一.数据查看

数据集地址,用红白酒为例．

import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import matplotlib as mpl import numpy as np import seaborn as snswhite_wine = pd.read_csv('winequality-white.csv', sep=';') red_wine = pd.read_csv('winequality-red.csv', sep=';')# store wine type as an attribute red_wine['wine_type'] = 'red' white_wine['wine_type'] = 'white'# bucket wine quality scores into qualitative quality labels red_wine['quality_label'] = red_wine['quality'].apply(lambda value: 'low'if value <= 5 else 'medium'if value <= 7 else 'high') red_wine['quality_label'] = pd.Categorical(red_wine['quality_label'],categories=['low', 'medium', 'high']) white_wine['quality_label'] = white_wine['quality'].apply(lambda value: 'low'if value <= 5 else 'medium'if value <= 7 else 'high') white_wine['quality_label'] = pd.Categorical(white_wine['quality_label'],categories=['low', 'medium', 'high'])# merge red and white wine datasets wines = pd.concat([red_wine, white_wine])# print('wines.head()\n', wines.head()) # re-shuffle records just to randomize data points wines = wines.sample(frac=1, random_state=42).reset_index(drop=True) print('wines.head()\n', wines.head())subset_attributes = ['residual sugar', 'total sulfur dioxide', 'sulphates','alcohol', 'volatile acidity', 'quality'] rs = round(red_wine[subset_attributes].describe(), 2) ws = round(white_wine[subset_attributes].describe(), 2)rs_ws = pd.concat([rs, ws], axis=1, keys=['Red Wine Statistics', 'White Wine Statistics'])

二.数据分析

1.每个特性　都做直方图

wines.hist(bins=15, color='steelblue', edgecolor='black', linewidth=1.0,xlabelsize=8, ylabelsize=8, grid=False, figsize=(12.8, 9.6)) # plt.tight_layout(rect=(0, 0, 1.2, 1.2)) plt.savefig('./wines_analysis.jpg')

2.sulphates属性做直方图和核密度估计

fig = plt.figure(figsize=(6, 4)) title = fig.suptitle("Sulphates Content in Wine", fontsize=14) fig.subplots_adjust(top=0.85, wspace=0.3)ax = fig.add_subplot(1, 1, 1) ax.set_xlabel("Sulphates") ax.set_ylabel("Frequency") ax.text(1.2, 800, r'$\mu$='+str(round(wines['sulphates'].mean(), 2)),fontsize=12) freq, bins, patches = ax.hist(wines['sulphates'], color='steelblue', bins=15,edgecolor='black', linewidth=1) plt.savefig('./sulphates_historm_analysis.jpg')# Density Plot fig = plt.figure(figsize = (6, 4)) title = fig.suptitle("Sulphates Content in Wine", fontsize=14) fig.subplots_adjust(top=0.85, wspace=0.3)ax1 = fig.add_subplot(1, 1, 1) ax1.set_xlabel("Sulphates") ax1.set_ylabel("Frequency") sns.kdeplot(wines['sulphates'], ax=ax1, shade=True, color='steelblue') plt.savefig('./sulphates_Density_analysis.jpg')

 3.对属性相关性　进行热力图分析

# Correlation Matrix Heatmap f, ax = plt.subplots(figsize=(10, 6)) corr = wines.corr() hm = sns.heatmap(round(corr,2), annot=True, ax=ax, cmap="coolwarm",fmt='.2f',linewidths=.05) f.subplots_adjust(top=0.93) t= f.suptitle('Wine Attributes Correlation Heatmap', fontsize=14) plt.savefig('./Wine_Attributes_Correlation_Heatmap.jpg')

4.seaborn一幅图画两个对比的直方图

# Multi-bar Plot plt.figure() cp = sns.countplot(x="quality", hue="wine_type", data=wines,palette={"red": "#FF9999", "white": "#FFE888"}) plt.savefig('./quality_wine_type.jpg')

  

 5. 用箱线图表示质量和浓度关系

f, (ax) = plt.subplots(1, 1, figsize=(12, 4)) f.suptitle('Wine Quality - Alcohol Content', fontsize=14)sns.boxplot(x="quality", y="alcohol", data=wines, ax=ax) ax.set_xlabel("Wine Quality", size=12, alpha=0.8) ax.set_ylabel("Wine Alcohol %", size=12, alpha=0.8) plt.savefig('./box_quality_Alcohol.jpg')

 

6. 3d view 

fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111, projection='3d')xs = wines['residual sugar'] ys = wines['fixed acidity'] zs = wines['alcohol'] ax.scatter(xs, ys, zs, s=50, alpha=0.6, edgecolors='w')ax.set_xlabel('Residual Sugar') ax.set_ylabel('Fixed Acidity') ax.set_zlabel('Alcohol') plt.savefig('./3d_view.jpg')

7. 用气泡图2d做3d数据的可视化 

plt.figure() # Visualizing 3-D numeric data with a bubble chart # length, breadth and size plt.scatter(wines['fixed acidity'], wines['alcohol'], s=wines['residual sugar']*25,alpha=0.4, edgecolors='w')plt.xlabel('Fixed Acidity') plt.ylabel('Alcohol') plt.title('Wine Alcohol Content - Fixed Acidity - Residual Sugar',y=1.05) plt.savefig('./2d_bubble_view.jpg')

8.用seaborn按照不同质量去分类画直方图

# Visualizing 3-D categorical data using bar plots # leveraging the concepts of hue and facets plt.figure() fc = sns.factorplot(x="quality", hue="wine_type", col="quality_label",data=wines, kind="count",palette={"red": "#FF9999", "white": "#FFE888"}) plt.savefig('./seaborn_quality_classify.jpg')

9.用seaborn 查看两个变量的核密度图

plt.figure() ax = sns.kdeplot(white_wine['sulphates'], white_wine['alcohol'],cmap="YlOrBr", shade=True, shade_lowest=False) ax = sns.kdeplot(red_wine['sulphates'], red_wine['alcohol'],cmap="Reds", shade=True, shade_lowest=False) plt.savefig('./seaborn_see_density.jpg')

 

10. 可视化四维数据用颜色区分

# Visualizing 4-D mix data using scatter plots # leveraging the concepts of hue and depth fig = plt.figure(figsize=(8, 6)) t = fig.suptitle('Wine Residual Sugar - Alcohol Content - Acidity - Type', fontsize=14) ax = fig.add_subplot(111, projection='3d')xs = list(wines['residual sugar']) ys = list(wines['alcohol']) zs = list(wines['fixed acidity']) data_points = [(x, y, z) for x, y, z in zip(xs, ys, zs)] colors = ['red' if wt == 'red' else 'yellow' for wt in list(wines['wine_type'])]for data, color in zip(data_points, colors):x, y, z = dataax.scatter(x, y, z, alpha=0.4, c=color, edgecolors='none', s=30)ax.set_xlabel('Residual Sugar') ax.set_ylabel('Alcohol') ax.set_zlabel('Fixed Acidity') plt.savefig('./view_4d_by_3d.jpg')

11.可视化4d数据 只不过用二维图片　加入大小

# Visualizing 4-D mix data using bubble plots # leveraging the concepts of hue and size size = wines['residual sugar']*25 fill_colors = ['#FF9999' if wt=='red' else '#FFE888' for wt in list(wines['wine_type'])] edge_colors = ['red' if wt=='red' else 'orange' for wt in list(wines['wine_type'])]plt.scatter(wines['fixed acidity'], wines['alcohol'], s=size,alpha=0.4, color=fill_colors, edgecolors=edge_colors)plt.xlabel('Fixed Acidity') plt.ylabel('Alcohol') plt.title('Wine Alcohol Content - Fixed Acidity - Residual Sugar - Type',y=1.05) plt.savefig('./view_4d_by_2d.jpg')

 

12. 可视化5d数据　用3d加大小和颜色

# Visualizing 5-D mix data using bubble charts # leveraging the concepts of hue, size and depth fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111, projection='3d') t = fig.suptitle('Wine Residual Sugar - Alcohol Content - Acidity - Total Sulfur Dioxide - Type', fontsize=14)xs = list(wines['residual sugar']) ys = list(wines['alcohol']) zs = list(wines['fixed acidity']) data_points = [(x, y, z) for x, y, z in zip(xs, ys, zs)]ss = list(wines['total sulfur dioxide']) colors = ['red' if wt == 'red' else 'yellow' for wt in list(wines['wine_type'])]for data, color, size in zip(data_points, colors, ss):x, y, z = dataax.scatter(x, y, z, alpha=0.4, c=color, edgecolors='none', s=size)ax.set_xlabel('Residual Sugar') ax.set_ylabel('Alcohol') ax.set_zlabel('Fixed Acidity') plt.savefig('./view_5d_by_3d.jpg')

 13.可视化6d数据　用3d加大小，颜色和形状

fig = plt.figure(figsize=(8, 6)) t = fig.suptitle('Wine Residual Sugar - Alcohol Content - Acidity - Total Sulfur Dioxide - Type - Quality', fontsize=14) ax = fig.add_subplot(111, projection='3d')xs = list(wines['residual sugar']) ys = list(wines['alcohol']) zs = list(wines['fixed acidity']) data_points = [(x, y, z) for x, y, z in zip(xs, ys, zs)]ss = list(wines['total sulfur dioxide']) colors = ['red' if wt == 'red' else 'yellow' for wt in list(wines['wine_type'])] markers = [',' if q == 'high' else 'x' if q == 'medium' else 'o' for q in list(wines['quality_label'])]for data, color, size, mark in zip(data_points, colors, ss, markers):x, y, z = dataax.scatter(x, y, z, alpha=0.4, c=color, edgecolors='none', s=size, marker=mark)ax.set_xlabel('Residual Sugar') ax.set_ylabel('Alcohol') ax.set_zlabel('Fixed Acidity') plt.savefig('./view_6d_by_3d.jpg')

 

参考

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