来源：Kaggle 官方课程 Data Visualization
2024-09-01@isSeymour

Data Visualization

总览：
Data Visualization - sns

展现内容：

  1. Trends 趋势

    代码 功能
    lineplot 折线图 用于展示数据随时间或其他连续变量的变化趋势

  2. Relationship 关系

    代码 功能
    barplot 柱状图 用于展示不同类别或分组数据的数量或频率
    heatmap 热图 用于显示数据的密度或强度，通过颜色的深浅来表示数值的大小
    scatterplot 散点图 用于展示两个变量之间关系
    swarmplot 蜂群图 用于显示数据分布的可视化图表，通过将数据点以散点的方式展示在类别上，并避免数据点重叠。它适用于展示单变量或多变量的离散数据分布，特别是当数据量较小或希望看到每个数据点的具体位置时
    regplot 回归图 用于展示回归分析结果的数据可视化工具。它通常包括一个散点图和一条回归线，帮助直观地展示两个变量之间的关系及其回归模型的拟合效果
    lmplot 线性回归图 用于展示线性回归分析结果的可视化工具。它通过将散点图和线性回归线结合在一起，帮助直观地理解自变量与因变量之间的线性关系

  3. Distribution 分布

    代码 功能
    jointplot 联合图 是一种综合了散点图和边际直方图的数据可视化工具，用于展示两个变量之间的关系及其各自的分布情况。联合图通过将散点图与边际分布图结合，提供了对数据关系和分布的全面视角
    kdeplot 核密度估计图 用于估计和可视化数据分布的图表。它通过核密度估计方法将数据的概率密度函数平滑化，提供了数据分布的平滑估计
    histplot 直方图 用于展示数据分布情况的图表。它通过将数据分成多个区间（或称为“桶”）并计算每个区间中的数据点数目，直观地表示了数据的分布情况

0. 导入库

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import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")
Setup Complete

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museum_filepath = "../input/museum_visitors.csv"
museum_data = pd.read_csv(museum_filepath, index_col="Date", parse_dates=True)
print("Shape: ", museum_data.shape)
museum_data.tail()
Shape:  (59, 4)
Avila Adobe Firehouse Museum Chinese American Museum America Tropical Interpretive Center
Date
2018-07-01 23136 4191 2620 4718
2018-08-01 20815 4866 2409 3891
2018-09-01 21020 4956 2146 3180
2018-10-01 19280 4622 2364 3775
2018-11-01 17163 4082 2385 4562 
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# 折线图
plt.figure(figsize=(12,6))
sns.lineplot(data=museum_data)
plt.title("Monthly Vistors")
Text(0.5, 1.0, 'Monthly Vistors')

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# 折线图
plt.figure(figsize=(12,6))
plt.title("Monthly Vistors to Avila Adobe")
sns.lineplot(data=museum_data["Avila Adobe"])
plt.xlabel("Date")
Text(0.5, 0, 'Date')

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2. Relationship 关系

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ign_filepath = "../input/ign_scores.csv"
ign_data = pd.read_csv(ign_filepath, index_col="Platform")
print("Shape: ", ign_data.shape)
ign_data
Shape:  (21, 12)
Action Action, Adventure Adventure Fighting Platformer Puzzle RPG Racing Shooter Simulation Sports Strategy
Platform
Dreamcast 6.882857 7.511111 6.281818 8.200000 8.340000 8.088889 7.700000 7.042500 7.616667 7.628571 7.272222 6.433333
Game Boy Advance 6.373077 7.507692 6.057143 6.226316 6.970588 6.532143 7.542857 6.657143 6.444444 6.928571 6.694444 7.175000
Game Boy Color 6.272727 8.166667 5.307692 4.500000 6.352941 6.583333 7.285714 5.897436 4.500000 5.900000 5.790698 7.400000
GameCube 6.532584 7.608333 6.753846 7.422222 6.665714 6.133333 7.890909 6.852632 6.981818 8.028571 7.481319 7.116667
Nintendo 3DS 6.670833 7.481818 7.414286 6.614286 7.503448 8.000000 7.719231 6.900000 7.033333 7.700000 6.388889 7.900000
Nintendo 64 6.649057 8.250000 7.000000 5.681250 6.889655 7.461538 6.050000 6.939623 8.042857 5.675000 6.967857 6.900000
Nintendo DS 5.903608 7.240000 6.259804 6.320000 6.840000 6.604615 7.222619 6.038636 6.965217 5.874359 5.936667 6.644737
Nintendo DSi 6.827027 8.500000 6.090909 7.500000 7.250000 6.810526 7.166667 6.563636 6.500000 5.195652 5.644444 6.566667
PC 6.805791 7.334746 7.136798 7.166667 7.410938 6.924706 7.759930 7.032418 7.084878 7.104889 6.902424 7.310207
PlayStation 6.016406 7.933333 6.313725 6.553731 6.579070 6.757895 7.910000 6.773387 6.424000 6.918182 6.751220 6.496875
PlayStation 2 6.467361 7.250000 6.315152 7.306349 7.068421 6.354545 7.473077 6.585065 6.641667 7.152632 7.197826 7.238889
PlayStation 3 6.853819 7.306154 6.820988 7.710938 7.735714 7.350000 7.436111 6.978571 7.219553 7.142857 7.485816 7.355172
PlayStation 4 7.550000 7.835294 7.388571 7.280000 8.390909 7.400000 7.944000 7.590000 7.804444 9.250000 7.430000 6.566667
PlayStation Portable 6.467797 7.000000 6.938095 6.822222 7.194737 6.726667 6.817778 6.401961 7.071053 6.761538 6.956790 6.550000
PlayStation Vita 7.173077 6.133333 8.057143 7.527273 8.568750 8.250000 7.337500 6.300000 7.660000 5.725000 7.130000 8.900000
Wii 6.262718 7.294643 6.234043 6.733333 7.054255 6.426984 7.410345 5.011667 6.479798 6.327027 5.966901 6.975000
Wireless 7.041699 7.312500 6.972414 6.740000 7.509091 7.360550 8.260000 6.898305 6.906780 7.802857 7.417699 7.542857
Xbox 6.819512 7.479032 6.821429 7.029630 7.303448 5.125000 8.277778 7.021591 7.485417 7.155556 7.884397 7.313333
Xbox 360 6.719048 7.137838 6.857353 7.552239 7.559574 7.141026 7.650000 6.996154 7.338153 7.325000 7.317857 7.112245
Xbox One 7.702857 7.566667 7.254545 7.171429 6.733333 8.100000 8.291667 8.163636 8.020000 7.733333 7.331818 8.500000
iPhone 6.865445 7.764286 7.745833 6.087500 7.471930 7.810784 7.185185 7.315789 6.995588 7.328571 7.152174 7.534921 
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# 柱状图
plt.figure(figsize=(8,6))
plt.title("Average Score for Racing Games, for Platform")
sns.barplot(x=ign_data["Racing"], y=ign_data.index)
plt.xlabel("Racing Score")
Text(0.5, 0, 'Racing Score')

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# 热图
plt.figure(figsize=(10,10))
plt.title("Average Game Score, by Platform and Genre")
sns.heatmap(data=ign_data, annot=True)
plt.xlabel("Genre")
Text(0.5, 80.7222222222222, 'Genre')

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candy_filepath = "../input/candy.csv"
candy_data = pd.read_csv(candy_filepath, index_col="id")
print("Shape: ", candy_data.shape)
candy_data.head()
Shape:  (83, 13)
competitorname chocolate fruity caramel peanutyalmondy nougat crispedricewafer hard bar pluribus sugarpercent pricepercent winpercent
id
0 100 Grand Yes No Yes No No Yes No Yes No 0.732 0.860 66.971725
1 3 Musketeers Yes No No No Yes No No Yes No 0.604 0.511 67.602936
2 Air Heads No Yes No No No No No No No 0.906 0.511 52.341465
3 Almond Joy Yes No No Yes No No No Yes No 0.465 0.767 50.347546
4 Baby Ruth Yes No Yes Yes Yes No No Yes No 0.604 0.767 56.914547 
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# 散点图
sns.scatterplot(x=candy_data["sugarpercent"], y=candy_data['winpercent'])
<Axes: xlabel='sugarpercent', ylabel='winpercent'>

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# 散点图
sns.scatterplot(x=candy_data['pricepercent'], y=candy_data['winpercent'], hue=candy_data['chocolate'])
<Axes: xlabel='pricepercent', ylabel='winpercent'>

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# 回归图
sns.regplot(x=candy_data['sugarpercent'], y=candy_data['winpercent'])
<Axes: xlabel='sugarpercent', ylabel='winpercent'>

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# 线性回归图
sns.lmplot(x='pricepercent', y='winpercent', hue='chocolate', data=candy_data)
<seaborn.axisgrid.FacetGrid at 0x163884c10>

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# 蜂群图
sns.swarmplot(x=candy_data['chocolate'], y=candy_data['winpercent'])
<Axes: xlabel='chocolate', ylabel='winpercent'>

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3. Distributions 分布

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cancer_filepath = '../input/cancer.csv'
cancer_data = pd.read_csv(cancer_filepath, index_col='Id')
print("Shape: ", cancer_data.shape)
cancer_data.head()
Shape:  (569, 31)
Diagnosis Radius (mean) Texture (mean) Perimeter (mean) Area (mean) Smoothness (mean) Compactness (mean) Concavity (mean) Concave points (mean) Symmetry (mean) ... Radius (worst) Texture (worst) Perimeter (worst) Area (worst) Smoothness (worst) Compactness (worst) Concavity (worst) Concave points (worst) Symmetry (worst) Fractal dimension (worst)
Id
8510426 B 13.540 14.36 87.46 566.3 0.09779 0.08129 0.06664 0.047810 0.1885 ... 15.110 19.26 99.70 711.2 0.14400 0.17730 0.23900 0.12880 0.2977 0.07259
8510653 B 13.080 15.71 85.63 520.0 0.10750 0.12700 0.04568 0.031100 0.1967 ... 14.500 20.49 96.09 630.5 0.13120 0.27760 0.18900 0.07283 0.3184 0.08183
8510824 B 9.504 12.44 60.34 273.9 0.10240 0.06492 0.02956 0.020760 0.1815 ... 10.230 15.66 65.13 314.9 0.13240 0.11480 0.08867 0.06227 0.2450 0.07773
854941 B 13.030 18.42 82.61 523.8 0.08983 0.03766 0.02562 0.029230 0.1467 ... 13.300 22.81 84.46 545.9 0.09701 0.04619 0.04833 0.05013 0.1987 0.06169
85713702 B 8.196 16.84 51.71 201.9 0.08600 0.05943 0.01588 0.005917 0.1769 ... 8.964 21.96 57.26 242.2 0.12970 0.13570 0.06880 0.02564 0.3105 0.07409

5 rows × 31 columns

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# Histogram 直方图
sns.histplot(data=cancer_data, x='Area (mean)', hue='Diagnosis')
<Axes: xlabel='Area (mean)', ylabel='Count'>

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# KDE 核密度估计图
sns.kdeplot(data=cancer_data, x='Radius (mean)', hue='Diagnosis', fill=True)
<Axes: xlabel='Radius (mean)', ylabel='Density'>

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# joint Plot 联合图
sns.jointplot(x=cancer_data['Radius (mean)'], y=cancer_data['Texture (mean)'], kind="kde")
<seaborn.axisgrid.JointGrid at 0x163ab5190>

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Custom Styles 自定义样式

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# (1)"darkgrid", (2)"whitegrid", (3)"dark", (4)"white", and (5)"ticks"
sns.set_style("darkgrid")

plt.figure(figsize=(12,6))
sns.lineplot(data=museum_data)
plt.title("Monthly Vistors")
Text(0.5, 1.0, 'Monthly Vistors')

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