TooTouch

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("whitegrid")

import matplotlib as mpl
plt.rcParams["font.family"] = 'NanumGothicCoding'
mpl.rcParams['axes.unicode_minus'] = False

TooTouch

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

from math import pi

import matplotlib as mpl
plt.rcParams["font.family"] = 'NanumGothicCoding'
mpl.rcParams['axes.unicode_minus'] = False

TooTouch

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("whitegrid")

from tslearn.generators import random_walks
from tslearn.clustering import TimeSeriesKMeans

import matplotlib as mpl

TooTouch

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

from matplotlib.pyplot import imread

def cat_plot(cluster: list, save=False):    
    # 이미지 불러오기 
    cat_images = [imread(f'../images/category icon/mcorp_sample/cat{i+1}.png') for i in range(15)]
    legend_image = imread('../images/category icon/mcorp_sample/legend.png')

TooTouch

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

from matplotlib.pyplot import imread

def sex_age_plot(cluster: int, save=False):
    # 이미지 불러오기
    female_images = [imread(f'../images/ages icon/F{i}.png') for i in range(10,80,10)]
    male_images = [imread(f'../images/ages icon/M{i}.png') for i in range(10,80,10)]    

TooTouch

TooTouch

TooTouch