[EDA] 탐색적 데이터 분석, 와인 데이터 Wine Data

와인 품질 EDA¶

• https://github.com/cbrownley/foundations-for-analytics-with-python

• 와인의 품질에는 어떤 요소가 영향을 많이 줄지, 그 영향이 통계적으로 유의한지 알아보자

In [108]:

#라이브러리 정의

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf

In [23]:

#데이터 읽어오기, 테이블 살짝 조정해주기

wine = pd.read_csv('./winequality-both.csv', sep=',', header=0)
wine.columns = wine.columns.str.replace(' ', '_')
wine

Out[23]:

	type	fixed_acidity	volatile_acidity	citric_acid	residual_sugar	chlorides	free_sulfur_dioxide	total_sulfur_dioxide	density	pH	sulphates	alcohol	quality
0	red	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.99780	3.51	0.56	9.4	5
1	red	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.99680	3.20	0.68	9.8	5
2	red	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.99700	3.26	0.65	9.8	5
3	red	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.99800	3.16	0.58	9.8	6
4	red	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.99780	3.51	0.56	9.4	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...
6492	white	6.2	0.21	0.29	1.6	0.039	24.0	92.0	0.99114	3.27	0.50	11.2	6
6493	white	6.6	0.32	0.36	8.0	0.047	57.0	168.0	0.99490	3.15	0.46	9.6	5
6494	white	6.5	0.24	0.19	1.2	0.041	30.0	111.0	0.99254	2.99	0.46	9.4	6
6495	white	5.5	0.29	0.30	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7
6496	white	6.0	0.21	0.38	0.8	0.020	22.0	98.0	0.98941	3.26	0.32	11.8	6

6497 rows × 13 columns

In [98]:

#결측치 확인하기

wine.isnull().sum()

Out[98]:

type                    0
fixed_acidity           0
volatile_acidity        0
citric_acid             0
residual_sugar          0
chlorides               0
free_sulfur_dioxide     0
total_sulfur_dioxide    0
density                 0
pH                      0
sulphates               0
alcohol                 0
quality                 0
dtype: int64

• 결측치 없음

In [99]:

#요약 정보 확인하기

wine.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6497 entries, 0 to 6496
Data columns (total 13 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   type                  6497 non-null   object 
 1   fixed_acidity         6497 non-null   float64
 2   volatile_acidity      6497 non-null   float64
 3   citric_acid           6497 non-null   float64
 4   residual_sugar        6497 non-null   float64
 5   chlorides             6497 non-null   float64
 6   free_sulfur_dioxide   6497 non-null   float64
 7   total_sulfur_dioxide  6497 non-null   float64
 8   density               6497 non-null   float64
 9   pH                    6497 non-null   float64
 10  sulphates             6497 non-null   float64
 11  alcohol               6497 non-null   float64
 12  quality               6497 non-null   int64  
dtypes: float64(11), int64(1), object(1)
memory usage: 660.0+ KB

In [3]:

#요약 통계 확인하기

wine.describe()

Out[3]:

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000	6497.000000
mean	7.215307	0.339666	0.318633	5.443235	0.056034	30.525319	115.744574	0.994697	3.218501	0.531268	10.491801	5.818378
std	1.296434	0.164636	0.145318	4.757804	0.035034	17.749400	56.521855	0.002999	0.160787	0.148806	1.192712	0.873255
min	3.800000	0.080000	0.000000	0.600000	0.009000	1.000000	6.000000	0.987110	2.720000	0.220000	8.000000	3.000000
25%	6.400000	0.230000	0.250000	1.800000	0.038000	17.000000	77.000000	0.992340	3.110000	0.430000	9.500000	5.000000
50%	7.000000	0.290000	0.310000	3.000000	0.047000	29.000000	118.000000	0.994890	3.210000	0.510000	10.300000	6.000000
75%	7.700000	0.400000	0.390000	8.100000	0.065000	41.000000	156.000000	0.996990	3.320000	0.600000	11.300000	6.000000
max	15.900000	1.580000	1.660000	65.800000	0.611000	289.000000	440.000000	1.038980	4.010000	2.000000	14.900000	9.000000

In [25]:

#레드 와인, 화이트 와인의 개수와 품질 고유값 찾아내기

print(wine.quality.value_counts())
print(wine.type.value_counts())

6    2836
5    2138
7    1079
4     216
8     193
3      30
9       5
Name: quality, dtype: int64
white    4898
red      1599
Name: type, dtype: int64

In [90]:

#시각화하면서 탐색하기

sns.set_theme(style='dark')

red_wine = wine.loc[wine['type'] == 'red', 'quality']
white_wine = wine.loc[wine['type'] == 'white', 'quality']

print(sns.histplot(data=red_wine, color='Red', label='Red Wine'))
print(sns.histplot(data=white_wine, color='white', label='White Wine'))
plt.title('Counts of Wine by Quality')

AxesSubplot(0.125,0.11;0.775x0.77)
AxesSubplot(0.125,0.11;0.775x0.77)

Out[90]:

Text(0.5, 1.0, 'Counts of Wine by Quality')

In [26]:

#상관관계 분석하기

wine.corr()

C:\Users\Administrator\AppData\Local\Temp\ipykernel_32632\3997320868.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  wine.corr()

Out[26]:

	fixed_acidity	volatile_acidity	citric_acid	residual_sugar	chlorides	free_sulfur_dioxide	total_sulfur_dioxide	density	pH	sulphates	alcohol	quality
fixed_acidity	1.000000	0.219008	0.324436	-0.111981	0.298195	-0.282735	-0.329054	0.458910	-0.252700	0.299568	-0.095452	-0.076743
volatile_acidity	0.219008	1.000000	-0.377981	-0.196011	0.377124	-0.352557	-0.414476	0.271296	0.261454	0.225984	-0.037640	-0.265699
citric_acid	0.324436	-0.377981	1.000000	0.142451	0.038998	0.133126	0.195242	0.096154	-0.329808	0.056197	-0.010493	0.085532
residual_sugar	-0.111981	-0.196011	0.142451	1.000000	-0.128940	0.402871	0.495482	0.552517	-0.267320	-0.185927	-0.359415	-0.036980
chlorides	0.298195	0.377124	0.038998	-0.128940	1.000000	-0.195045	-0.279630	0.362615	0.044708	0.395593	-0.256916	-0.200666
free_sulfur_dioxide	-0.282735	-0.352557	0.133126	0.402871	-0.195045	1.000000	0.720934	0.025717	-0.145854	-0.188457	-0.179838	0.055463
total_sulfur_dioxide	-0.329054	-0.414476	0.195242	0.495482	-0.279630	0.720934	1.000000	0.032395	-0.238413	-0.275727	-0.265740	-0.041385
density	0.458910	0.271296	0.096154	0.552517	0.362615	0.025717	0.032395	1.000000	0.011686	0.259478	-0.686745	-0.305858
pH	-0.252700	0.261454	-0.329808	-0.267320	0.044708	-0.145854	-0.238413	0.011686	1.000000	0.192123	0.121248	0.019506
sulphates	0.299568	0.225984	0.056197	-0.185927	0.395593	-0.188457	-0.275727	0.259478	0.192123	1.000000	-0.003029	0.038485
alcohol	-0.095452	-0.037640	-0.010493	-0.359415	-0.256916	-0.179838	-0.265740	-0.686745	0.121248	-0.003029	1.000000	0.444319
quality	-0.076743	-0.265699	0.085532	-0.036980	-0.200666	0.055463	-0.041385	-0.305858	0.019506	0.038485	0.444319	1.000000

In [36]:

#상관관계 시각화

sns.heatmap(data=wine.corr(), cmap='Blues')

C:\Users\Administrator\AppData\Local\Temp\ipykernel_32632\2611584541.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(data=wine.corr(), cmap='Blues')

Out[36]:

<AxesSubplot: >

• 1. quality와 alcohol이 유의미한 상관관계를 가진다.

• 1. residual_sugar와 density가 유의미한 상관관계를 가진다.

• 1. density와 alcohol이 유의미한 상관관계를 가진다.

In [93]:

#분석한 상관관계에 따라 두 개 이상의 변수 탐색하기

sns.set_style('whitegrid')
sns.barplot(wine, x='quality', y='alcohol', hue='type', palette='RdBu')
plt.title('1. Quality by Alcohol')

Out[93]:

Text(0.5, 1.0, '1. Quality by Alcohol')

In [96]:

sns.pointplot(wine, x='quality', y='alcohol', hue='type', ci=None, palette='RdBu')
plt.title('Alcohol changes by quality')

C:\Users\Administrator\AppData\Local\Temp\ipykernel_32632\2890491864.py:1: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.pointplot(wine, x='quality', y='alcohol', hue='type', ci=None, palette='RdBu')

Out[96]:

Text(0.5, 1.0, 'Alcohol changes by quality')

In [74]:

sns.barplot(wine, x='density', y='residual_sugar', hue='type', palette='summer')
plt.title('2. Residual Sugar by Density')

Out[74]:

Text(0.5, 1.0, '2. Residual Sugar by Density')

In [95]:

sns.lineplot(data=wine, x='density', y='alcohol', hue='type', ci=None, palette='RdBu')
plt.title('3. Alcohol by Density')

C:\Users\Administrator\AppData\Local\Temp\ipykernel_32632\1594631851.py:1: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.lineplot(data=wine, x='density', y='alcohol', hue='type', ci=None, palette='RdBu')

Out[95]:

Text(0.5, 1.0, '3. Alcohol by Density')

In [106]:

#와인 종류에 따라 품질의 차이가 통계적으로 유의미한지 알아보기 위해 그룹화, t검정 실시

print(wine.groupby(['type'])[['quality']].agg(['std', 'mean']))
tstat, pvalue, df = sm.stats.ttest_ind(red_wine, white_wine)
print('tstat: %.3f pvalue: %.4f' % (tstat, pvalue))

        quality          
            std      mean
type                     
red    0.807569  5.636023
white  0.885639  5.877909
tstat: -9.686 pvalue: 0.0000

• 유의확률 pvalue값이 유의수준(0.05)보다 작으므로 종류에 따른 품질의 차이가 통계적으로 유의미하다.

In [114]:

sns.set_style('dark')

sns.pairplot(data=wine, kind='reg', diag_kind='hist', diag_kws={'bins': 10, 'alpha': 1.0}, palette=dict(red='red', white='white'), vars=['quality', 'alcohol', 'density'], markers=['o', 's'], hue='type')
plt.suptitle('Histograms and Scatter Plots og Quality, Alcohol, and Density', fontsize=14, horizontalalignment='center', verticalalignment='top', x=0.5, y=0.999)
plt.show()