linestyle='solid')

# add a title

plt.title("Nominal GDP")

# add a label to the y-axis

plt.ylabel("Billions of $")

movies = ["Annie Hall", "Ben-Hur", "Casablanca", "Gandhi", "West Side Story"]

num_oscars = [5, 11, 3, 8, 10]

# bars are by default width 0.8, so we'll add 0.1 to the left coordinates

# so that each bar is centered

xs = [i + 0.1 for i, _ in enumerate(movies)]

# plot bars with left x-coordinates [xs], heights [num_oscars]

plt.bar(xs, num_oscars)

plt.ylabel("# of Academy Awards")

plt.title("My Favorite Movies")

# label x-axis with movie names at bar centers

plt.xticks([i + 0.5 for i, _ in enumerate(movies)], movies)

grades = [83,95,91,87,70,0,85,82,100,67,73,77,0]

decile = lambda grade: grade // 10 * 10

histogram = Counter(decile(grade) for grade in grades)

plt.bar([x - 4 for x in histogram.keys()], # shift each bar to the left by 4

histogram.values(), # give each bar its correct height

8) # give each bar a width of 8

plt.axis([-5, 105, 0, 5]) # x-axis from -5 to 105,

# y-axis from 0 to 5

plt.xticks([10 * i for i in range(11)]) # x-axis labels at 0, 10, ..., 100

plt.xlabel("Decile")

plt.ylabel("# of Students")

plt.title("Distribution of Exam 1 Grades")

mentions = [500, 505]

years = [2013, 2014]

plt.bar([2012.6, 2013.6], mentions, 0.8)

plt.xticks(years)

plt.ylabel("# of times I heard someone say 'data science'")

# if you don't do this, matplotlib will label the x-axis 0, 1

# and then add a +2.013e3 off in the corner (bad matplotlib!)

plt.ticklabel_format(useOffset=False)

if mislead:

# misleading y-axis only shows the part above 500

plt.axis([2012.5,2014.5,499,506])

plt.title("Look at the 'Huge' Increase!")

else:

plt.axis([2012.5,2014.5,0,550])

plt.title("Not So Huge Anymore.")

variance = [1,2,4,8,16,32,64,128,256]

bias_squared = [256,128,64,32,16,8,4,2,1]

total_error = [x + y for x, y in zip(variance, bias_squared)]

xs = range(len(variance))

# we can make multiple calls to plt.plot

# to show multiple series on the same chart

plt.plot(xs, variance, 'g-', label='variance') # green solid line

plt.plot(xs, bias_squared, 'r-.', label='bias^2') # red dot-dashed line

plt.plot(xs, total_error, 'b:', label='total error') # blue dotted line

# because we've assigned labels to each series

# we can get a legend for free

# loc=9 means "top center"

plt.legend(loc=9)

plt.xlabel("model complexity")

plt.title("The Bias-Variance Tradeoff")

friends = [ 70, 65, 72, 63, 71, 64, 60, 64, 67]

minutes = [175, 170, 205, 120, 220, 130, 105, 145, 190]

labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']

plt.scatter(friends, minutes)

# label each point

for label, friend_count, minute_count in zip(labels, friends, minutes):

plt.annotate(label,

xy=(friend_count, minute_count), # put the label with its point

xytext=(5, -5), # but slightly offset

textcoords='offset points')

plt.title("Daily Minutes vs. Number of Friends")

plt.xlabel("# of friends")

plt.ylabel("daily minutes spent on the site")

test_1_grades = [ 99, 90, 85, 97, 80]

test_2_grades = [100, 85, 60, 90, 70]

plt.scatter(test_1_grades, test_2_grades)

plt.xlabel("test 1 grade")

plt.ylabel("test 2 grade")

if equal_axes:

plt.title("Axes Are Comparable")

plt.axis("equal")

else:

plt.title("Axes Aren't Comparable")

plt.pie([0.95, 0.05], labels=["Uses pie charts", "Knows better"])

# make sure pie is a circle and not an oval

plt.axis("equal")