Python数据可视化--Seaborn绘图总结2

ゝ一纸荒年。 2022-10-16 04:49 252阅读 0赞

Python 数据可视化–Seaborn绘图总结2

Seaborn其实是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易。同时它能高度兼容numpy与pandas数据结构以及scipy与statsmodels等统计模式

![在这里插入图片描述][20210514142700374.gif_pic_center]  
`reference`

1.  [seaborn官网][seaborn]
2.  [matplotlib实用绘图技巧总结][matplotlib]
3.  [ Seaborn教程][Seaborn]
4.  [10分钟python图表绘制][10_python]

### 文章目录 ###

*  类型
 *   *  boxplot
     *  violinplot
     *  heatmap
     *  jointplot
     *  barplot
     *  catplot
     *  pointplot
     *  scatterplot
     *  boxenplot
     *  Scatterplot
     *  PairGrid
     *  residplot
     *  swarmplot
     *  pairplot
     *  clustermap

推荐阅读：

[matplotlib实用绘图技巧总结][matplotlib]  
[Python 数据可视化–Seaborn绘图总结1][Python _Seaborn_1]  
[Python数据可视化–Seaborn绘图总结2][Python_Seaborn_2]

--------------------

[Tableau数据分析-Chapter01条形图、堆积图、直方图][Tableau_-Chapter01]  
[Tableau数据分析-Chapter02数据预处理、折线图、饼图][Tableau_-Chapter02]  
[Tableau数据分析-Chapter03基本表、树状图、气泡图、词云][Tableau_-Chapter03]  
[Tableau数据分析-Chapter04标靶图、甘特图、瀑布图][Tableau_-Chapter04]  
[Tableau数据分析-Chapter05数据集合并、符号地图][Tableau_-Chapter05]  
[Tableau数据分析-Chapter06填充地图、多维地图、混合地图][Tableau_-Chapter06]  
[Tableau数据分析-Chapter07多边形地图和背景地图][Tableau_-Chapter07]  
[Tableau数据分析-Chapter08数据分层、数据分组、数据集][Tableau_-Chapter08]  
[Tableau数据分析-Chapter09粒度、聚合与比率][Tableau_-Chapter09]  
[Tableau数据分析-Chapter10 人口金字塔、漏斗图、箱线图][Tableau_-Chapter10]  
[Tableau中国五城市六年PM2.5数据挖掘][Tableau_PM2.5]

# 类型 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_20_color_FFFFFF_t_70]

*  Relational plots 关系类图表
    
    1.  relplot() 关系类图表的接口，其实是下面两种图的集成，通过指定kind参数可以画出下面的两种图
    2.  scatterplot() 散点图
    3.  lineplot() 折线图
 *  Categorical plots 分类图表
    
    1.  catplot() 分类图表的接口，其实是下面八种图表的集成，通过指定kind参数可以画出下面的八种图
    2.  stripplot() 分类散点图
    3.  swarmplot() 能够显示分布密度的分类散点图
    4.  boxplot() 箱图
    5.  violinplot() 小提琴图
    6.  boxenplot() 增强箱图
    7.  pointplot() 点图
    8.  barplot() 条形图
    9.  countplot() 计数图
 *  Distribution plot 分布图
    
    1.  jointplot() 双变量关系图
    2.  pairplot() 变量关系组图
    3.  distplot() 直方图，质量估计图
    4.  kdeplot() 核函数密度估计图
    5.  rugplot() 将数组中的数据点绘制为轴上的数据
 *  Regression plots 回归图
    
    1.  lmplot() 回归模型图
    2.  regplot() 线性回归图
    3.  residplot() 线性回归残差图
 *  Matrix plots 矩阵图
    
    1.  heatmap() 热力图
    2.  clustermap() 聚集图

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple jieba

import seaborn as sns
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib inline
    import warnings
    warnings.filterwarnings("ignore")

有一套的参数可以控制绘图元素的比例。

首先，让我们通过`set()`重置默认的参数：

有五种seaborn的风格，它们分别是：`darkgrid`, `whitegrid`, `dark`, `white`, `ticks`。它们各自适合不同的应用和个人喜好。默认的主题是`darkgrid`。

sns.set(style="ticks")

## boxplot ##

箱形图（Box-plot）又称为盒须图、盒式图或箱线图，是一种用作显示一组数据分散情况资料的统计图。它能显示出一组数据的最大值、最小值、中位数及上下四分位数。

"""
    Grouped boxplots
    ================
    """
    sns.set(style="ticks", palette="pastel")
    
    # Load the example tips dataset
    tips = pd.read_csv("./seaborn-data-master/tips.csv")
    
    # Draw a nested boxplot to show bills by day and time
    sns.boxplot(x="day", y="total_bill",
                hue="smoker", palette=["m", "g"],
                data=tips)
    sns.despine(offset=10, trim=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center]

## violinplot ##

violinplot与boxplot扮演类似的角色，它显示了定量数据在一个（或多个）分类变量的多个层次上的分布，这些分布可以进行比较。不像箱形图中所有绘图组件都对应于实际数据点，小提琴绘图以基础分布的核密度估计为特征。

"""
    Violinplots with observations
    =============================
    
    """
    
    
    sns.set()
    
    # Create a random dataset across several variables
    rs = np.random.RandomState(0)
    n, p = 40, 8
    d = rs.normal(0, 2, (n, p))
    d += np.log(np.arange(1, p + 1)) * -5 + 10
    
    # Use cubehelix to get a custom sequential palette
    pal = sns.cubehelix_palette(p, rot=-.5, dark=.3)
    
    # Show each distribution with both violins and points
    sns.violinplot(data=d, palette=pal, inner="points")

<AxesSubplot:>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 1]

"""
    Grouped violinplots with split violins
    ======================================
    """
    sns.set(style="whitegrid", palette="pastel", color_codes=True)
    
    # Load the example tips dataset
    tips = pd.read_csv("./seaborn-data-master/tips.csv")
    
    # Draw a nested violinplot and split the violins for easier comparison
    sns.violinplot(x="day", y="total_bill", hue="smoker",
                   split=True, inner="quart",
                   palette={
        "Yes": "y", "No": "b"},
                   data=tips)
    sns.despine(left=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 2]

"""
    Violinplot from a wide-form dataset
    ===================================
    
    """
    
    sns.set(style="whitegrid")
    
    # Load the example dataset of brain network correlations
    df = pd.read_csv("./seaborn-data-master/brain_networks.csv", header=[0, 1, 2], index_col=0)
    
    # Pull out a specific subset of networks
    used_networks = [1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 16, 17]
    used_columns = (df.columns.get_level_values("network")
                              .astype(int)
                              .isin(used_networks))
    df = df.loc[:, used_columns]
    
    # Compute the correlation matrix and average over networks
    corr_df = df.corr().groupby(level="network").mean()
    corr_df.index = corr_df.index.astype(int)
    corr_df = corr_df.sort_index().T
    
    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=(11, 6))
    
    # Draw a violinplot with a narrower bandwidth than the default
    sns.violinplot(data=corr_df, palette="Set3", bw=.2, cut=1, linewidth=1)
    
    # Finalize the figure
    ax.set(ylim=(-.7, 1.05))
    sns.despine(left=True, bottom=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 3]

## heatmap ##

热力图  
利用热力图可以看数据表里多个特征两两的相似度。

"""
    Annotated heatmaps
    ==================
    
    """
    sns.set()
    
    # Load the example flights dataset and conver to long-form
    flights_long = pd.read_csv("./seaborn-data-master/flights.csv")
    flights = flights_long.pivot("month", "year", "passengers")
    
    # Draw a heatmap with the numeric values in each cell
    f, ax = plt.subplots(figsize=(9, 6))
    sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)

<AxesSubplot:xlabel='year', ylabel='month'>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 4]

"""
    Plotting a diagonal correlation matrix
    ======================================
    
    """
    from string import ascii_letters
    
    sns.set(style="white")
    
    # Generate a large random dataset
    rs = np.random.RandomState(33)
    d = pd.DataFrame(data=rs.normal(size=(100, 26)),
                     columns=list(ascii_letters[26:]))
    
    # Compute the correlation matrix
    corr = d.corr()
    
    # Generate a mask for the upper triangle
    mask = np.zeros_like(corr, dtype=np.bool)
    mask[np.triu_indices_from(mask)] = True
    
    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=(11, 9))
    
    # Generate a custom diverging colormap
    cmap = sns.diverging_palette(220, 10, as_cmap=True)
    
    # Draw the heatmap with the mask and correct aspect ratio
    sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
                square=True, linewidths=.5, cbar_kws={
        "shrink": .5})

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 5]

## jointplot ##

用于2个变量的画图

"""
    Joint kernel density estimate
    =============================
    """
    sns.set(style="white")
    
    # Generate a random correlated bivariate dataset
    rs = np.random.RandomState(5)
    mean = [0, 0]
    cov = [(1, .5), (.5, 1)]
    x1, x2 = rs.multivariate_normal(mean, cov, 500).T
    x1 = pd.Series(x1, name="$X_1$")
    x2 = pd.Series(x2, name="$X_2$")
    
    # Show the joint distribution using kernel density estimation
    g = sns.jointplot(x1, x2, kind="kde", height=7, space=0)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 6]

HexBin图

直方图的双变量类似物被称为“hexbin”图，因为它显示了落在六边形仓内的观测数。该图适用于较大的数据集。

"""
    Hexbin plot with marginal distributions
    =======================================
    """
    sns.set(style="ticks")
    
    rs = np.random.RandomState(11)
    x = rs.gamma(2, size=1000)
    y = -.5 * x + rs.normal(size=1000)
    
    sns.jointplot(x, y, kind="hex", color="#4CB391")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 7]

"""
    Linear regression with marginal distributions
    =============================================
    
    """
    
    sns.set(style="darkgrid")
    
    tips = pd.read_csv("./seaborn-data-master/tips.csv")
    g = sns.jointplot("total_bill", "tip", data=tips, kind="reg",
                      xlim=(0, 60), ylim=(0, 12), color="m", height=7)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 8]

## barplot ##

条形图表示数值变量与每个矩形高度的中心趋势的估计值，并使用误差线提供关于该估计值附近的不确定性的一些指示。

"""
    Horizontal bar plots
    ====================
    """
    sns.set(style="whitegrid")
    
    # Load the example car crash dataset
    crashes = pd.read_csv("./seaborn-data-master/car_crashes.csv").sort_values("total", ascending=False)
    
    # Initialize the matplotlib figure
    f, ax = plt.subplots(figsize=(6, 15))
    # Plot the total crashes
    sns.set_color_codes("pastel")
    sns.barplot(x="total", y="abbrev", data=crashes,
                label="Total", color="b")
    
    # Plot the crashes where alcohol was involved
    sns.set_color_codes("muted")
    sns.barplot(x="alcohol", y="abbrev", data=crashes,
                label="Alcohol-involved", color="b")
    
    # Add a legend and informative axis label
    ax.legend(ncol=2, loc="lower right", frameon=True)
    ax.set(xlim=(0, 24), ylabel="",
           xlabel="Automobile collisions per billion miles")
    sns.despine(left=True, bottom=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 9]

## catplot ##

分类图表的接口，通过指定kind参数可以画出下面的八种图

stripplot() 分类散点图

swarmplot() 能够显示分布密度的分类散点图

boxplot() 箱图

violinplot() 小提琴图

boxenplot() 增强箱图

pointplot() 点图

barplot() 条形图

countplot() 计数图

"""
    Grouped barplots
    ================
    """
    sns.set(style="whitegrid")
    
    # Load the example Titanic dataset
    titanic = pd.read_csv("./seaborn-data-master/titanic.csv")
    
    # Draw a nested barplot to show survival for class and sex
    g = sns.catplot(x="class", y="survived", hue="sex", data=titanic,
                    height=6, kind="bar", palette="muted")
    g.despine(left=True)
    g.set_ylabels("survival probability")

<seaborn.axisgrid.FacetGrid at 0x2c7be6f7e20>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 10]

"""
    Plotting a three-way ANOVA
    ==========================
    
    """
    
    sns.set(style="whitegrid")
    
    # Load the example exercise dataset
    df = pd.read_csv("./seaborn-data-master/exercise.csv")
    
    # Draw a pointplot to show pulse as a function of three categorical factors
    g = sns.catplot(x="time", y="pulse", hue="kind", col="diet",
                    capsize=.2, palette="YlGnBu_d", height=6, aspect=.75,
                    kind="point", data=df)
    g.despine(left=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 11]

## pointplot ##

点图

"""
    Conditional means with observations
    ===================================
    
    """
    sns.set(style="whitegrid")
    iris = pd.read_csv("./seaborn-data-master/iris.csv")
    
    # "Melt" the dataset to "long-form" or "tidy" representation
    iris = pd.melt(iris, "species", var_name="measurement")
    
    # Initialize the figure
    f, ax = plt.subplots()
    sns.despine(bottom=True, left=True)
    
    # Show each observation with a scatterplot
    sns.stripplot(x="value", y="measurement", hue="species",
                  data=iris, dodge=True, jitter=True,
                  alpha=.25, zorder=1)
    
    # Show the conditional means
    sns.pointplot(x="value", y="measurement", hue="species",
                  data=iris, dodge=.532, join=False, palette="dark",
                  markers="d", scale=.75, ci=None)
    
    # Improve the legend 
    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles[3:], labels[3:], title="species",
              handletextpad=0, columnspacing=1,
              loc="lower right", ncol=3, frameon=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 12]

## scatterplot ##

散点图

"""
    Scatterplot with categorical and numerical semantics
    ====================================================
    """
    sns.set(style="whitegrid")
    
    # Load the example iris dataset
    diamonds = pd.read_csv("./seaborn-data-master/diamonds.csv")
    
    # Draw a scatter plot while assigning point colors and sizes to different
    # variables in the dataset
    f, ax = plt.subplots(figsize=(6.5, 6.5))
    sns.despine(f, left=True, bottom=True)
    clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
    sns.scatterplot(x="carat", y="price",
                    hue="clarity", size="depth",
                    palette="ch:r=-.2,d=.3_r",
                    hue_order=clarity_ranking,
                    sizes=(1, 8), linewidth=0,
                    data=diamonds, ax=ax)

<AxesSubplot:xlabel='carat', ylabel='price'>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 13]

## boxenplot ##

增强箱图

"""
    Plotting large distributions
    ============================
    
    """
    sns.set(style="whitegrid")
    
    diamonds = pd.read_csv("./seaborn-data-master/diamonds.csv")
    clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
    
    sns.boxenplot(x="clarity", y="carat",
                  color="b", order=clarity_ranking,
                  scale="linear", data=diamonds)

<AxesSubplot:xlabel='clarity', ylabel='carat'>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 14]

## Scatterplot ##

散点图

"""
    Scatterplot with continuous hues and sizes
    ==========================================
    
    """
    
    sns.set()
    
    # Load the example iris dataset
    planets = pd.read_csv("./seaborn-data-master/planets.csv")
    
    cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
    ax = sns.scatterplot(x="distance", y="orbital_period",
                         hue="year", size="mass",
                         palette=cmap, sizes=(10, 200),
                         data=planets)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 15]

"""
    Scatterplot with marginal ticks
    ===============================
    """
    sns.set(style="white", color_codes=True)
    
    # Generate a random bivariate dataset
    rs = np.random.RandomState(9)
    mean = [0, 0]
    cov = [(1, 0), (0, 2)]
    x, y = rs.multivariate_normal(mean, cov, 100).T
    
    # Use JointGrid directly to draw a custom plot
    grid = sns.JointGrid(x, y, space=0, height=6, ratio=50)
    grid.plot_joint(plt.scatter, color="g")
    grid.plot_marginals(sns.rugplot, height=1, color="g")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 16]

## PairGrid ##

用于绘制数据集中成对关系的子图网格。

"""
    Paired density and scatterplot matrix
    =====================================
    
    """
    
    sns.set(style="white")
    
    df = pd.read_csv("./seaborn-data-master/iris.csv")
    
    g = sns.PairGrid(df, diag_sharey=False)
    g.map_lower(sns.kdeplot)
    g.map_upper(sns.scatterplot)
    g.map_diag(sns.kdeplot, lw=3)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 17]

"""
    Paired categorical plots
    ========================
    
    """
    sns.set(style="whitegrid")
    
    # Load the example Titanic dataset
    titanic = pd.read_csv("./seaborn-data-master/titanic.csv")
    
    # Set up a grid to plot survival probability against several variables
    g = sns.PairGrid(titanic, y_vars="survived",
                     x_vars=["class", "sex", "who", "alone"],
                     height=5, aspect=.5)
    
    # Draw a seaborn pointplot onto each Axes
    g.map(sns.pointplot, scale=1.3, errwidth=4, color="xkcd:plum")
    g.set(ylim=(0, 1))
    sns.despine(fig=g.fig, left=True)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 18]

## residplot ##

线性回归残差图

"""
    Plotting model residuals
    ========================
    
    """
    
    sns.set(style="whitegrid")
    
    # Make an example dataset with y ~ x
    rs = np.random.RandomState(7)
    x = rs.normal(2, 1, 75)
    y = 2 + 1.5 * x + rs.normal(0, 2, 75)
    
    # Plot the residuals after fitting a linear model
    sns.residplot(x, y, lowess=True, color="g")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 19]

"""
    Scatterplot with varying point sizes and hues
    ==============================================
    
    """
    sns.set(style="white")
    
    # Load the example mpg dataset
    mpg = pd.read_csv("./seaborn-data-master/mpg.csv")
    
    # Plot miles per gallon against horsepower with other semantics
    sns.relplot(x="horsepower", y="mpg", hue="origin", size="weight",
                sizes=(40, 400), alpha=.5, palette="muted",
                height=6, data=mpg)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 20]

## swarmplot ##

能够显示分布密度的分类散点图

"""
    Scatterplot with categorical variables
    ======================================
    
    """
    
    sns.set(style="whitegrid", palette="muted")
    
    # Load the example iris dataset
    iris = pd.read_csv("./seaborn-data-master/iris.csv")
    
    # "Melt" the dataset to "long-form" or "tidy" representation
    iris = pd.melt(iris, "species", var_name="measurement")
    
    # Draw a categorical scatterplot to show each observation
    sns.swarmplot(x="measurement", y="value", hue="species",
                  palette=["r", "c", "y"], data=iris)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 21]

## pairplot ##

变量关系组图

"""
    Scatterplot Matrix
    ==================
    
    """
    
    sns.set(style="ticks")
    
    df = pd.read_csv("./seaborn-data-master/iris.csv")
    sns.pairplot(df, hue="species")

<seaborn.axisgrid.PairGrid at 0x2c7c11763a0>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 22]

## clustermap ##

聚集图

"""
    Discovering structure in heatmap data
    =====================================
    
    """
    
    sns.set()
    
    # Load the brain networks example dataset
    df = pd.read_csv("./seaborn-data-master/brain_networks.csv", header=[0, 1, 2], index_col=0)
    
    # Select a subset of the networks
    used_networks = [1, 5, 6, 7, 8, 12, 13, 17]
    used_columns = (df.columns.get_level_values("network")
                              .astype(int)
                              .isin(used_networks))
    df = df.loc[:, used_columns]
    
    # Create a categorical palette to identify the networks
    network_pal = sns.husl_palette(8, s=.45)
    network_lut = dict(zip(map(str, used_networks), network_pal))
    
    # Convert the palette to vectors that will be drawn on the side of the matrix
    networks = df.columns.get_level_values("network")
    network_colors = pd.Series(networks, index=df.columns).map(network_lut)
    
    # Draw the full plot
    sns.clustermap(df.corr(), center=0, cmap="vlag",
                   row_colors=network_colors, col_colors=network_colors,
                   linewidths=.75, figsize=(13, 13))

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center 23]

![在这里插入图片描述][resize_m_lfit_w_962_pic_center]

推荐阅读：  
[Tableau数据分析-Chapter01条形图、堆积图、直方图][Tableau_-Chapter01]  
[Tableau数据分析-Chapter02数据预处理、折线图、饼图][Tableau_-Chapter02]  
[Tableau数据分析-Chapter03基本表、树状图、气泡图、词云][Tableau_-Chapter03]  
[Tableau数据分析-Chapter04标靶图、甘特图、瀑布图][Tableau_-Chapter04]  
[Tableau数据分析-Chapter05数据集合并、符号地图][Tableau_-Chapter05]  
[Tableau数据分析-Chapter06填充地图、多维地图、混合地图][Tableau_-Chapter06]  
[Tableau数据分析-Chapter07多边形地图和背景地图][Tableau_-Chapter07]  
[Tableau数据分析-Chapter08数据分层、数据分组、数据集][Tableau_-Chapter08]  
[Tableau数据分析-Chapter09粒度、聚合与比率][Tableau_-Chapter09]  
[Tableau数据分析-Chapter10 人口金字塔、漏斗图、箱线图][Tableau_-Chapter10]  
[Tableau中国五城市六年PM2.5数据挖掘][Tableau_PM2.5]

--------------------

> 到这里就结束了，如果对你有帮助你，欢迎点赞关注，你的点赞对我很重要

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70]

[20210514142700374.gif_pic_center]: /images/20221014/02f937a3919945a6ba3c7c5ed6074660.png
[seaborn]: https://seaborn.pydata.org/
[matplotlib]: https://beishan.blog.csdn.net/article/details/116401687
[Seaborn]: https://zhuanlan.zhihu.com/p/40303932
[10_python]: https://zhuanlan.zhihu.com/p/24464836
[Python _Seaborn_1]: https://beishan.blog.csdn.net/article/details/117380983
[Python_Seaborn_2]: https://beishan.blog.csdn.net/article/details/117305614
[Tableau_-Chapter01]: https://blog.csdn.net/qq_45176548/article/details/112756406
[Tableau_-Chapter02]: https://blog.csdn.net/qq_45176548/article/details/112757539
[Tableau_-Chapter03]: https://blog.csdn.net/qq_45176548/article/details/112758689
[Tableau_-Chapter04]: https://blog.csdn.net/qq_45176548/article/details/112777582
[Tableau_-Chapter05]: https://blog.csdn.net/qq_45176548/article/details/112788089
[Tableau_-Chapter06]: https://blog.csdn.net/qq_45176548/article/details/112797650
[Tableau_-Chapter07]: https://blog.csdn.net/qq_45176548/article/details/112849661
[Tableau_-Chapter08]: https://blog.csdn.net/qq_45176548/article/details/112850737
[Tableau_-Chapter09]: https://blog.csdn.net/qq_45176548/article/details/112873059
[Tableau_-Chapter10]: https://beishan.blog.csdn.net/article/details/116400020
[Tableau_PM2.5]: https://blog.csdn.net/qq_45176548/article/details/113619239
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