多元回归分析数据集_白酒数据集分析

%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
 
color = sns.color_palette()
pd.set_option('precision', 3)

df = pd.read_csv(r"winequality-white.csv",sep = ';')

df.head(10)

df.info()

df.describe()

df['quality'].describe()
 
#了解一下质量评分的描述性统计的情况

print(df.isnull().sum())
 
#可以看出这个数据集没有空值

# set plot style
plt.style.use('ggplot')
 
#对于每个特征变量画出箱型图
colnm = df.columns.tolist()
fig = plt.figure(figsize = (10, 6))
 
for i in range(12):
    plt.subplot(2,6, i+1)
    sns.boxplot(df[colnm[i]], orient='v', width = 0.5, color = color[0])
    plt.ylabel(colnm[i], fontsize = 12)
    
plt.tight_layout()
print("nFigure 1 :Univariate Boxplots")

#对每个特征变量画出直方图
colnm = df.columns.tolist()
plt.figure(figsize = (10, 8))
 
for i in range(12):
    plt.subplot(4, 3, i+1)
    df[colnm[i]].hist(bins = 100, color = color[0])
    plt.xlabel(colnm[i], fontsize = 12)
    plt.ylabel('Frequency')
plt.tight_layout()
print('nFigure 2 :Univariate Histograms')

acidityFeat = ['fixed acidity', 'volatile acidity', 'citric acid',
                 'free sulfur dioxide', 'total sulfur dioxide', 'sulphates']
 
plt.figure(figsize = (10, 4))
 
for i in range(6):
    ax = plt.subplot(2,3,i + 1)
    v = np.log10(np.clip(df[acidityFeature[i]].values, a_min = 0.001,
                        a_max = None))
    plt.hist(v, bins = 50, color = color[0])
    
    plt.xlabel('log(' + acidityFeat[i] + ')', fontsize = 12)
    plt.ylabel('Frequency')
plt.tight_layout()
print('nFigure 3: Acidity Features in log10 Scale')

plt.figure(figsize = (6,3))
 
bins = 10 ** (np.linspace(-2, 2))
plt.hist(df['fixed acidity'], bins = bins, edgecolor = 'k', label = 
        'Fixed Acidity')
plt.hist(df['volatile acidity'], bins = bins, edgecolor = 'k', label = 
        'Volatile Acidity')
plt.hist(df['citric acid'], bins = bins, edgecolor = 'k', alpha = 0.8,
        label = 'Citric Acid')
plt.xscale('log')
plt.xlabel('Acid Concentration (g/dm^3)')
plt.ylabel('Frequency')
plt.title('Histogram of Acid Concentration')
plt.legend()
plt.tight_layout()
 
print('Figure 4')

#总酸度
df['total acid'] = df['fixed acidity'] + df['volatile acidity'] + df['citric acid']
 
plt.figure(figsize = (8, 3))
 
plt.subplot(121)
plt.hist(df['total acid'], bins = 50, color = color[0])
plt.xlabel('total acid')
plt.ylabel('Frequency')
plt.subplot(222)
plt.hist(np.log(df['total acid']), bins = 50, color = color[0])
plt.xlabel('log(total acid)')
plt.ylabel('Frequency')
plt.tight_layout()
 
print("Figure 5: Total Acid Histogram")

df['sweetness'] = pd.cut(df['residual sugar'], bins = [0, 4, 12, 45], 
                         labels=["dry", "medium dry", "semi-sweet"])
 
plt.figure(figsize = (5,3))
df['sweetness'].value_counts().plot(kind = 'bar', color = color[0])
plt.xticks(rotation=0)
plt.xlabel('sweetness', fontsize = 12)
plt.ylabel('Frequency', fontsize = 12)
plt.tight_layout()
print("Figure 6: Sweetness")

sns.set_style('ticks')
sns.set_context("notebook", font_scale=1.1)
 
colnm = df.columns.tolist()[:11] + ['total acid']
plt.figure(figsize = (10, 8))
 
for i in range(12):
    plt.subplot(4,3,i + 1)
    sns.boxplot(x = 'quality', y = colnm[i], data = df, color = color[1], width = 0.6)
    plt.ylabel(colnm[i], fontsize = 12)
plt.tight_layout()
print("nFigure 7: Physicochemical Properties and Wine Quality by Boxplot")

sns.set_style("dark")
 
plt.figure(figsize = (10,8))
colnm = df.columns.tolist()[:11] + ['total acid', 'quality']
mcorr = df[colnm].corr()
mask = np.zeros_like(mcorr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
cmap = sns.diverging_palette(220, 10, as_cmap=True)
g = sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True,
               fmt='0.2f')
print("nFigure 8: Pairwise Correlation Plot")

sns.set_style('ticks')
sns.set_context("notebook", font_scale = 1.4)
 
plt.figure(figsize = (6,4))
sns.regplot(x = 'density', y = 'alcohol', data = df, scatter_kws = {
    's':10
}, color = color[1])
plt.xlim(0.986,1.040)
plt.ylim(6.5,16)
print("nFigure 9: Desity vs Alcohol")

acidity_related = ['fixed acidity', 'volatile acidity', 'total sulfur dioxide', 
                   'sulphates', 'total acid']
 
plt.figure(figsize = (10, 6))
 
for i in range(5):
    plt.subplot(2,3,i + 1)
    sns.regplot(x='pH', y = acidity_related[i], data = df, scatter_kws = {
        's':10
    }, color = color[1])
plt.tight_layout()
print("Figure 10: pH vs acid")

#酒精浓度、挥发性酸度和品质
plt.style.use('ggplot')
 
sns.lmplot(x = 'alcohol', y = 'volatile acidity', hue = 'quality',
          data = df, fit_reg = False, scatter_kws={'s':10},
          height = 5)
print("Figure 11-1:Scatter Plots of Alcohol, Volatile Acid and Quality")

sns.lmplot(x = 'alcohol', y = 'volatile acidity', col='quality', hue=
          'quality',
          data = df, fit_reg = False, height = 3, aspect = 0.9, col_wrap=3,
          scatter_kws={'s':20})
print("Figure 11-2: Scatter Plots of Alcohol, Volatile Acid and Quality")

#pH和非挥发性酸以及柠檬酸有相关性。整体趋势是浓度越高，pH越低。
sns.set_style('ticks')
sns.set_context("notebook", font_scale = 1.4)
 
plt.figure(figsize = (6,5))
cm = plt.cm.get_cmap('RdBu')
sc = plt.scatter(df['fixed acidity'],df['citric acid'],c=df['pH'],
                vmin=2.6, vmax=4, s=15, cmap=cm)
bar = plt.colorbar(sc)
bar.set_label('pH', rotation = 0)
plt.xlabel('fixed acidity')
plt.ylabel('ctric acid')
plt.xlim(4,18)
plt.ylim(0,1)
print('Figure 12: pH with Fixed Acidity and Citric Acid')