BACK END/Python Library
[MatPlotLib] matplotlib 정리
circle kim
2021. 3. 2. 10:35
matplotlib : ploting library. 그래프 생성을 위한 다양한 함수 지원.
* mat1.py
import numpy as np
import matplotlib.pyplot as plt
plt.rc('font', family = 'malgun gothic') # 한글 깨짐 방지
plt.rcParams['axes.unicode_minus'] = False # 음수 깨짐 방지
x = ["서울", "인천", "수원"]
y = [5, 3, 7]
# tuple 가능. set 불가능.
plt.xlabel("지역") # x축 라벨
plt.xlim([-1, 3]) # x축 범위
plt.ylim([0, 10]) # y축 범위
plt.yticks(list(range(0, 11, 3))) # y축 칸 number 지정
plt.plot(x, y) # 그래프 생성
plt.show() # 그래프 출력
data = np.arange(1, 11, 2)
print(data) # [1 3 5 7 9]
plt.plot(data) # 그래프 생성
x = [0, 1, 2, 3, 4]
for a, b in zip(x, data):
plt.text(a, b, str(b)) # 그래프 라인에 text 표시
plt.show() # 그래프 출력
plt.plot(data)
plt.plot(data, data, 'r')
plt.show()
x = np.arange(10)
y = np.sin(x)
print(x, y)
plt.plot(x, y, 'bo') # 파란색 o로 표시. style 옵션 적용.
plt.plot(x, y, 'r+') # 빨간색 +로 표시
plt.plot(x, y, 'go--', linewidth = 2, markersize = 10) # 초록색 o 표시
plt.show()
# 홀드 : 그림 겹쳐 보기
x = np.arange(0, np.pi * 3, 0.1)
print(x)
y_sin = np.sin(x)
y_cos = np.cos(x)
plt.plot(x, y_sin, 'r') # 직선, 곡선
plt.scatter(x, y_cos) # 산포도
#plt.plot(x, y_cos, 'b')
plt.xlabel('x축')
plt.ylabel('y축')
plt.legend(['사인', '코사인']) # 범례
plt.title('차트 제목') # 차트 제목
plt.show()
# subplot : Figure를 여러 행열로 분리
x = np.arange(0, np.pi * 3, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
plt.subplot(2, 1, 1)
plt.plot(x, y_sin)
plt.title('사인')
plt.subplot(2, 1, 2)
plt.plot(x, y_cos)
plt.title('코사인')
plt.show()
irum = ['a', 'b', 'c', 'd', 'e']
kor = [80, 50, 70, 70, 90]
eng = [60, 70, 80, 70, 60]
plt.plot(irum, kor, 'ro-')
plt.plot(irum, eng, 'gs--')
plt.ylim([0, 100])
plt.legend(['국어', '영어'], loc = 4)
plt.grid(True) # grid 추가
fig = plt.gcf() # 이미지 저장 선언
plt.show() # 이미지 출력
fig.savefig('test.png') # 이미지 저장
from matplotlib.pyplot import imread
img = imread('test.png') # 이미지 read
plt.imshow(img) # 이미지 출력
plt.show()
차트의 종류 결정
* mat2.py
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure() # 명시적으로 차트 영역 객체 선언
ax1 = fig.add_subplot(1, 2, 1) # 1행 2열 중 1열에 표기
ax2 = fig.add_subplot(1, 2, 2) # 1행 2열 중 2열에 표기
ax1.hist(np.random.randn(10), bins=10, alpha=0.9) # 히스토그램 출력 - bins : 구간 수, alpha: 투명도
ax2.plot(np.random.randn(10)) # plot 출력
plt.show()
fig, ax = plt.subplots(nrows=2, ncols=1)
ax[0].plot(np.random.randn(10))
ax[1].plot(np.random.randn(10) + 20)
plt.show()
data = [50, 80, 100, 70, 90]
plt.bar(range(len(data)), data)
plt.barh(range(len(data)), data)
plt.show()
data = [50, 80, 100, 70, 90]
err = np.random.randn(len(data))
plt.barh(range(len(data)), data, xerr=err, alpha = 0.6)
plt.show()
plt.pie(data, explode=(0, 0.2, 0, 0, 0), colors = ['yellow', 'blue', 'red'])
plt.show()
n = 30
np.random.seed(42)
x = np.random.rand(n)
y = np.random.rand(n)
color = np.random.rand(n)
scale = np.pi * (15 * np.random.rand(n)) ** 2
plt.scatter(x, y, s = scale, c = color)
plt.show()
import pandas as pd
sdata = pd.Series(np.random.rand(10).cumsum(), index = np.arange(0, 100, 10))
plt.plot(sdata)
plt.show()
# DataFrame
fdata = pd.DataFrame(np.random.randn(1000, 4), index = pd.date_range('1/1/2000', periods = 1000),\
columns = list('ABCD'))
print(fdata)
fdata = fdata.cumsum()
plt.plot(fdata)
plt.show()
seaborn : matplotlib 라이브러리의 기능을 보완하기 위해 사용
* mat3.py
import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset("titanic")
print(titanic.info())
print(titanic.head(3))
age = titanic['age']
sns.kdeplot(age) # 밀도
plt.show()
sns.distplot(age) # kdeplot + hist
plt.show()
import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset("titanic")
print(titanic.info())
print(titanic.head(3))
age = titanic['age']
sns.kdeplot(age) # 밀도
plt.show()
sns.relplot(x = 'who', y = 'age', data=titanic)
plt.show()
sns.countplot(x = 'class', data=titanic, hue='who') # hue : 카테고리
plt.show()
t_pivot = titanic.pivot_table(index='class', columns='age', aggfunc='size')
print(t_pivot)
sns.heatmap(t_pivot, cmap=sns.light_palette('gray', as_cmap=True), fmt = 'd', annot=True)
plt.show()
import pandas as pd
iris_data = pd.read_csv('../testdata/iris.csv')
print(iris_data.info())
print(iris_data.head(3))
'''
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
'''
plt.scatter(iris_data['Sepal.Length'], iris_data['Petal.Length'])
plt.xlabel('Sepal.Length')
plt.ylabel('Petal.Length')
plt.show()
cols = []
for s in iris_data['Species']:
choice = 0
if s == 'setosa' : choice = 1
elif s == 'versicolor' : choice = 2
else: choice = 3
cols.append(choice)
plt.scatter(iris_data['Sepal.Length'], iris_data['Petal.Length'], c = cols)
plt.xlabel('Sepal.Length')
plt.ylabel('Petal.Length')
plt.show()
# pandas의 시각화 기능
print(type(iris_data)) # DataFrame
from pandas.plotting import scatter_matrix
#scatter_matrix(iris_data, diagonal='hist')
scatter_matrix(iris_data, diagonal='kde')
plt.show()
# seabon
sns.pairplot(iris_data, hue = 'Species', height = 1)
plt.show()
x = iris_data['Sepal.Length'].values
sns.rugplot(x)
plt.show()
sns.kdeplot(x)
plt.show()
# pandas의 시각화 기능
import numpy as np
df = pd.DataFrame(np.random.randn(10, 3), index = pd.date_range('1/1/2000', periods=10), columns=['a', 'b', 'c'])
print(df)
#df.plot() # 꺽은선
#df.plot(kind= 'bar')
df.plot(kind= 'box')
plt.xlabel('time')
plt.ylabel('data')
plt.show()
df[:5].plot.bar(rot=0)
plt.show()
* mat4.py
import pandas as pd
tips = pd.read_csv('../testdata/tips.csv')
print(tips.info())
print(tips.head(3))
'''
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
'''tips['gender'] = tips['sex'] # sex 칼럼을 gender 칼럼으로 변경
del tips['sex']
print(tips.head(3))
'''
total_bill tip smoker day time size gender
0 16.99 1.01 No Sun Dinner 2 Female
1 10.34 1.66 No Sun Dinner 3 Male
2 21.01 3.50 No Sun Dinner 3 Male
'''# tip 비율 추가
tips['tip_pct'] = tips['tip'] / tips['total_bill']
print(tips.head(3))
'''
total_bill tip smoker day time size gender tip_pct
0 16.99 1.01 No Sun Dinner 2 Female 0.059447
1 10.34 1.66 No Sun Dinner 3 Male 0.160542
2 21.01 3.50 No Sun Dinner 3 Male 0.166587
'''
tip_pct_group = tips['tip_pct'].groupby([tips['gender'], tips['smoker']]) # 성별, 흡연자별 그륩화
print(tip_pct_group)
print(tip_pct_group.sum())
print(tip_pct_group.max())
print(tip_pct_group.min())
result = tip_pct_group.describe()
print(result)
'''
count mean std ... 50% 75% max
gender smoker ...
Female No 54.0 0.156921 0.036421 ... 0.149691 0.181630 0.252672
Yes 33.0 0.182150 0.071595 ... 0.173913 0.198216 0.416667
Male No 97.0 0.160669 0.041849 ... 0.157604 0.186220 0.291990
Yes 60.0 0.152771 0.090588 ... 0.141015 0.191697 0.710345print(tip_pct_group.agg('sum'))
print(tip_pct_group.agg('mean'))
print(tip_pct_group.agg('max'))
print(tip_pct_group.agg('min'))
def diffFunc(group):
diff = group.max() - group.min()
return diff
result2 = tip_pct_group.agg(['var', 'mean', 'max', diffFunc])
print(result2)
'''
var mean max diffFunc
gender smoker
Female No 0.001327 0.156921 0.252672 0.195876
Yes 0.005126 0.182150 0.416667 0.360233
Male No 0.001751 0.160669 0.291990 0.220186
Yes 0.008206 0.152771 0.710345 0.674707
'''import matplotlib.pyplot as plt
result2.plot(kind = 'barh', title = 'aff fund', stacked = True)
plt.show()