In [1]:
from __future__ import unicode_literals
from collections import Counter
import matplotlib
matplotlib.rc('font',family='AppleGothic')
# 한글 폰트명은 시스템에 따라 변경 가능
import matplotlib.pyplot as plt
%pylab inline
In [2]:
def make_simple_line_chart():
""" 그림 3-1. 간단한 선 그래프 """
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='v', linestyle='solid')
# makrer, linestyle등 표시 방법은 http://matplotlib.org/api/lines_api.html 참조
# add a title
plt.title("명목 GDP")
# add a label to the y-axis
plt.ylabel("Billions of $")
plt.show()
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make_simple_line_chart()
In [4]:
def make_simple_bar_chart():
""" 그림 3-2. 간단한 막대 그래프 """
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)]
# xs = [0.1, 1.1, 2.1, 3.1, 4.1], 일반적은로 _ 변수는 쓰지 않는 변수를 할당할 때 사용함
# 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)
plt.show()
In [5]:
make_simple_bar_chart()
In [6]:
def make_histogram():
""" 그림 3-3. 막대 그래프로 히스토그램 그리기 """
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)
# Counter([grade // 10 * 10 for grade in grades])
# Counter({80: 4, 70: 3, 0: 2, 90: 2, 100: 1, 60: 1})
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")
plt.show()
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make_histogram()
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def make_misleading_y_axis(mislead=True):
"""
그림 3-4. y축이 오해를 불러일으키는 그래프,
그림 3-5. y축히 오해를 불러일으키지 않는 그래프 """
# Y축의 간격이 너무 커지는 문제를 지적함
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]) # Y축의 범례가 499 ~ 506
plt.title("Look at the 'Huge' Increase!")
else:
plt.axis([2012.5,2014.5,0,550]) # Y축의 범례가 0 ~ 550
plt.title("Not So Huge Anymore.")
plt.show()
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make_misleading_y_axis(mislead=True) # Y축의 범례가 499 ~ 506, Y축의 간격이 넓어보임
In [10]:
make_misleading_y_axis(mislead=False) # Y축의 범례가 0 ~ 550, Y축의 간격이 좁아보임
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def make_several_line_charts():
""" 그림 3-6. 여러 개의 선 그래프와 범례 동시에 그리기 """
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)]
# [257, 130, 68, 40, 32, 40, 68, 130, 257]
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")
plt.show()
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make_several_line_charts()
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def make_scatter_plot():
""" 그림 3-7. 친구의 수와 사이트 체류 시간에 관한 산점도 """
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, 각 점에 대한 라베링이 떨어진 거리 x축, y축
textcoords='offset points')
plt.title("Daily Minutes vs. Number of Friends")
plt.xlabel("# of friends")
plt.ylabel("daily minutes spent on the site")
plt.show()
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make_scatter_plot()
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def make_scatterplot_axes(equal_axes=False):
"""
그림 3-8. 축 간 공정한 비교를 할 수 없는 산점도
그림 3-9. 축 간 공정한 비교를 할 수 있는 산점도 """
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") # X, Y 축간 간격을 적절히 조절함
else:
plt.title("Axes Aren't Comparable")
plt.show()
In [16]:
make_scatterplot_axes(equal_axes=False)
make_scatterplot_axes(equal_axes=True)