seaborn学习笔记（三）：直⽅图、条形图、条带图1 直⽅图与条形图
在过去很长⼀段时间，我⼀直分不清直⽅图和条形图，或者说⼀直认为两者是⼀样的，直到看到histplot()和barplot()两个绘图⽅法，前者是绘制直⽅图，后者是绘制条形图。通过仔细对⽐两者各项功能后，我得出结论，两者⼗分相似，但有些许不同：直⽅图侧重于统计数据在数轴上各个位置的分布情况，统计的对象往往是连续型数值数据的，根据数值的⼤⼩分区间进⾏分组统计，例如有100个学⽣的⾝⾼，需要统计100个学⽣在各个⾝⾼段的分布情况；条形图不同，条形图分组往往是针对离散型数据或者说定性的分类数据，例如对⽐男⽣的平均⾝⾼和⼥⽣的平均⾝⾼。不知道我这么理解对不对，欢迎留⾔讨论。
2 histplot()：直⽅图
主要参数如下：
data：绘图数据，可以是pandas.DataFrame，numpy.ndarray或者字典等
x，y：指定的x轴, y轴数据，可以是向量或者字符串，当是字符串时，⼀定是data中的⼀个key
hue：可以是向量（pandas中的⼀列，或者是list），也可以是字符串（data中的⼀个
key），seaborn将根据这⼀列设置不同颜⾊
weights: 数据加权的权重
stat: 柱形的统计⽅式
1）count：统计每个区间的值的个数
2）frequency：区间内取值个数除以区间宽度
3）probability或proportion：进⾏标准化使条形⾼度总和为1
4）percent：标准化使条形总⾼度为100
5）使条形总⾯积为1
bins: 字符型、整型、向量都可以，可以是引⽤规则的名称、箱⼦的数量或箱⼦的分段或者分箱规则名称，规则名称见下⽅⽰例
binwidth: 条形宽度
binrange: 条形边缘的最⼤值或最⼩值
discrete: 如果为True，则默认为binwidth=1，并绘制条形图，使其位于相应数据点的中
⼼。这避免了在使⽤离散（整数）数据时可能出现的“间隙”。
cumulative: 布尔型，是否逐个累加每个条形⾼度进⾏绘图
multiple: 直接看下⽂效果吧
element: 直接看下⽂效果吧
fill: 条形内部是否填充颜⾊
shrink: 缩⼩条形的宽度
kde: 是否⽣成核密度曲线
color: 设置条形颜⾊
legend: 是否显⽰图例
ax: 绘图的坐标轴实例
In [1]:
import numpy as np import pandas as pd
import matplotlib.pyplot as plt import seaborn as
sns In [2]:
penguins = sns .load_dataset('penguins',data_home ='.'
)In [3]:
penguins .head(2)2.1 为图像设置标题
histplot()⽅法返回值为matplotlib.axes._subplots.AxesSubplot 类实例，通过实例的set_title()⽅法可以为图像添加标题：
In [4]:
pic = sns .histplot(penguins, x ="flipper_length_mm")pic .set_title('flipper_length_mm')2.2 ax ：⾃定义绘图坐标系
在不传递ax 参数时，seaborn 会⾃⾏创建坐标系进⾏绘图，我们也可以⾃⼰创建坐标系，通过ax 参数传递，这样做的好处是可以灵活绘制多个⼦图：
In [5]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0])pic = sns .histplot(penguins, x ="body_mass_g", ax =ax[1])另外，在histplot()⽅法中，没有提供太多关于坐标轴设置的参数，难以对坐标轴进⾏个性化定制，如果在绘图前，先创建好坐标轴，即可完成对坐标轴的设置(关于坐标轴的创建和设置，
请参考
In [6]:
ax =plt .axes((0.1, 0.1, 0.8, 0.7), facecolor ='green')
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax)2.3 x, y ：传递数据，控制图形⽅向
Out[3]:
species island bill_length_mm bill_depth_mm
flipper_length_mm
body_mass_g
sex 0Adelie Torgersen 39.118.7181.03750.0Male 1
Adelie
Torgersen
39.5
17.4
186.0
3800.0
Female
Out[4]:
Text(0.5, 1.0, 'flipper_length_mm')
x, y 参数不能同时传递，当传递x 时，x 轴为数据取值范围，y 轴为统计次数；当传递y 值时，y 轴为数据取值范围，x 轴为统计次数：
In [7]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0])pic .set_title('x')
pic = sns .histplot(penguins, y ="flipper_length_mm", ax =ax[1])pic .set_title('y')2.4 stat ：y
轴显⽰数据的⽅式
In [8]:
fig, ax =plt .subplots(1,5,constrained_layout =True , figsize =(15, 3))
_ = sns .histplot(penguins, x ="flipper_length_mm", stat ="count", ax =ax[0]) # count, 也是默认值_ = sns .histplot(penguins, x ="flipper_length_mm", stat ="frequency", ax =ax[1]) # frequency _ = sns .histplot(penguins, x ="flipper_length_mm", stat ="probability", ax =ax[2])# probability _ = sns .histplot(penguins, x ="flipper_length_mm", stat ="percent", ax =ax[3]) # percent _ = sns .histplot(penguins, x ="flipper_length_mm", stat ="density", ax =ax[4]) # density 2.5 bins ：指定分箱⽅式
指定分箱个数或者每个条形区间进⾏绘图：
In [9]:
fig, ax =plt .subplots(1,3,constrained_layout =True , figsize =(12, 3))pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0], bins =5)pic .set_title('bins=5')
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[1], bins =10)pic .set_title('bins=10')
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[2], bins =[150, 175, 200, 225, 250])pic .set_title('bins=[150, 175, 200, 225, 250]')
也可以指定分箱的规则：
In [10]:
fig, ax =plt .subplots(2,4,constrained_layout =True , figsize =(15, 6))
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="auto", ax =ax[0][0]) # count, 也是默认值pic .set_title('auto')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="fd", ax =ax[0][1]) # frequency pic .set_title('fd')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="doane", ax =ax[0][2])# probability pic .set_title('doane')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="scott", ax =ax[0][3]) # percent pic .set_title('scott')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="stone", ax =ax[1][0]) # count, 也是默认值pic .set_title('stone')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="rice", ax =ax[1][1]) # frequency pic .set_title('rice')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="sturges", ax =ax[1][2])# probability pic .set_title('sturges')
pic = sns .histplot(penguins, x ="flipper_length_mm", bins ="sqrt", ax =ax[1][3]) # percent pic .set_title('sqrt')
Out[7]:
Text(0.5, 1.0, 'y')
Out[9]:
Text(0.5, 1.0, 'bins=[150, 175, 200, 225, 250]')
2.6 binwidth ：设置柱形宽度
In [11]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))
_ = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0], binwidth =1)_ = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[1], binwidth =3)2.7 cumulative
：累积每个条形⾼度进⾏绘图
In [12]:
sns .histplot(penguins, x ="flipper_length_mm", cumulative =True )In [13]:
sns .histplot(penguins, x ="flipper_length_mm", multiple ="layer")2.8 hue ：颜⾊区分条形组成
In [14]:
_ = sns .histplot(penguins, x ="flipper_length_mm", hue ="species") 2.9 element
In [15]:
fig, ax =plt .subplots(1,3,constrained_layout =True , figsize =(12, 3))
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[0], element ="bars")pic .set_title('element="bars"')
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[1], element ="step")pic .set_title('element="step')
直条图和直方图图片pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[2], element ="poly")pic .set_title('element="poly"')
D:\ProgramData\Anaconda3\envs\machine_learning\lib\site-packages\numpy\lib\histograms.py:669: RuntimeWarning: The number of b ins estimated may be suboptimal.
bin_edges, _ = _get_bin_edges(a, bins, range, weights)Out[10]:
Text(0.5, 1.0, 'sqrt')
Out[12]:
<AxesSubplot:xlabel='flipper_length_mm', ylabel='Count'>
Out[13]:
<AxesSubplot:xlabel='flipper_length_mm', ylabel='Count'>
Out[15]:
Text(0.5, 1.0, 'element="poly"')
2.10 multiple
In [16]:
fig, ax =plt .subplots(1,4,constrained_layout =True , figsize =(16, 3))
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[0], multiple ="layer")pic .set_title('multiple="layer"')
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[1], multiple ="dodge")pic .set_title('multiple="dodge')
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[2], multiple ="stack")pic .set_title('multiple="stack"')
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ="species", ax =ax[3], multiple ="fill")pic .set_title('multiple="fill"')2.11 kde
：是否⽣成核密度曲线
In [17]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0], kde =False ) # 默认值，不⽣成核密度曲线pic .set_title('kde=False')
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[1], kde =True ) # 值为True ，显⽰核密度曲线pic .set_title('kde=True')2.12 color
：设置条形颜⾊
In [18]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0], color ="#FFC0CB") # 可以使16进制颜⾊pic .set_title('color="#FFC0CB"')
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[1], color ="orange") # 也可以是 英⽂颜⾊字符串pic .set_title('color="orange"'
)In [19]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[0], color ="#FFC0CB") # 可以使16进制颜⾊pic .set_title('color="#FFC0CB"')
pic = sns .histplot(penguins, x ="flipper_length_mm", ax =ax[1], color ="orange") # 也可以是 英⽂颜⾊字符串pic .set_title('color="orange"')2.13 fill
：条形内部是否填充颜⾊
In [20]:
fig, ax =plt .subplots(1,2,constrained_layout =True , figsize =(8, 3))
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ='sex', ax =ax[0], fill =False )pic .set_title('fill=False')
pic = sns .histplot(penguins, x ="flipper_length_mm", hue ='sex', ax =ax[1], fill =True )pic .set_title('fill=True')
Out[16]:
Text(0.5, 1.0, 'multiple="fill"')
Out[17]:
Text(0.5, 1.0, 'kde=True')
Out[18]:
Text(0.5, 1.0, 'color="orange"')
Out[19]:
Text(0.5, 1.0, 'color="orange"')
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