绘制网格图表¶

目录¶

Axis grids对象
使用FacetGrid对数据选择子集
- 生成FacetGrid对象并应用map方法
- FacetGrid和map的参数
- 分面的大小
- 分面的顺序
- hue层次的颜色
- hue层次的其他元素
- col_wrap
- FacetGrid.set()
- Figure和Axes对象
将自编绘图函数映射至grid
- 自编绘图函数示例
- 修改Seaborn绘图函数
使用PairGrid对象和pairplot()函数绘制成对关系图
- PairGrid对象
  - 设置对角线图表的种类
  - hue层次的颜色
  - 选择绘制的变量
  - 对角线上方和下方绘制不同图表
  - x_vars参数、y_vars参数设置不同的变量
  - 设置图表的其他元素
- sns.pairplot()方法
  - 设置图表的其他元素

In [1]:

import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
sns.set(style="ticks")
np.random.seed(sum(map(ord, "axis_grids")))
%matplotlib inline

In [2]:

tips = sns.load_dataset("tips")
titanic = sns.load_dataset("titanic")
iris = sns.load_dataset("iris")

Axis grids对象¶

FacetGrid

Subplot grid for plotting conditional relationships.

PairGrid

Subplot grid for plotting pairwise relationships in a dataset.

JointGrid

Grid for drawing a bivariate plot with marginal univariate plots.

FacetGrid对象 + map方法、sns.factorplot() + kind参数

第5章“绘制分面类别图”部分

PairGrid对象 + map方法、sns.pairplot() + kind参数

第4章“多个变量相对于一个变量的回归线”部分

JointGrid对象 + map方法、sns.jointplot() + kind参数

第3章“JointGrid对象”部分

绘制分面图，数据必须以Pandas DataFrame的"tidy data"形式存储，即每一列代表一个变量，每一行代表一个观测
lmplot()、factorplot()生成FacetGrid对象，pairplot()生成PairGrid对象，jointplot()生成JointGrid对象

使用FacetGrid对数据选择子集¶

row、col、hue参数

生成FacetGrid对象并应用map方法¶

In [3]:

g = sns.FacetGrid(tips, col="time") # 生成FacetGrid对象，需要传入data、row、col、hue参数，等同于生成matplotlib的figure和axes，不会绘制任何图表

In [4]:

g = sns.FacetGrid(tips, col="time")
g.map(plt.hist, "tip") # 传入plotting function以及需要绘制的变量名（字符串形式）

Out[4]:

<seaborn.axisgrid.FacetGrid at 0x9bdffd0>

FacetGrid和map的参数¶

In [5]:

g = sns.FacetGrid(tips, col="sex", hue="smoker")
g.map(plt.scatter, "total_bill", "tip", alpha=.7) # map中也可以传入其他plotting function的参数，如alpha
g.add_legend() # 添加图例

Out[5]:

<seaborn.axisgrid.FacetGrid at 0x9fe2438>

In [6]:

g = sns.FacetGrid(tips, row="smoker", col="time", margin_titles=True) # FacetGrid中还有很多参数可选，如margin_titles参数
g.map(sns.regplot, "size", "total_bill", color=".3", fit_reg=False, x_jitter=.1)

Out[6]:

<seaborn.axisgrid.FacetGrid at 0xafa5898>

分面的大小¶

In [7]:

g = sns.FacetGrid(tips, col="day", size=4, aspect=.5) # size、aspect参数控制每个分面的大小
g.map(sns.barplot, "sex", "total_bill")

Out[7]:

<seaborn.axisgrid.FacetGrid at 0xb34d9b0>

In [8]:

titanic = titanic.assign(deck=titanic.deck.astype(object)).sort_values("deck")
# gridspec_kws参数，Dictionary of keyword arguments passed to matplotlib’s gridspec module (via plt.subplots)
g = sns.FacetGrid(titanic, col="class", sharex=False,
                  gridspec_kws={"width_ratios": [5, 3, 3]}) # 当每个分面中图表的内容多少有区别时，最好把每个分面设置不同的大小
g.map(sns.boxplot, "deck", "age")

Out[8]:

<seaborn.axisgrid.FacetGrid at 0xb93c550>

分面的顺序¶

In [9]:

ordered_days = tips.day.value_counts().index
g = sns.FacetGrid(tips, row="day", row_order=ordered_days,
                  size=1.7, aspect=4,) # *_order设置分面的顺序，如没有设置该参数，则按照Categorical datatype的order，或者变量在DataFrame中出现的顺序
g.map(sns.distplot, "total_bill", hist=False, rug=True)

Out[9]:

<seaborn.axisgrid.FacetGrid at 0xbfcefd0>

hue层次的颜色¶

palette参数

In [10]:

pal = dict(Lunch="seagreen", Dinner="gray") # 通过字典来设置颜色
g = sns.FacetGrid(tips, hue="time", palette=pal, size=5) # palette可以接受任何能够被color_palette()接受的值，也可以接受一个hue:"col"的字典
g.map(plt.scatter, "total_bill", "tip", s=50, alpha=.7, linewidth=.5, edgecolor="white")
g.add_legend()

Out[10]:

<seaborn.axisgrid.FacetGrid at 0xd217d30>

hue层次的其他元素¶

hue_kws参数

In [11]:

# hue_kws参数，接受"the names of plotting function keyword arguments": [lists of keyword values]形式的字典，如本例中的marker设置散点的形状
g = sns.FacetGrid(tips, hue="sex", palette="Set1", size=5, hue_kws={"marker": ["^", "v"]})
g.map(plt.scatter, "total_bill", "tip", s=100, linewidth=.5, edgecolor="white")
g.add_legend()

Out[11]:

<seaborn.axisgrid.FacetGrid at 0xd4af978>

col_wrap¶

col_wrap参数，此时row参数被禁用

In [12]:

attend = sns.load_dataset("attention").query("subject <= 12")
g = sns.FacetGrid(attend, col="subject", col_wrap=4, size=2, ylim=(0, 10))
g.map(sns.pointplot, "solutions", "score", color=".3", ci=None)

Out[12]:

<seaborn.axisgrid.FacetGrid at 0xd6c04e0>

FacetGrid.set()¶

还包含了FacetGrid.set_axis_labels()等一系列更为特定的方法

In [13]:

with sns.axes_style("white"):
    g = sns.FacetGrid(tips, row="sex", col="smoker", margin_titles=True, size=2.5)
g.map(plt.scatter, "total_bill", "tip", color="#334488", edgecolor="white", lw=.5)
g.set_axis_labels("Total bill (US Dollars)", "Tip") # 设置坐标轴标签
g.set(xticks=[10, 30, 50], yticks=[2, 6, 10]) # 设置坐标轴刻度
g.fig.subplots_adjust(wspace=.02, hspace=.02) # matplotlib中的方法

Figure和Axes对象¶

For even more customization, you can work directly with the underling matplotlib Figure and Axes objects, which are stored as member attributes at fig and axes (a two-dimensional array), respectively. When making a figure without row or column faceting, you can also use the ax attribute to directly access the single axes.

In [14]:

g = sns.FacetGrid(tips, col="smoker", margin_titles=True, size=4)
g.map(plt.scatter, "total_bill", "tip", color="#338844", edgecolor="white", s=50, lw=1)
for ax in g.axes.flat: # 获取ax对象
    ax.plot((0, 50), (0, .2 * 50), c=".2", ls="--") # 绘制45度线
g.set(xlim=(0, 60), ylim=(0, 14))

Out[14]:

<seaborn.axisgrid.FacetGrid at 0xac08ba8>

将自编绘图函数映射至grid¶

自编函数的要求

It must plot onto the "currently active" matplotlib Axes. This will be true of functions in the matplotlib.pyplot namespace, and you can call plt.gca to get a reference to the current Axes if you want to work directly with its methods.
It must accept the data that it plots in positional arguments. Internally, FacetGrid will pass a Series of data for each of the named positional arguments passed to FacetGrid.map.
It must be able to accept color and label keyword arguments, and, ideally, it will do something useful with them. In most cases, it's easiest to catch a generic dictionary of ****kwargs** and pass it along to the underlying plotting function.

自编绘图函数示例¶

In [15]:

def quantile_plot(x, **kwargs):
    qntls, xr = stats.probplot(x, fit=False)
    plt.scatter(xr, qntls, **kwargs)
    
g = sns.FacetGrid(tips, col="sex", size=4)
g.map(quantile_plot, "total_bill")

Out[15]:

<seaborn.axisgrid.FacetGrid at 0xe543860>

In [16]:

def qqplot(x, y, **kwargs):
    _, xr = stats.probplot(x, fit=False)
    _, yr = stats.probplot(y, fit=False)
    plt.scatter(xr, yr, **kwargs)
    
g = sns.FacetGrid(tips, col="smoker", size=4)
g.map(qqplot, "total_bill", "tip")

Out[16]:

<seaborn.axisgrid.FacetGrid at 0xf51a390>

In [17]:

g = sns.FacetGrid(tips, hue="time", col="sex", size=4)
g.map(qqplot, "total_bill", "tip")
g.add_legend()

Out[17]:

<seaborn.axisgrid.FacetGrid at 0xf184d30>

In [18]:

g = sns.FacetGrid(tips, hue="time", col="sex", size=4,
                  hue_kws={"marker": ["s", "D"]})
g.map(qqplot, "total_bill", "tip", s=40, edgecolor="w")
g.add_legend()

Out[18]:

<seaborn.axisgrid.FacetGrid at 0xf5c9160>

修改Seaborn绘图函数¶

Sometimes, though, you'll want to map a function that doesn't work the way you expect with the color and label keyword arguments. In this case, you'll want to explictly catch them and handle them in the logic of your custom function. For example, this approach will allow use to map plt.hexbin, which otherwise does not play well with the FacetGrid API

In [19]:

def hexbin(x, y, color, **kwargs):
    cmap = sns.light_palette(color, as_cmap=True)
    plt.hexbin(x, y, gridsize=15, cmap=cmap, **kwargs)

with sns.axes_style("dark"):
    g = sns.FacetGrid(tips, hue="time", col="time", size=4)
g.map(hexbin, "total_bill", "tip", extent=[0, 50, 0, 10])

Out[19]:

<seaborn.axisgrid.FacetGrid at 0xd3c5860>

使用PairGrid对象和pairplot()函数绘制成对关系图¶

在PairGrid中，每一行、列都赋予不同的变量，因而可以展示变量之间的两两关系

PairGrid对象¶

In [20]:

g = sns.PairGrid(iris)
g.map(plt.scatter) # 绘制成对散点图，对角线上默认绘制散点图

Out[20]:

<seaborn.axisgrid.PairGrid at 0xf51aa58>

设置对角线图表的种类¶

map_diag()、map_offdiag()方法

In [21]:

g = sns.PairGrid(iris)
g.map_diag(plt.hist) # 可以在对角线上绘制直方图或核密度图，但是坐标轴与直方图的count、核密度图的density不对应
g.map_offdiag(plt.scatter) # 在非对角线上绘制成对散点图

Out[21]:

<seaborn.axisgrid.PairGrid at 0x11c22860>

hue层次的颜色¶

In [22]:

g = sns.PairGrid(iris, hue="species") # 设置hue参数，对不同类别的观测点绘制不同颜色
g.map_diag(plt.hist)
g.map_offdiag(plt.scatter)
g.add_legend()

Out[22]:

<seaborn.axisgrid.PairGrid at 0x144af780>

选择绘制的变量¶

vars参数

In [23]:

g = sns.PairGrid(iris, vars=["sepal_length", "sepal_width"], hue="species") # 默认绘制data数据集中的所有变量，设置vars参数，绘制部分变量
g.map(plt.scatter)

Out[23]:

<seaborn.axisgrid.PairGrid at 0x16baad68>

对角线上方和下方绘制不同图表¶

map_upper()、map_lower()方法

In [24]:

g = sns.PairGrid(iris)
g.map_upper(plt.scatter)
g.map_lower(sns.kdeplot, cmap="Blues_d")
g.map_diag(sns.kdeplot, lw=3, legend=False)

Out[24]:

<seaborn.axisgrid.PairGrid at 0x173d71d0>

x_vars参数、y_vars参数设置不同的变量¶

In [25]:

g = sns.PairGrid(tips, y_vars=["tip"], x_vars=["total_bill", "size"], size=4)
g.map(sns.regplot, color=".3")
g.set(ylim=(-1, 11), yticks=[0, 5, 10])

Out[25]:

<seaborn.axisgrid.PairGrid at 0x16a45438>

设置图表的其他元素¶

PairGrid方法和map方法中有一系列参数可供设置

In [26]:

g = sns.PairGrid(tips, hue="size", palette="GnBu_d")
g.map(plt.scatter, s=50, edgecolor="white")
g.add_legend()

Out[26]:

<seaborn.axisgrid.PairGrid at 0x15eefc88>

sns.pairplot()方法¶

默认绘制散点图和直方图，非对角线可绘制回归线，对角线可绘制核密度图

In [27]:

sns.pairplot(iris, hue="species", size=2.5)

Out[27]:

<seaborn.axisgrid.PairGrid at 0x15e8b358>

设置图表的其他元素¶

In [28]:

g = sns.pairplot(iris, hue="species", palette="Set2", diag_kind="kde", size=2.5)