[Python][AI] Exploratory Data Analysis (EDA) - Wine Quality Dataset - 2

 

2025.02.10 - [개발 Code/인공지능 A.I.] - [Python][AI] Exploratory Data Analysis (EDA) - Wine Quality Dataset - 1

[Python][AI] Exploratory Data Analysis (EDA) - Wine Quality Dataset - 1

 

  Continuing from the previous analysis, we will now load the dataset and examine its basic information and summary statistics.

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Loading the Data

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# Library Version
# pandas    : 2.2.1
# numpy     : 1.26.4
# matplotlib: 3.9.2

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

# data load
# Define CSV file paths
red_wine_path = './wine+quality/winequality-red.csv'
white_wine_path = './wine+quality/winequality-white.csv'

# Load CSV files
red_wine = pd.read_csv(red_wine_path, sep=';')
white_wine = pd.read_csv(white_wine_path, sep=';')

# Display first few rows
red_wine.head()
#	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
# 0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
# 1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
# 2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
# 3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
# 4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

white_wine.head()
#	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
# 0	7.0	0.27	0.36	20.7	0.045	45.0	170.0	1.0010	3.00	0.45	8.8	6
# 1	6.3	0.30	0.34	1.6	0.049	14.0	132.0	0.9940	3.30	0.49	9.5	6
# 2	8.1	0.28	0.40	6.9	0.050	30.0	97.0	0.9951	3.26	0.44	10.1	6
# 3	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6
# 4	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6

 

Since the CSV files are separated by ;, we specify sep=';' in read_csv() to correctly load the data.

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Exploring the Dataset

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1. Basic Information Summary

red_wine.info()

# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 1599 entries, 0 to 1598
# Data columns (total 12 columns):
#  #   Column                Non-Null Count  Dtype  
# ---  ------                --------------  -----  
#  0   fixed acidity         1599 non-null   float64
#  1   volatile acidity      1599 non-null   float64
#  2   citric acid           1599 non-null   float64
#  3   residual sugar        1599 non-null   float64
#  4   chlorides             1599 non-null   float64
#  5   free sulfur dioxide   1599 non-null   float64
#  6   total sulfur dioxide  1599 non-null   float64
#  7   density               1599 non-null   float64
#  8   pH                    1599 non-null   float64
#  9   sulphates             1599 non-null   float64
#  10  alcohol               1599 non-null   float64
#  11  quality               1599 non-null   int64  
# dtypes: float64(11), int64(1)
# memory usage: 150.0 KB

white_wine.info()

# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 4898 entries, 0 to 4897
# Data columns (total 12 columns):
#  #   Column                Non-Null Count  Dtype  
# ---  ------                --------------  -----  
#  0   fixed acidity         4898 non-null   float64
#  1   volatile acidity      4898 non-null   float64
#  2   citric acid           4898 non-null   float64
#  3   residual sugar        4898 non-null   float64
#  4   chlorides             4898 non-null   float64
#  5   free sulfur dioxide   4898 non-null   float64
#  6   total sulfur dioxide  4898 non-null   float64
#  7   density               4898 non-null   float64
#  8   pH                    4898 non-null   float64
#  9   sulphates             4898 non-null   float64
#  10  alcohol               4898 non-null   float64
#  11  quality               4898 non-null   int64  
# dtypes: float64(11), int64(1)
# memory usage: 459.3 KB

 

Red Wine Dataset

• Total Samples: 1,599
• Columns: 12 (11 numerical features + 1 quality score)
• Data Types:
  • 11 float64 columns (chemical properties)
  • 1 int64 column (quality)
• Missing Values: None

White Wine Dataset

• Total Samples: 4,898
• Columns: 12 (same as red wine)
• Data Types:
  • 11 float64 columns
  • 1 int64 column (quality)
• Missing Values: None

Both datasets contain no missing values, and the quality column is the only integer variable.

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2. Descriptive Statistics

 

# round(data, ndigits) Rounding Half Even Function
round(red_wine.describe(), 4)

# 	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
# count	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000	1599.0000
# mean	8.3196	0.5278	0.2710	2.5388	0.0875	15.8749	46.4678	0.9967	3.3111	0.6581	10.4230	5.6360
# std	1.7411	0.1791	0.1948	1.4099	0.0471	10.4602	32.8953	0.0019	0.1544	0.1695	1.0657	0.8076
# min	4.6000	0.1200	0.0000	0.9000	0.0120	1.0000	6.0000	0.9901	2.7400	0.3300	8.4000	3.0000
# 25%	7.1000	0.3900	0.0900	1.9000	0.0700	7.0000	22.0000	0.9956	3.2100	0.5500	9.5000	5.0000
# 50%	7.9000	0.5200	0.2600	2.2000	0.0790	14.0000	38.0000	0.9968	3.3100	0.6200	10.2000	6.0000
# 75%	9.2000	0.6400	0.4200	2.6000	0.0900	21.0000	62.0000	0.9978	3.4000	0.7300	11.1000	6.0000
# max	15.9000	1.5800	1.0000	15.5000	0.6110	72.0000	289.0000	1.0037	4.0100	2.0000	14.9000	8.0000

round(white_wine.describe(), 4)

# 	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
# count	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000	4898.0000
# mean	6.8548	0.2782	0.3342	6.3914	0.0458	35.3081	138.3607	0.9940	3.1883	0.4898	10.5143	5.8779
# std	0.8439	0.1008	0.1210	5.0721	0.0218	17.0071	42.4981	0.0030	0.1510	0.1141	1.2306	0.8856
# min	3.8000	0.0800	0.0000	0.6000	0.0090	2.0000	9.0000	0.9871	2.7200	0.2200	8.0000	3.0000
# 25%	6.3000	0.2100	0.2700	1.7000	0.0360	23.0000	108.0000	0.9917	3.0900	0.4100	9.5000	5.0000
# 50%	6.8000	0.2600	0.3200	5.2000	0.0430	34.0000	134.0000	0.9937	3.1800	0.4700	10.4000	6.0000
# 75%	7.3000	0.3200	0.3900	9.9000	0.0500	46.0000	167.0000	0.9961	3.2800	0.5500	11.4000	6.0000
# max	14.2000	1.1000	1.6600	65.8000	0.3460	289.0000 440.0000	1.0390	3.8200	1.0800	14.2000	9.0000

 

Feature	Red Wine ( Mean ± Std)	White Wine (Mean ± Std)
Fixed Acidity	8.32 ± 1.74	6.85 ± 0.84
Volatile Acidity	0.53 ± 0.18	0.28 ± 0.10
Citric Acid	0.27 ± 0.19	0.33 ± 0.12
Residual Sugar	2.54 ± 1.41	6.39 ± 5.07
Chlorides	0.088 ± 0.047	0.046 ± 0.022
Free Sulfur Dioxide	15.87 ± 10.46	35.31 ± 17.01
Total Sulfur Dioxide	46.47 ± 32.90	138.36 ± 42.50
Density	0.9967 ± 0.0019	0.9940 ± 0.0030
pH	3.31 ± 0.15	3.19 ± 0.15
Alcohol	10.42 ± 1.07	10.51 ± 1.23
Quality Score	5.64 ± 0.81	5.88 ± 0.89

 

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Key Insights from Data Analysis

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(1) Fixed Acidity

• Red Wine: Mean = 8.32, Std = 1.74
• White Wine: Mean = 6.85, Std = 0.84
• Interpretation:
  • Red wine has a higher fixed acidity than white wine.
  • This suggests that red wine tends to have a stronger tartness.
  • The wider distribution in red wine indicates greater variety in acidity levels.

(2) Volatile Acidity

• Red Wine: Mean = 0.53, Std = 0.18
• White Wine: Mean = 0.28, Std = 0.10
• Interpretation:
  • Red wine has almost double the volatile acidity compared to white wine.
  • This means volatile acidity is more likely to impact red wine quality.
  • Since white wine has lower volatile acidity, it is less likely to have a negative impact on quality.

(3) Citric Acid

• Red Wine: Mean = 0.27, Std = 0.19
• White Wine: Mean = 0.33, Std = 0.12
• Interpretation:
  • White wine contains more citric acid, contributing to a fresher, crispier taste.
  • Red wine shows a wider distribution, meaning greater variability in citric acid levels.

(4) Residual Sugar

• Red Wine: Mean = 2.54, Max = 15.5
• White Wine: Mean = 6.39, Max = 65.8
• Interpretation:
  • White wine has significantly higher residual sugar, making it noticeably sweeter.
  • White wine’s maximum value (65.8) is much higher than red wine’s (15.5), suggesting potential outliers.

(5) Chlorides

• Red Wine: Mean = 0.087, Max = 0.611
• White Wine: Mean = 0.046, Max = 0.346
• Interpretation:
  • Red wine has almost twice the chloride concentration as white wine.
  • Higher chloride levels can negatively affect wine quality.

(6) Free Sulfur Dioxide

• Red Wine: Mean = 15.87, Max = 72
• White Wine: Mean = 35.31, Max = 289
• Interpretation:
  • White wine has over twice the free sulfur dioxide compared to red wine.
  • White wine needs more sulfur dioxide to prevent oxidation and maintain freshness.

(7) Total Sulfur Dioxide

• Red Wine: Mean = 46.47, Max = 289
• White Wine: Mean = 138.36, Max = 440
• Interpretation:
  • White wine contains almost three times the total sulfur dioxide.
  • This highlights an important preservation difference between red and white wines.

(8) Density

• Red Wine: Mean = 0.9967
• White Wine: Mean = 0.9940
• Interpretation:
  • White wine has lower density, likely due to higher alcohol and sugar content.

(9) pH

• Red Wine: Mean = 3.31
• White Wine: Mean = 3.19
• Interpretation:
  • Red wine has a slightly higher pH, meaning it is less acidic than white wine.
  • This could result in a smoother, rounder taste for red wine.

(10) Alcohol Content

• Red Wine: Mean = 10.42, Max = 14.9
• White Wine: Mean = 10.51, Max = 14.2
• Interpretation:
  • Both wines have similar alcohol distributions, with white wine being slightly higher on average.

(11) Quality Score

• Red Wine: Mean = 5.64, Max = 8
• White Wine: Mean = 5.88, Max = 9
• Interpretation:
  • White wine has a slightly higher average quality score.
  • The highest recorded quality score is higher for white wine.

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Summary of Findings

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• Acidity: Red wine is more acidic than white wine.
• Sweetness: White wine has significantly more residual sugar.
• Sulfur Dioxide: White wine requires more sulfur dioxide for preservation.
• Quality: White wine has a slightly higher average quality score.