Python 数据可视化：Seaborn 库的使用

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Seaborn 简介

Seaborn 是一个基于 Python 的数据可视化库，它建立在 matplotlib 之上，并与 pandas 数据结构密切集成。Seaborn 的主要目的是通过使用更高级的界面来制作有吸引力的统计图形，从而使可视化变得更简单。

Seaborn 的主要特点包括：

1. 高级界面： Seaborn 提供了更高级的界面来绘制有吸引力的统计图形，例如散点图、条形图、箱线图等。这些图形可以通过几行代码快速生成，而不需要手动调整每个细节。
2. 内置主题： Seaborn 包括几个预定义的主题，可以用于改变图形的外观。这使得它更容易创建美观且专业的图形。
3. 与 pandas 集成： Seaborn 与 pandas 数据结构紧密集成，这意味着你可以直接在 pandas 的 DataFrame 上调用 Seaborn 的函数，从而方便地进行数据可视化。
4. 统计绘图： Seaborn 不仅提供了用于绘制基本图形的函数，还提供了用于绘制更复杂的统计图形的函数，例如 Pairplot（用于显示数据集中的成对关系）、Violinplot（用于显示分组的分布）等。
5. 颜色控制： Seaborn 允许用户通过参数控制图形的颜色，使其更易于创建颜色协调的图形。
6. 面板绘图： Seaborn 支持面板绘图，可以方便地在一个图形中显示多个不同的视图。

Seaborn 安装

Seaborn 可以通过 pip 安装：

pip install seaborn

Seaborn 使用

要使用 Seaborn，必须先导入 Seaborn 库。通常，Seaborn 会与 numpy、pandas 和 matplotlib 一起导入：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Seaborn 样例数据集

Seaborn 中提供了多种样例数据集，可以用于练习和测试。可以通过以下命令查看所有可用的数据集：

print(sns.get_dataset_names())

得到以下输出：

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'exercise', 'flights', 'fmri', 'gammas', 'geyser', 'iris', 'mpg', 'penguins', 'planets', 'taxis', 'tips', 'titanic']

Seaborn 中的数据集可以通过 load_dataset() 函数加载，该函数返回一个 pandas 的 DataFrame 对象。例如，要加载 Seaborn 中的 anscombe 数据集，可以使用以下命令：

df = sns.load_dataset("anscombe")
print(df.head())

得到以下输出：

  dataset     x     y
0       I  10.0  8.04
1       I   8.0  6.95
2       I  13.0  7.58
3       I   9.0  8.81
4       I  11.0  8.33

Seaborn 样式设置

Seaborn 中的样式可以通过 set_style() 函数设置。Seaborn 中有五种不同的样式，可以通过 set_style() 函数的 style 参数设置：

• darkgrid：默认样式，带有灰色网格。
• whitegrid：带有白色网格的样式。
• dark：不带网格的黑色背景样式。
• white：不带网格的白色背景样式。
• ticks：不带网格的样式，但带有刻度。

例如，要将样式设置为 whitegrid，可以使用以下命令：

sns.set_style("whitegrid")

Seaborn 颜色设置

Seaborn 中的颜色可以通过 set_palette() 函数设置。Seaborn 中有六种不同的调色板，可以通过 set_palette() 函数的 palette 参数设置：

• deep：默认调色板
• muted：较柔和的颜色
• pastel：柔和的颜色
• bright：明亮的颜色
• dark：暗色调
• colorblind：适合色盲的颜色

除此之外，还可以使用任何 matplotlib 调色板或者自定义调色盘。

要将颜色设置为 pastel，可以使用以下命令：

sns.set_palette("pastel")

Seaborn 绘图函数

Seaborn 支持绘制超多种不同的图表。下面列出了 Seaborn 支持的所有图表：

• 关系图表 relplot()
  • 散点图 scatterplot()
  • 折线图 lineplot()
• 分布图表 displot()
  • 直方图 histplot()
  • 核密度估计图 kdeplot()
  • 累积分布图 ecdfplot()
  • 地毯图 rugplot()
• 分类图表 catplot()
  • 分类散点图 stripplot()、swarmplot()
  • 分类分布图 boxplot()、violinplot()、boxenplot()
  • 分类估计图 pointplot()、barplot()、countplot()
• 回归图表
  • 回归模型图 lmplot()
    • 简单回归图 regplot()
    • 多图网格 FacetGrid
  • 回归残差图 residplot()
• 其他图表
  • 热力图 heatmap()
  • 聚类图 clustermap()
  • 成对关系图 pairplot()
    • 成对网格 PairGrid
  • 联合分布图 jointplot()
    • 联合网格 JointGrid

绘图示例

示例 1

sns.set_theme(style="ticks")

# Load the example dataset for Anscombe's quartet
df = sns.load_dataset("anscombe")

# Show the results of a linear regression within each dataset
sns.lmplot(
    data=df,
    x="x",
    y="y",
    col="dataset",
    hue="dataset",
    col_wrap=2,
    palette="muted",
    ci=None,
    height=4,
    scatter_kws={"s": 50, "alpha": 1},
)
plt.show()

示例 2

sns.set_theme(style="whitegrid")

# Load the example diamonds dataset
diamonds = sns.load_dataset("diamonds")

# 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,
)
plt.show()

示例 3

sns.set_theme(style="darkgrid")

# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")

# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal", hue="region", style="event", data=fmri)
plt.show()

示例 4

sns.set_theme(style="darkgrid")
df = sns.load_dataset("penguins")
sns.displot(
    df,
    x="flipper_length_mm",
    col="species",
    row="sex",
    binwidth=3,
    height=3,
    facet_kws=dict(margin_titles=True),
)
plt.show()

示例 5

sns.set_theme(style="ticks")

dots = sns.load_dataset("dots")

# Define the palette as a list to specify exact values
palette = sns.color_palette("rocket_r")

# Plot the lines on two facets
sns.relplot(
    data=dots,
    x="time",
    y="firing_rate",
    hue="coherence",
    size="choice",
    col="align",
    kind="line",
    size_order=["T1", "T2"],
    palette=palette,
    height=5,
    aspect=0.75,
    facet_kws=dict(sharex=False),
)
plt.show()

示例 6

sns.set_theme(style="whitegrid")

penguins = sns.load_dataset("penguins")

# Draw a nested barplot by species and sex
g = sns.catplot(
    data=penguins,
    kind="bar",
    x="species",
    y="body_mass_g",
    hue="sex",
    palette="dark",
    alpha=0.6,
    height=6,
)
g.despine(left=True)
g.set_axis_labels("", "Body mass (g)")
g.legend.set_title("")
plt.show()

示例 7

sns.set_theme(style="ticks", palette="pastel")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# 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)
plt.show()

示例 8

sns.set_theme(style="whitegrid")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(
    data=tips,
    x="day",
    y="total_bill",
    hue="smoker",
    split=True,
    inner="quart",
    linewidth=1,
    palette={"Yes": "b", "No": ".85"},
)
sns.despine(left=True)
plt.show()

示例 9

sns.set_theme(style="whitegrid")

# Load the brain networks dataset, select subset, and collapse the multi-index
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)

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]

df.columns = df.columns.map("-".join)

# Compute a correlation matrix and convert to long-form
corr_mat = df.corr().stack().reset_index(name="correlation")

# Draw each cell as a scatter point with varying size and color
g = sns.relplot(
    data=corr_mat,
    x="level_0",
    y="level_1",
    hue="correlation",
    size="correlation",
    palette="vlag",
    hue_norm=(-1, 1),
    edgecolor=".7",
    height=10,
    sizes=(50, 250),
    size_norm=(-0.2, 0.8),
)

# Tweak the figure to finalize
g.set(xlabel="", ylabel="", aspect="equal")
g.despine(left=True, bottom=True)
g.ax.margins(0.02)
for label in g.ax.get_xticklabels():
    label.set_rotation(90)
for artist in g.legend.legendHandles:
    artist.set_edgecolor(".7")
plt.show()

示例 10

sns.set_theme(style="ticks")

rs = np.random.RandomState(11)
x = rs.gamma(2, size=1000)
y = -0.5 * x + rs.normal(size=1000)

sns.jointplot(x=x, y=y, kind="hex", color="#4CB391")
plt.show()

示例 11

sns.set_theme(style="ticks")

diamonds = sns.load_dataset("diamonds")

f, ax = plt.subplots(figsize=(7, 5))
sns.despine(f)

sns.histplot(
    diamonds,
    x="price",
    hue="cut",
    multiple="stack",
    palette="light:m_r",
    edgecolor=".3",
    linewidth=0.5,
    log_scale=True,
)
ax.set_xticks([500, 1000, 2000, 5000, 10000])
plt.show()

示例 12

sns.set_theme(style="ticks")

# Initialize the figure with a logarithmic x axis
f, ax = plt.subplots(figsize=(7, 6))
ax.set_xscale("log")

# Load the example planets dataset
planets = sns.load_dataset("planets")

# Plot the orbital period with horizontal boxes
sns.boxplot(
    x="distance", y="method", data=planets, whis=[0, 100], width=0.6, palette="vlag"
)

# Add in points to show each observation
sns.stripplot(x="distance", y="method", data=planets, size=4, color=".3", linewidth=0)

# Tweak the visual presentation
ax.xaxis.grid(True)
ax.set(ylabel="")
sns.despine(trim=True, left=True)
plt.show()

示例 13

sns.set_theme(style="whitegrid")
iris = sns.load_dataset("iris")

# "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(
    data=iris,
    x="value",
    y="measurement",
    hue="species",
    dodge=True,
    alpha=0.25,
    zorder=1,
)

# Show the conditional means, aligning each pointplot in the
# center of the strips by adjusting the width allotted to each
# category (.8 by default) by the number of hue levels
sns.pointplot(
    data=iris,
    x="value",
    y="measurement",
    hue="species",
    join=False,
    dodge=0.8 - 0.8 / 3,
    palette="dark",
    markers="d",
    scale=0.75,
    errorbar=None,
)

# Improve the legend
sns.move_legend(
    ax, loc="lower right", ncol=3, frameon=True, columnspacing=1, handletextpad=0
)
plt.show()

示例 14

sns.set_theme(style="ticks")

# Load the planets dataset and initialize the figure
planets = sns.load_dataset("planets")
g = sns.JointGrid(data=planets, x="year", y="distance", marginal_ticks=True)

# Set a log scaling on the y axis
g.ax_joint.set(yscale="log")

# Create an inset legend for the histogram colorbar
cax = g.figure.add_axes([0.15, 0.55, 0.02, 0.2])

# Add the joint and marginal histogram plots
g.plot_joint(
    sns.histplot,
    discrete=(True, False),
    cmap="light:#03012d",
    pmax=0.8,
    cbar=True,
    cbar_ax=cax,
)
g.plot_marginals(sns.histplot, element="step", color="#03012d")
plt.show()

示例 15

sns.set_theme(style="ticks")

# Load the penguins dataset
penguins = sns.load_dataset("penguins")

# Show the joint distribution using kernel density estimation
g = sns.jointplot(
    data=penguins,
    x="bill_length_mm",
    y="bill_depth_mm",
    hue="species",
    kind="kde",
)
plt.show()

示例 16

sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# Create the data
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m

# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, rot=-0.25, light=0.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=0.5, palette=pal)

# Draw the densities in a few steps
g.map(sns.kdeplot, "x", bw_adjust=0.5, clip_on=False, fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=0.5)

# passing color=None to refline() uses the hue mapping
g.refline(y=0, linewidth=2, linestyle="-", color=None, clip_on=False)


# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(
        0,
        0.2,
        label,
        fontweight="bold",
        color=color,
        ha="left",
        va="center",
        transform=ax.transAxes,
    )


g.map(label, "x")

# Set the subplots to overlap
g.figure.subplots_adjust(hspace=-0.25)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[], ylabel="")
g.despine(bottom=True, left=True)
plt.show()

示例 17

sns.set_theme(style="whitegrid")

diamonds = sns.load_dataset("diamonds")
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,
)
plt.show()

示例 18

sns.set_theme(style="dark")

# Simulate data from a bivariate Gaussian
n = 10000
mean = [0, 0]
cov = [(2, 0.4), (0.4, 0.2)]
rng = np.random.RandomState(0)
x, y = rng.multivariate_normal(mean, cov, n).T

# Draw a combo histogram and scatterplot with density contours
f, ax = plt.subplots(figsize=(6, 6))
sns.scatterplot(x=x, y=y, s=5, color=".15")
sns.histplot(x=x, y=y, bins=50, pthresh=0.1, cmap="mako")
sns.kdeplot(x=x, y=y, levels=5, color="w", linewidths=1)
plt.show()

示例 19

sns.set_theme(style="darkgrid")

# Load the example Titanic dataset
df = sns.load_dataset("titanic")

# Make a custom palette with gendered colors
pal = dict(male="#6495ED", female="#F08080")

# Show the survival probability as a function of age and sex
g = sns.lmplot(
    x="age",
    y="survived",
    col="sex",
    hue="sex",
    data=df,
    palette=pal,
    y_jitter=0.02,
    logistic=True,
    truncate=False,
)
g.set(xlim=(0, 80), ylim=(-0.05, 1.05))
plt.show()

示例 20

sns.set_theme(style="ticks")

# Create a dataset with many short random walks
rs = np.random.RandomState(4)
pos = rs.randint(-1, 2, (20, 5)).cumsum(axis=1)
pos -= pos[:, 0, np.newaxis]
step = np.tile(range(5), 20)
walk = np.repeat(range(20), 5)
df = pd.DataFrame(np.c_[pos.flat, step, walk], columns=["position", "step", "walk"])

# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(
    df, col="walk", hue="walk", palette="tab20c", col_wrap=4, height=1.5
)

# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")

# Draw a line plot to show the trajectory of each random walk
grid.map(plt.plot, "step", "position", marker="o")

# Adjust the tick positions and labels
grid.set(xticks=np.arange(5), yticks=[-3, 3], xlim=(-0.5, 4.5), ylim=(-3.5, 3.5))

# Adjust the arrangement of the plots
grid.fig.tight_layout(w_pad=1)
plt.show()

示例 21

from string import ascii_letters

sns.set_theme(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.triu(np.ones_like(corr, dtype=bool))

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(
    corr,
    mask=mask,
    cmap=cmap,
    vmax=0.3,
    center=0,
    square=True,
    linewidths=0.5,
    cbar_kws={"shrink": 0.5},
)
plt.show()

示例 22

sns.set_theme(style="white", color_codes=True)
mpg = sns.load_dataset("mpg")

# Use JointGrid directly to draw a custom plot
g = sns.JointGrid(data=mpg, x="mpg", y="acceleration", space=0, ratio=17)
g.plot_joint(
    sns.scatterplot,
    size=mpg["horsepower"],
    sizes=(30, 120),
    color="g",
    alpha=0.6,
    legend=False,
)
g.plot_marginals(sns.rugplot, height=1, color="g", alpha=0.6)
plt.show()

示例 23

sns.set_theme(style="darkgrid")
iris = sns.load_dataset("iris")

# Set up the figure
f, ax = plt.subplots(figsize=(8, 8))
ax.set_aspect("equal")

# Draw a contour plot to represent each bivariate density
sns.kdeplot(
    data=iris.query("species != 'versicolor'"),
    x="sepal_width",
    y="sepal_length",
    hue="species",
    thresh=0.1,
)
plt.show()

示例 24

sns.set_theme(style="whitegrid")

# Load the diamonds dataset
diamonds = sns.load_dataset("diamonds")

# Plot the distribution of clarity ratings, conditional on carat
sns.displot(
    data=diamonds,
    x="carat",
    hue="cut",
    kind="kde",
    height=6,
    multiple="fill",
    clip=(0, None),
    palette="ch:rot=-.25,hue=1,light=.75",
)
plt.show()

示例 25

sns.set_theme(style="ticks")
mpg = sns.load_dataset("mpg")

colors = (250, 70, 50), (350, 70, 50)
cmap = sns.blend_palette(colors, input="husl", as_cmap=True)
sns.displot(
    mpg,
    x="displacement",
    col="origin",
    hue="model_year",
    kind="ecdf",
    aspect=0.75,
    linewidth=2,
    palette=cmap,
)
plt.show()

示例 26

sns.set_theme()

# Load the penguins dataset
penguins = sns.load_dataset("penguins")

# Plot sepal width as a function of sepal_length across days
g = sns.lmplot(
    data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species", height=5
)

# Use more informative axis labels than are provided by default
g.set_axis_labels("Snoot length (mm)", "Snoot depth (mm)")
plt.show()

示例 27

sns.set_theme(style="white")

df = sns.load_dataset("penguins")

g = sns.PairGrid(df, diag_sharey=False)
g.map_upper(sns.scatterplot, s=15)
g.map_lower(sns.kdeplot)
g.map_diag(sns.kdeplot, lw=2)
plt.show()

示例 28

sns.set_theme(style="whitegrid")

# Load the example Titanic dataset
titanic = sns.load_dataset("titanic")

# 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=0.5,
)

# 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)
plt.show()

示例 29

sns.set_theme(style="whitegrid")

# Load the dataset
crashes = sns.load_dataset("car_crashes")

# Make the PairGrid
g = sns.PairGrid(
    crashes.sort_values("total", ascending=False),
    x_vars=crashes.columns[:-3],
    y_vars=["abbrev"],
    height=10,
    aspect=0.25,
)

# Draw a dot plot using the stripplot function
g.map(
    sns.stripplot,
    size=10,
    orient="h",
    jitter=False,
    palette="flare_r",
    linewidth=1,
    edgecolor="w",
)

# Use the same x axis limits on all columns and add better labels
g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")

# Use semantically meaningful titles for the columns
titles = [
    "Total crashes",
    "Speeding crashes",
    "Alcohol crashes",
    "Not distracted crashes",
    "No previous crashes",
]

for ax, title in zip(g.axes.flat, titles):
    # Set a different title for each axes
    ax.set(title=title)

    # Make the grid horizontal instead of vertical
    ax.xaxis.grid(False)
    ax.yaxis.grid(True)

sns.despine(left=True, bottom=True)
plt.show()

示例 30

sns.set_theme(style="white", context="talk")
rs = np.random.RandomState(8)

# Set up the matplotlib figure
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)

# Generate some sequential data
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1)
ax1.axhline(0, color="k", clip_on=False)
ax1.set_ylabel("Sequential")

# Center the data to make it diverging
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Diverging")

# Randomly reorder the data to make it qualitative
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitative")

# Finalize the plot
sns.despine(bottom=True)
plt.setp(f.axes, yticks=[])
plt.tight_layout(h_pad=2)
plt.show()

示例 31

sns.set_theme(style="white")
rs = np.random.RandomState(50)

# Set up the matplotlib figure
f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)

# Rotate the starting point around the cubehelix hue circle
for ax, s in zip(axes.flat, np.linspace(0, 3, 10)):
    # Create a cubehelix colormap to use with kdeplot
    cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)

    # Generate and plot a random bivariate dataset
    x, y = rs.normal(size=(2, 50))
    sns.kdeplot(
        x=x,
        y=y,
        cmap=cmap,
        fill=True,
        clip=(-5, 5),
        cut=10,
        thresh=0,
        levels=15,
        ax=ax,
    )
    ax.set_axis_off()

ax.set(xlim=(-3.5, 3.5), ylim=(-3.5, 3.5))
f.subplots_adjust(0, 0, 1, 1, 0.08, 0.08)
plt.show()

示例 32

sns.set_theme(style="whitegrid")

# Initialize the matplotlib figure
f, ax = plt.subplots(figsize=(6, 15))

# Load the example car crash dataset
crashes = sns.load_dataset("car_crashes").sort_values("total", ascending=False)

# 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)
plt.show()

示例 33

sns.set_theme(style="whitegrid")

# Load the example exercise dataset
exercise = sns.load_dataset("exercise")

# Draw a pointplot to show pulse as a function of three categorical factors
g = sns.catplot(
    data=exercise,
    x="time",
    y="pulse",
    hue="kind",
    col="diet",
    capsize=0.2,
    palette="YlGnBu_d",
    errorbar="se",
    kind="point",
    height=6,
    aspect=0.75,
)
g.despine(left=True)
plt.show()

示例 34

sns.set_theme()

# Generate an example radial datast
r = np.linspace(0, 10, num=100)
df = pd.DataFrame({"r": r, "slow": r, "medium": 2 * r, "fast": 4 * r})

# Convert the dataframe to long-form or "tidy" format
df = pd.melt(df, id_vars=["r"], var_name="speed", value_name="theta")

# Set up a grid of axes with a polar projection
g = sns.FacetGrid(
    df,
    col="speed",
    hue="speed",
    subplot_kws=dict(projection="polar"),
    height=4.5,
    sharex=False,
    sharey=False,
    despine=False,
)

# Draw a scatterplot onto each axes in the grid
g.map(sns.scatterplot, "theta", "r")
plt.show()

示例 35

sns.set_theme(style="darkgrid")

tips = sns.load_dataset("tips")
g = sns.jointplot(
    x="total_bill",
    y="tip",
    data=tips,
    kind="reg",
    truncate=False,
    xlim=(0, 60),
    ylim=(0, 12),
    color="m",
    height=7,
)
plt.show()

示例 36

sns.set_theme(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=x, y=y, lowess=True, color="g")
plt.show()

示例 37

sns.set_theme(style="white")

# Load the example mpg dataset
mpg = sns.load_dataset("mpg")

# Plot miles per gallon against horsepower with other semantics
sns.relplot(
    x="horsepower",
    y="mpg",
    hue="origin",
    size="weight",
    sizes=(40, 400),
    alpha=0.5,
    palette="muted",
    height=6,
    data=mpg,
)
plt.show()

示例 38

sns.set_theme(style="whitegrid", palette="muted")

# Load the penguins dataset
df = sns.load_dataset("penguins")

# Draw a categorical scatterplot to show each observation
ax = sns.swarmplot(data=df, x="body_mass_g", y="sex", hue="species")
ax.set(ylabel="")
plt.show()

示例 39

sns.set_theme(style="ticks")

df = sns.load_dataset("penguins")
sns.pairplot(df, hue="species")
plt.show()

示例 40

sns.set_theme(style="whitegrid")

# Load the example planets dataset
planets = sns.load_dataset("planets")

cmap = sns.cubehelix_palette(rot=-0.2, as_cmap=True)
g = sns.relplot(
    data=planets,
    x="distance",
    y="orbital_period",
    hue="year",
    size="mass",
    palette=cmap,
    sizes=(10, 200),
)
g.set(xscale="log", yscale="log")
g.ax.xaxis.grid(True, "minor", linewidth=0.25)
g.ax.yaxis.grid(True, "minor", linewidth=0.25)
g.despine(left=True, bottom=True)
plt.show()

示例 41

sns.set_theme()

# Create a random dataset across several variables
rs = np.random.default_rng(0)
n, p = 40, 8
d = rs.normal(0, 2, (n, p))
d += np.log(np.arange(1, p + 1)) * -5 + 10

# Show each distribution with both violins and points
sns.violinplot(data=d, palette="light:g", inner="points", orient="h")
plt.show()

示例 42

sns.set_theme(style="white")

df = sns.load_dataset("penguins")

g = sns.JointGrid(data=df, x="body_mass_g", y="bill_depth_mm", space=0)
g.plot_joint(
    sns.kdeplot,
    fill=True,
    clip=((2200, 6800), (10, 25)),
    thresh=0,
    levels=100,
    cmap="rocket",
)
g.plot_marginals(sns.histplot, color="#03051A", alpha=1, bins=25)
plt.show()

示例 43

sns.set_theme()

# Load the example flights dataset and convert to long-form
flights_long = sns.load_dataset("flights")
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=0.5, ax=ax)
plt.show()

示例 44

sns.set_theme()

mpg = sns.load_dataset("mpg")
sns.catplot(
    data=mpg, x="cylinders", y="acceleration", hue="weight", zorder=1, legend=False
)
sns.regplot(
    data=mpg,
    x="cylinders",
    y="acceleration",
    scatter=False,
    truncate=False,
    order=2,
    color=".2",
)
plt.show()

示例 45

sns.set_theme()

# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", 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=0.45)
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
g = sns.clustermap(
    df.corr(),
    center=0,
    cmap="vlag",
    row_colors=network_colors,
    col_colors=network_colors,
    dendrogram_ratio=(0.1, 0.2),
    cbar_pos=(0.02, 0.32, 0.03, 0.2),
    linewidths=0.75,
    figsize=(12, 13),
)

g.ax_row_dendrogram.remove()
plt.show()

示例 46

sns.set_theme(style="dark")

diamonds = sns.load_dataset("diamonds")
sns.displot(
    data=diamonds,
    x="price",
    y="color",
    col="clarity",
    log_scale=(True, False),
    col_wrap=4,
    height=4,
    aspect=0.7,
)
plt.show()

示例 47

sns.set_theme(style="dark")
flights = sns.load_dataset("flights")

# Plot each year's time series in its own facet
g = sns.relplot(
    data=flights,
    x="month",
    y="passengers",
    col="year",
    hue="year",
    kind="line",
    palette="crest",
    linewidth=4,
    zorder=5,
    col_wrap=3,
    height=2,
    aspect=1.5,
    legend=False,
)

# Iterate over each subplot to customize further
for year, ax in g.axes_dict.items():
    # Add the title as an annotation within the plot
    ax.text(0.8, 0.85, year, transform=ax.transAxes, fontweight="bold")

    # Plot every year's time series in the background
    sns.lineplot(
        data=flights,
        x="month",
        y="passengers",
        units="year",
        estimator=None,
        color=".7",
        linewidth=1,
        ax=ax,
    )

# Reduce the frequency of the x axis ticks
ax.set_xticks(ax.get_xticks()[::2])

# Tweak the supporting aspects of the plot
g.set_titles("")
g.set_axis_labels("", "Passengers")
g.tight_layout()
plt.show()

示例 48

sns.set_theme(style="whitegrid")

rs = np.random.RandomState(365)
values = rs.randn(365, 4).cumsum(axis=0)
dates = pd.date_range("1 1 2016", periods=365, freq="D")
data = pd.DataFrame(values, dates, columns=["A", "B", "C", "D"])
data = data.rolling(7).mean()

sns.lineplot(data=data, palette="tab10", linewidth=2.5)
plt.show()

示例 49

sns.set_theme(style="whitegrid")

# Load the example dataset of brain network correlations
df = sns.load_dataset("brain_networks", 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=0.2, cut=1, linewidth=1)

# Finalize the figure
ax.set(ylim=(-0.7, 1.05))
sns.despine(left=True, bottom=True)
plt.show()

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