四、绘图可视化之Seaborn

2023-06-05,,

Seaborn-Powerful Matplotlib Extension

seaborn实现直方图和密度图

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
s1=pd.Series(np.random.randn(1000))
plt.hist(s1)#直方图
  结果:
(array([  1.,   4.,  19.,  88., 231., 256., 218., 140.,  37.,   6.]),
array([-3.98123959, -3.28090493, -2.58057027, -1.8802356 , -1.17990094,
-0.47956628, 0.22076838, 0.92110304, 1.6214377 , 2.32177237,
3.02210703]),
<a list of 10 Patch objects>)

aaarticlea/png;base64,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" alt="" />

s1.plot(kind='kde')#密度图

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xb11fba8>

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" alt="" />

sns.distplot(s1,hist=True,kde=True)#可直接简写为sns.distplot(s1),因为默认情况下hist=True,kde=True

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xb205390>

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" alt="" />

sns.distplot(s1,bins=20,rug=True)#bins=20指的是分为20等份,rug用于显示数据的密集度    在jupyter notebook中可以通过shift+Tab查看函数功能

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xbe64240> 

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" alt="" />

sns.kdeplot(s1,shade=True,color='r')#密度图,shade会将曲线下边颜色进行填充,默认为蓝色,可修改颜色

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xda45908>

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" alt="" />

sns.rugplot(s1,height=1)#密集度图

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0x119b4198>

aaarticlea/png;base64,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" alt="" />

seaborn实现柱状图和热力图

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

  

df=sns.load_dataset('flights')#通过在线的repository(存储库)下载数据,sns的内置方法
print(df.head())
df.shape

  结果:

   year     month  passengers
0 1949 January 112
1 1949 February 118
2 1949 March 132
3 1949 April 129
4 1949 May 121
(144, 3)
df=df.pivot(index='month',columns='year',values='passengers')#生成透视表,阅读数据更加方便
print(df)

  结果:

year       1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  \
month
January 112 115 145 171 196 204 242 284 315 340 360
February 118 126 150 180 196 188 233 277 301 318 342
March 132 141 178 193 236 235 267 317 356 362 406
April 129 135 163 181 235 227 269 313 348 348 396
May 121 125 172 183 229 234 270 318 355 363 420
June 135 149 178 218 243 264 315 374 422 435 472
July 148 170 199 230 264 302 364 413 465 491 548
August 148 170 199 242 272 293 347 405 467 505 559
September 136 158 184 209 237 259 312 355 404 404 463
October 119 133 162 191 211 229 274 306 347 359 407
November 104 114 146 172 180 203 237 271 305 310 362
December 118 140 166 194 201 229 278 306 336 337 405 year 1960
month
January 417
February 391
March 419
April 461
May 472
June 535
July 622
August 606
September 508
October 461
November 390
December 432
sns.heatmap(df)#热力图

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xacac7f0>

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" alt="" width="400" height="273" />

sns.heatmap(df,annot=True,fmt='d')#annot显示是否有字,fmt=‘d’表示显示整数数字

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xb345e10>

s=df.sum()
print(s)
print(s.index)
print(s.values)

  结果:

year
1949 1520
1950 1676
1951 2042
1952 2364
1953 2700
1954 2867
1955 3408
1956 3939
1957 4421
1958 4572
1959 5140
1960 5714
dtype: int64
Int64Index([1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,
1960],
dtype='int64', name='year')
[1520 1676 2042 2364 2700 2867 3408 3939 4421 4572 5140 5714]

  

sns.barplot(x=s.index,y=s.values)

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0x26009b0>

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" alt="" />

print(type(s))
s.plot(kind='bar')#复习matplotlib画出柱状图

  结果:

<matplotlib.axes._subplots.AxesSubplot at 0xbce8f98>

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" alt="" />

seaborn设置图形显示效果

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

  

x=np.linspace(0,14,100)#等差数列,在0,14之间等分为100份
y1=np.sin(x)
y2=np.sin(x+2)*1.25

  

def sinplot():
plt.plot(x,y1)
plt.plot(x,y2)

  

sinplot()

  结果:

import seaborn as sns
sinplot()#seaborn为其定义了5种风格,下图为默认风格
style=['darkgrid','dark','white','whitegrid','ticks']

  结果:

sns.set_style(style[3])
sinplot()
sns.axes_style()#显示出主题的参数设置,可更改,见下条程序

  结果:

{'axes.axisbelow': True,
'axes.edgecolor': '.8',
'axes.facecolor': 'white',
'axes.grid': True,
'axes.labelcolor': '.15',
'axes.linewidth': 1.0,
'figure.facecolor': 'white',
'font.family': ['sans-serif'],
'font.sans-serif': ['Arial',
'DejaVu Sans',
'Liberation Sans',
'Bitstream Vera Sans',
'sans-serif'],
'grid.color': '.8',
'grid.linestyle': '-',
'image.cmap': 'rocket',
'legend.frameon': False,
'legend.numpoints': 1,
'legend.scatterpoints': 1,
'lines.solid_capstyle': 'round',
'text.color': '.15',
'xtick.color': '.15',
'xtick.direction': 'out',
'xtick.major.size': 0.0,
'xtick.minor.size': 0.0,
'ytick.color': '.15',
'ytick.direction': 'out',
'ytick.major.size': 0.0,
'ytick.minor.size': 0.0}

sns.set_style(style[3],{'axes.facecolor':'red'})
sinplot()

  结果:

{'axes.axisbelow': True,
'axes.edgecolor': '.8',
'axes.facecolor': 'red',
'axes.grid': True,
'axes.labelcolor': '.15',
'axes.linewidth': 1.0,
'figure.facecolor': 'white',
'font.family': ['sans-serif'],
'font.sans-serif': ['Arial',
'DejaVu Sans',
'Liberation Sans',
'Bitstream Vera Sans',
'sans-serif'],
'grid.color': '.8',
'grid.linestyle': '-',
'image.cmap': 'rocket',
'legend.frameon': False,
'legend.numpoints': 1,
'legend.scatterpoints': 1,
'lines.solid_capstyle': 'round',
'text.color': '.15',
'xtick.color': '.15',
'xtick.direction': 'out',
'xtick.major.size': 0.0,
'xtick.minor.size': 0.0,
'ytick.color': '.15',
'ytick.direction': 'out',
'ytick.major.size': 0.0,
'ytick.minor.size': 0.0}

sinplot()#原本的设置已经改变 ,要想恢复到以前的形式,需要清空当前风格设置

  结果:

sns.set()#清空当前风格设置
sinplot()#回到默认设置

  结果:

context=['paper','notebook','talk','poster']#曲线属性
sns.set_context(context[0])
sinplot()

  结果:

sns.set_context(context[3])#与第0个相比会变粗,变大
sinplot()
sns.plotting_context()#显示出属性的设置

  结果:

{'axes.labelsize': 17.6,
'axes.titlesize': 19.200000000000003,
'font.size': 19.200000000000003,
'grid.linewidth': 1.6,
'legend.fontsize': 16.0,
'lines.linewidth': 2.8000000000000003,
'lines.markeredgewidth': 0.0,
'lines.markersize': 11.200000000000001,
'patch.linewidth': 0.48,
'xtick.labelsize': 16.0,
'xtick.major.pad': 11.200000000000001,
'xtick.major.width': 1.6,
'xtick.minor.width': 0.8,
'ytick.labelsize': 16.0,
'ytick.major.pad': 11.200000000000001,
'ytick.major.width': 1.6,
'ytick.minor.width': 0.8}

sns.set_context(context[3],rc={'grid.linewidth': 10})#sns.set_context(context=None, font_scale=1, rc=None)
sinplot()

  结果:

sinplot()#原本的设置已经改变 ,要想恢复到以前的形式,需要清空当前风格设置

  结果:

sns.set()#清空当前风格设置
sinplot()#回到默认设置

  结果:

seaborn强大的调色功能

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
def sinplot():
x=np.linspace(0,14,100)
plt.figure(figsize=(8,6))#调整图像大小
for i in range(4):
plt.plot(x,np.sin(x+i)*(i+0.75),label='sin(x+%s)*(%s+0.75)'%(i,i))
plt.legend()
sinplot()

  结果:

import seaborn as sns
sinplot()

  结果:

sns.color_palette()#当前系统所使用的调色板 每组中有三个值,分别为RGB

  结果:

[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),
(1.0, 0.4980392156862745, 0.054901960784313725),
(0.17254901960784313, 0.6274509803921569, 0.17254901960784313),
(0.8392156862745098, 0.15294117647058825, 0.1568627450980392),
(0.5803921568627451, 0.403921568627451, 0.7411764705882353),
(0.5490196078431373, 0.33725490196078434, 0.29411764705882354),
(0.8901960784313725, 0.4666666666666667, 0.7607843137254902),
(0.4980392156862745, 0.4980392156862745, 0.4980392156862745),
(0.7372549019607844, 0.7411764705882353, 0.13333333333333333),
(0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]
sns.palplot(sns.color_palette())#画出色板的颜色

  结果:

pal_style=['deep','muted','pastel','bright','dark','colorblind']#6种主题分类色板
sns.palplot(sns.color_palette('deep'))

  结果:

sns.set_palette(sns.color_palette('dark'))#设置为dark色板
sinplot()

  结果:

sns.set()#恢复到以前默认的色板
sinplot()

  结果:

with sns.color_palette('dark'):#设置临时色板
sinplot()

  结果:

sinplot()#上述为临时色板

  结果:

pall=sns.color_palette([(0.5,0.1,0.7),(0.3,0.1,0.9)])#生成自己的色板
sns.palplot(pall)

  结果:

sns.palplot(sns.color_palette('hls',8))#生成8种颜色的色板,随便指定数目

  结果:

四、绘图可视化之Seaborn的相关教程结束。

《四、绘图可视化之Seaborn.doc》

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