[Data Science] 데이터 시각화 (3)

데이터 파일 불러오기

 

test_data.csv

0.04MB

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

plt.rcParams["font.family"] = "Malgun Gothic"
df = pd.read_csv("test_data.csv")

df.head(10)

 

데이터 구조 확인

 

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 13 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   id           1000 non-null   int64  
 1   age          1000 non-null   int64  
 2   gender       1000 non-null   int64  
 3   height       1000 non-null   int64  
 4   weight       1000 non-null   float64
 5   ap_hi        1000 non-null   int64  
 6   ap_lo        1000 non-null   int64  
 7   cholesterol  1000 non-null   int64  
 8   gluc         1000 non-null   int64  
 9   smoke        1000 non-null   int64  
 10  alco         1000 non-null   int64  
 11  active       1000 non-null   int64  
 12  cardio       1000 non-null   int64  
dtypes: float64(1), int64(12)
memory usage: 101.7 KB

df.describe()

 

컬럼 추가 및 특정 속성값 범주화

 

# 과체중 overweight 컬럼 추가
df['overweight'] = np.where(df['weight']/((df['height']/100)**2) > 25, 1, 0)

# 'cholesterol' 또는 'gluc'의 값이 1이면 값을 0으로 만들고, 값이 1보다 크면 값을 1로 만들기
df['cholesterol'] = np.where(df['cholesterol'] == 1, 0, 1)
df['gluc'] = np.where(df['gluc'] == 1, 0, 1)

df.head(5)

 

categorical plots : stripplot

 

graph = sns.stripplot(x="gender", y="ap_lo", data=df[:200])

 

categorical plots : violinplot & swarmplot

 

sns.violinplot(x="gender", y="ap_hi", data=df[:100])
sns.swarmplot(x="gender", y="ap_hi", data=df[:100], color='white')

 

categorical plots : boxenplot

 

sns.boxenplot(x="gender", y="ap_hi", data=df[:100])

 

categorical plots : catplot

 

sns.catplot(x="smoke", y="cholesterol", col="gender", data=df[:200], kind="bar")

df_cat = pd.melt(df, id_vars=['cardio'], value_vars=['cholesterol', 'gluc', 'smoke', 'alco', 'active', 'overweight'])
df_cat = df_cat.groupby(['cardio','variable','value'], as_index = False).size().rename(columns = {'size':'total'})

sns.catplot(x = 'variable', hue = 'value', col = 'cardio', y = 'total', kind = 'bar', data = df_cat).set_axis_labels("variable", "total")

 

Outlier (이상치) 데이터 제외

 

df_heat = df[(df['ap_lo'] <= df['ap_hi']) &
             (df['height'] >= df['height'].quantile(0.025)) &
             (df['height'] <= df['height'].quantile(0.975)) &
             (df['weight'] >= df['weight'].quantile(0.025)) &
             (df['weight'] <= df['weight'].quantile(0.975))]

df_heat.describe()

 

상관 관계 분석

 

corr = df_heat.corr()
corr

 

Heatmap 그리기

 

plt.figure(figsize=(10,10))
sns.heatmap(corr, annot=True, fmt = '.2f', linewidths=.5, cmap='Reds')