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')

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seaborn的一些画图

一.数据查看

二.数据分析

1.每个特性　都做直方图

2.sulphates属性做直方图和核密度估计

3.对属性相关性　进行热力图分析

4.seaborn一幅图画两个对比的直方图

5. 用箱线图表示质量和浓度关系

6. 3d view

7. 用气泡图2d做3d数据的可视化

8.用seaborn按照不同质量去分类画直方图

9.用seaborn 查看两个变量的核密度图

10. 可视化四维数据用颜色区分

11.可视化4d数据只不过用二维图片　加入大小

12. 可视化5d数据　用3d加大小和颜色

13.可视化6d数据　用3d加大小，颜色和形状

seaborn的一些画图相关推荐

最新文章

热门文章

seaborn的一些画图

一.数据查看

二.数据分析

1.每个特性 都做直方图

2.sulphates属性做直方图和核密度估计

3.对属性相关性 进行热力图分析

4.seaborn一幅图画两个对比的直方图

5. 用箱线图表示质量和浓度关系

6. 3d view

7. 用气泡图2d做3d数据的可视化

8.用seaborn按照不同质量去分类画直方图

9.用seaborn 查看两个变量的核密度图

10. 可视化四维数据用颜色区分

11.可视化4d数据 只不过用二维图片 加入大小

12. 可视化5d数据 用3d加大小和颜色

13.可视化6d数据 用3d加大小，颜色和形状

seaborn的一些画图相关推荐

最新文章

热门文章

1.每个特性　都做直方图

3.对属性相关性　进行热力图分析

11.可视化4d数据只不过用二维图片　加入大小

12. 可视化5d数据　用3d加大小和颜色

13.可视化6d数据　用3d加大小，颜色和形状