[Python] Matplotlib을 활용한 시각화 실습

matplotlib 그래프¶

pyplot 공식 도큐먼트 살펴보기

In [1]:

!pip install --upgrade --force-reinstall seaborn

Defaulting to user installation because normal site-packages is not writeable
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Installing collected packages: pytz, zipp, tzdata, six, pyparsing, pillow, packaging, numpy, kiwisolver, fonttools, cycler, python-dateutil, importlib-resources, contourpy, pandas, matplotlib, seaborn
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
scipy 1.8.1 requires numpy<1.25.0,>=1.17.3, but you have numpy 2.0.0 which is incompatible.
Successfully installed contourpy-1.2.1 cycler-0.12.1 fonttools-4.53.0 importlib-resources-6.4.0 kiwisolver-1.4.5 matplotlib-3.9.0 numpy-2.0.0 packaging-24.1 pandas-2.2.2 pillow-10.3.0 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 seaborn-0.13.2 six-1.16.0 tzdata-2024.1 zipp-3.19.2

[notice] A new release of pip available: 22.2.2 -> 24.1.1
[notice] To update, run: pip install --upgrade pip

모듈 Import¶

In [2]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings('ignore')

# Unicode warning 제거 (폰트 관련 경고메시지)
plt.rcParams['axes.unicode_minus']=False

# 한글 폰트 설정
plt.rcParams['font.family'] = "NanumGothic"

# 그래프 출력 사이즈 설정
plt.rcParams["figure.figsize"] = (12, 10)

Scatterplot¶

0 ~ 1 사이의 임의의 랜덤한 값을 생성합니다.

In [3]:

np.random.rand(50)

Out[3]:

array([0.62881031, 0.42937313, 0.78624726, 0.58871169, 0.24828663,
       0.03681471, 0.85600288, 0.50050205, 0.260603  , 0.69850733,
       0.56113708, 0.860106  , 0.79483302, 0.45692983, 0.54384138,
       0.47542397, 0.09426981, 0.3702961 , 0.21580396, 0.51580194,
       0.45960071, 0.91801268, 0.91253519, 0.75383199, 0.89420131,
       0.0890961 , 0.64751513, 0.09491463, 0.97345499, 0.18921112,
       0.55480593, 0.68921671, 0.9243705 , 0.5243626 , 0.781508  ,
       0.70847555, 0.99692983, 0.50728519, 0.00788625, 0.25956128,
       0.50683897, 0.92157855, 0.00652356, 0.65054316, 0.76185927,
       0.70391341, 0.31149907, 0.02614032, 0.27916092, 0.9953763 ])

x, y, colors, area 설정하기¶

• colors 는 임의 값을 color 값으로 변환합니다.
• area는 점의 넓이를 나타냅니다. 값이 커지면 당연히 넓이도 커집니다.

In [4]:

x = np.random.rand(50)
y = np.random.rand(50)
colors = np.arange(50)
area = x * y * 250

In [5]:

plt.scatter(x, y, s=area, c=colors)
plt.show()

color, alpha¶

• color에 컬러를 지정하면, 컬러 값을 모두 같게 가져갈 수도 있습니다.
• alpha값은 투명도를 나타내며 0 ~ 1 사이의 값을 지정해 줄 수 있으며, 0에 가까울 수록 투명한 값을 가집니다.

In [6]:

np.random.rand(50)

Out[6]:

array([0.24557946, 0.44399065, 0.31220244, 0.93847529, 0.04239565,
       0.37289896, 0.40686842, 0.94386493, 0.94232699, 0.93225503,
       0.25688768, 0.91783008, 0.57507472, 0.13402612, 0.7316266 ,
       0.50171634, 0.70980674, 0.22579066, 0.73296916, 0.87460534,
       0.61589997, 0.91675269, 0.14489578, 0.54333542, 0.64616292,
       0.37452708, 0.85776254, 0.15532672, 0.2286975 , 0.58649138,
       0.31110777, 0.9368848 , 0.76920667, 0.57317576, 0.71403262,
       0.20433079, 0.25853246, 0.53995399, 0.14023826, 0.07321291,
       0.56570372, 0.28921533, 0.79367733, 0.10096392, 0.31030015,
       0.17688359, 0.16222511, 0.6619724 , 0.54803703, 0.73946328])

In [7]:

plt.figure(figsize=(12, 6))

plt.subplot(131)
plt.scatter(x, y, s=area, color='purple', alpha=0.1)
plt.title('alpha=0.1')

plt.subplot(132)
plt.title('alpha=0.5')
plt.scatter(x, y, s=area, color='purple', alpha=0.5)

plt.subplot(133)
plt.title('alpha=1.0')
plt.scatter(x, y, s=area, color='purple', alpha=1.0)

plt.show()

Barplot, Barhplot¶

1개의 canvas 안에 다중 그래프 그리기

Barplot 생성¶

In [8]:

x = ['Math', 'Programming', 'Data Science', 'Art', 'English', 'Physics']
y = [66, 80, 60, 50, 80, 10]

plt.figure(figsize=(6, 3))
# plt.bar(x, y)
plt.bar(x, y, align='center', alpha=0.7, color='red')
plt.xticks(x)
plt.ylabel('Number of Students')
plt.title('Subjects')

plt.show()

Barhplot (축 변환)¶

barh 함수에서는 xticks로 설정했던 부분을 yticks로 변경합니다.

In [9]:

x = ['Math', 'Programming', 'Data Science', 'Art', 'English', 'Physics']
y = [66, 80, 60, 50, 80, 10]

plt.barh(x, y, align='center', alpha=0.7, color='green')
plt.yticks(x)
plt.xlabel('Number of Students')
plt.title('Subjects')

plt.show()

Barplot에서 비교 그래프 그리기¶

In [10]:

x_label = ['Math', 'Programming', 'Data Science', 'Art', 'English', 'Physics']
x = np.arange(len(x_label))
y_1 = [66, 80, 60, 50, 80, 10]
y_2 = [55, 90, 40, 60, 70, 20]

# 넓이 지정
width = 0.35

# subplots 생성
fig, axes = plt.subplots()

# 넓이 설정
axes.bar(x - width/2, y_1, width, align='center', alpha=0.5)
axes.bar(x + width/2, y_2, width, align='center', alpha=0.8)

# xtick 설정
plt.xticks(x)
axes.set_xticklabels(x_label)
plt.ylabel('Number of Students')
plt.title('Subjects')

plt.legend(['john', 'peter'])

plt.show()

In [11]:

x_label = ['Math', 'Programming', 'Data Science', 'Art', 'English', 'Physics']
x = np.arange(len(x_label))
y_1 = [66, 80, 60, 50, 80, 10]
y_2 = [55, 90, 40, 60, 70, 20]

# 넓이 지정
width = 0.35

# subplots 생성
fig, axes = plt.subplots()

# 넓이 설정
axes.barh(x - width/2, y_1, width, align='center', alpha=0.5, color='green')
axes.barh(x + width/2, y_2, width, align='center', alpha=0.8, color='red')

# xtick 설정
plt.yticks(x)
axes.set_yticklabels(x_label)
plt.xlabel('Number of Students')
plt.title('Subjects')

plt.legend(['john', 'peter'])

plt.show()

Line Plot¶

기본 lineplot 그리기¶

In [12]:

x = np.arange(0, 10, 0.1)
y = 1 + np.sin(x)

plt.plot(x, y)

plt.xlabel('x value', fontsize=15)
plt.ylabel('y value', fontsize=15)
plt.title('sin graph', fontsize=18)

plt.grid()

plt.show()

2개 이상의 그래프 그리기¶

• color: 컬러 옵션
• alpha: 투명도 옵션

In [13]:

x = np.arange(0, 10, 0.1)
y_1 = 1 + np.sin(x)
y_2 = 1 + np.cos(x)

plt.plot(x, y_1, label='1+sin', color='blue', alpha=0.3)
plt.plot(x, y_2, label='1+cos', color='red', alpha=0.7)

plt.xlabel('x value', fontsize=15)
plt.ylabel('y value', fontsize=15)
plt.title('sin and cos graph', fontsize=18)

plt.grid()
plt.legend()

plt.show()

마커 스타일링¶

• marker: 마커 옵션

In [14]:

x = np.arange(0, 10, 0.1)
y_1 = 1 + np.sin(x)
y_2 = 1 + np.cos(x)

plt.plot(x, y_1, label='1+sin', color='blue', alpha=0.3, marker='o')
plt.plot(x, y_2, label='1+cos', color='red', alpha=0.7, marker='+')

plt.xlabel('x value', fontsize=15)
plt.ylabel('y value', fontsize=15)
plt.title('sin and cos graph', fontsize=18)

plt.grid()
plt.legend()

plt.show()

라인 스타일 변경¶

• linestyle: 라인 스타일 변경 옵션

In [15]:

x = np.arange(0, 10, 0.1)
y_1 = 1 + np.sin(x)
y_2 = 1 + np.cos(x)

plt.plot(x, y_1, label='1+sin', color='blue', linestyle=':')
plt.plot(x, y_2, label='1+cos', color='red', linestyle='-.')

plt.xlabel('x value', fontsize=15)
plt.ylabel('y value', fontsize=15)
plt.title('sin and cos graph', fontsize=18)

plt.grid()
plt.legend()

plt.show()

Areaplot (Filled Area)¶

matplotlib에서 area plot을 그리고자 할 때는 fill_between 함수를 사용합니다.

In [16]:

y = np.random.randint(low=5, high=10, size=20)
y

Out[16]:

array([8, 8, 7, 7, 9, 6, 8, 7, 6, 5, 6, 5, 9, 6, 8, 6, 6, 7, 5, 5])

기본 areaplot 그리기¶

In [17]:

x = np.arange(1,21)
y =  np.random.randint(low=5, high=10, size=20)

# fill_between으로 색칠하기
plt.fill_between(x, y, color="green", alpha=0.6)
plt.show()

경계선을 굵게 그리고 area는 옅게 그리는 효과 적용¶

In [18]:

plt.fill_between(x, y, color="green", alpha=0.3)
plt.plot(x, y, color="green", alpha=0.8)

Out[18]:

[<matplotlib.lines.Line2D at 0x7efe8ff1de50>]

여러 그래프를 겹쳐서 표현¶

In [19]:

x = np.arange(1, 10, 0.05)
y_1 =  np.cos(x)+1
y_2 =  np.sin(x)+1
y_3 = y_1 * y_2 / np.pi

plt.fill_between(x, y_1, color="green", alpha=0.1)
plt.fill_between(x, y_2, color="blue", alpha=0.2)
plt.fill_between(x, y_3, color="red", alpha=0.3)

Out[19]:

<matplotlib.collections.PolyCollection at 0x7efe8ffcb610>

Histogram¶

기본 Histogram 생성¶

In [20]:

N = 100000
bins = 30

x = np.random.randn(N)

plt.hist(x, bins=bins)

plt.show()

• sharey: y축을 다중 그래프가 share
• tight_layout: graph의 패딩을 자동으로 조절해주어 fit한 graph를 생성

다중 Histogram, bin의 크기변화¶

In [21]:

N = 100000
bins = 30

x = np.random.randn(N)

fig, axs = plt.subplots(1, 3, 
                        sharey=True, 
                        tight_layout=True
                       )

fig.set_size_inches(12, 5)

axs[0].hist(x, bins=bins)
axs[1].hist(x, bins=bins*2)
axs[2].hist(x, bins=bins*4)

plt.show()

Y축에 Density 표기¶

In [22]:

N = 100000
bins = 30

x = np.random.randn(N)

fig, axs = plt.subplots(1, 2, 
                        tight_layout=True
                       )
fig.set_size_inches(9, 3)

# density=True 값을 통하여 Y축에 density를 표기할 수 있습니다.
axs[0].hist(x, bins=bins, density=True, cumulative=True)
axs[1].hist(x, bins=bins, density=True)

plt.show()

Pie Chart¶

pie chart 옵션

• explode: 파이에서 툭 튀어져 나온 비율
• autopct: 퍼센트 자동으로 표기
• shadow: 그림자 표시
• startangle: 파이를 그리기 시작할 각도

texts, autotexts 인자를 리턴 받습니다.

texts는 label에 대한 텍스트 효과를

autotexts는 파이 위에 그려지는 텍스트 효과를 다룰 때 활용합니다.

In [23]:

labels = ['Samsung', 'Huawei', 'Apple', 'Xiaomi', 'Oppo', 'Etc']
sizes = [20.4, 15.8, 10.5, 9, 7.6, 36.7]
explode = (0.3, 0, 0, 0, 0, 0)

# texts, autotexts 인자를 활용하여 텍스트 스타일링을 적용합니다
patches, texts, autotexts = plt.pie(sizes, 
                                    explode=explode, 
                                    labels=labels,  
                                    autopct='%1.1f%%',
                                    shadow=True, 
                                    startangle=90)

plt.title('Smartphone pie', fontsize=15)

# label 텍스트에 대한 스타일 적용
for t in texts:
    t.set_fontsize(12)
    t.set_color('gray')
    
# pie 위의 텍스트에 대한 스타일 적용
for t in autotexts:
    t.set_color("white")
    t.set_fontsize(10)

plt.show()

Box Plot¶

샘플 데이터를 생성합니다.

In [24]:

# 샘플 데이터 생성
spread = np.random.rand(50) * 100
center = np.ones(25) * 50
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
data = np.concatenate((spread, center, flier_high, flier_low))

기본 박스플롯 생성¶

In [25]:

plt.boxplot(data)
plt.tight_layout()
plt.show()

Box Plot 축 바꾸기¶

vert=False 옵션을 통해 표시하고자 하는 축을 바꿀 수 있습니다.

In [26]:

plt.title('Horizontal Box Plot', fontsize=15)
plt.boxplot(data, vert=False)

plt.show()

Outlier 마커 심볼과 컬러 변경¶

In [27]:

outlier_marker = dict(markerfacecolor='r', marker='D')

In [28]:

plt.title('Changed Outlier Symbols', fontsize=15)
plt.boxplot(data, flierprops=outlier_marker)

plt.show()

3D 그래프¶

3d 로 그래프를 그리기 위해서는 mplot3d를 추가로 import 합니다

In [29]:

from mpl_toolkits import mplot3d

밑그림 그리기 (캔버스)¶

In [30]:

fig = plt.figure()
ax = plt.axes(projection='3d')

3d plot 그리기¶

In [31]:

# project=3d로 설정합니다
ax = plt.axes(projection='3d')

# x, y, z 데이터를 생성합니다
z = np.linspace(0, 15, 1000)
x = np.sin(z)
y = np.cos(z)

ax.plot3D(x, y, z, 'gray')
plt.show()

In [32]:

# project=3d로 설정합니다
ax = plt.axes(projection='3d')

sample_size = 100
x = np.cumsum(np.random.normal(0, 1, sample_size))
y = np.cumsum(np.random.normal(0, 1, sample_size))
z = np.cumsum(np.random.normal(0, 1, sample_size))

# marker 추가
ax.plot3D(x, y, z, alpha=0.6, marker='o')

plt.title("ax.plot")
plt.show()

3d-scatter¶

In [33]:

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111, projection='3d') # Axe3D object

sample_size = 500

x = np.cumsum(np.random.normal(0, 5, sample_size))
y = np.cumsum(np.random.normal(0, 5, sample_size))
z = np.cumsum(np.random.normal(0, 5, sample_size))

ax.scatter(x, y, z, c = z, s=20, alpha=0.5, cmap='Greens')

plt.title("ax.scatter")
plt.show()

contour3D 그리기 (등고선)¶

In [34]:

x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
x, y = np.meshgrid(x, y)

z = np.sin(np.sqrt(x**2 + y**2))

fig = plt.figure(figsize=(12, 6))
ax = plt.axes(projection='3d')

ax.contour3D(x, y, z, 20, cmap='Reds')

plt.title("ax.contour3D")
plt.show()

imshow¶

array 형식의 이미지(image) 데이터를 시각화 할 때는 imshow를 활용합니다.

In [35]:

import matplotlib.image as mpimg
from PIL import Image
import urllib


# 샘플 이미지 다운로드
url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/png/img6.png'
image_data = Image.open(urllib.request.urlopen(url))
image_data

Out[35]:

In [36]:

img = np.asarray(image_data)
img[:5, :5, 0]

Out[36]:

array([[64, 61, 47, 36, 35],
       [68, 60, 51, 38, 40],
       [63, 58, 54, 48, 43],
       [64, 58, 50, 51, 48],
       [71, 62, 50, 46, 47]], dtype=uint8)

In [37]:

plt.imshow(img)
plt.show()

제출¶

제출을 위해 Barplot에서 비교 그래프 그리기에서 그렸던 그래프 중 첫번째 그래프와 똑같은 그래프를 다시 그리세요.

• 즉 세로 막대 그래프를 의미합니다.

In [40]:

x_label = ['Math', 'Programming', 'Data Science', 'Art', 'English', 'Physics']
x = np.arange(len(x_label))
y_1 = [66, 80, 60, 50, 80, 10]
y_2 = [55, 90, 40, 60, 70, 20]

width = 0.35
fig, axes = plt.subplots()

# TODO: "Barplot에서 비교 그래프 그리기"에서 그렸던 그래프를 그리세요.
# 그래프를 이미지로 저장하기 전에 show() 함수를 호출하면 채점이 되지 않습니다.

# 넓이 설정
axes.bar(x - width/2, y_1, width, align='center', alpha=0.5)
axes.bar(x + width/2, y_2, width, align='center', alpha=0.8)

# xtick 설정
plt.xticks(x)
axes.set_xticklabels(x_label)
plt.ylabel('Number of Students')
plt.title('Subjects')

plt.legend(['john', 'peter'])

#plt.show()

# 이 아래를 수정하면 제대로 채점되지 않습니다.
plt.tight_layout()
plt.savefig('result.png')
plt.show()