Author： 马肖
E-Mail： maxiaoscut@aliyun.com
GitHub： https://github.com/Albertsr

In [1]:

import numpy as np
import pandas as pd

import warnings 
warnings.filterwarnings("ignore")

import seaborn as sns
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris

%matplotlib inline
sns.set(style="white", color_codes=True)

In [2]:

dataset = load_iris()
feature_names = [i.strip(' (cm)') for i in dataset.feature_names]
iris = pd.DataFrame(data=dataset.data, columns=feature_names)

species_dict={0:'Setosa' ,1:'Versicolour', 2:'Virginica'}
iris['species'] = pd.Series(dataset.target).map(species_dict)
iris.head()

Out[2]:

	sepal length	sepal width	petal length	petal width	species
0	5.1	3.5	1.4	0.2	Setosa
1	4.9	3.0	1.4	0.2	Setosa
2	4.7	3.2	1.3	0.2	Setosa
3	4.6	3.1	1.5	0.2	Setosa
4	5.0	3.6	1.4	0.2	Setosa

探索sepal length与sepal width之间的联系¶

In [3]:

sns.jointplot(x='sepal length', y='sepal width', data=iris, kind='scatter', size=5, ratio=7, space=0, color="b")

Out[3]:

<seaborn.axisgrid.JointGrid at 0x2a01263b710>

In [4]:

g = sns.FacetGrid(iris, hue="species", size=5)
g.map(plt.scatter, "sepal length", "sepal width").add_legend()

Out[4]:

<seaborn.axisgrid.FacetGrid at 0x2a01263b2e8>

绘制不同品种petal length的箱型图、散点图¶

In [5]:

sns.boxplot(x="species", y="petal length", data=iris)
sns.stripplot(x="species", y="petal length", data=iris, jitter=True)

Out[5]:

<matplotlib.axes._subplots.AxesSubplot at 0x2a0165714e0>

运用小提琴图查看不同品种的petal length分布¶

In [6]:

sns.violinplot(x="species", y="petal length", data=iris, size=6)
sns.stripplot(x="species", y="petal length", data=iris, jitter=True, size=6, palette='Set2')

Out[6]:

<matplotlib.axes._subplots.AxesSubplot at 0x2a01660ae10>

绘制不同品种的petal length对应的KDE曲线¶

In [7]:

sns.FacetGrid(iris, hue="species", size=4) \
   .map(sns.kdeplot, "petal length").add_legend()

Out[7]:

<seaborn.axisgrid.FacetGrid at 0x2a0164cdef0>

绘制特征之间的两两相关图¶

In [8]:

sns.pairplot(iris, hue="species", size=2, diag_kind='kde')

Out[8]:

<seaborn.axisgrid.PairGrid at 0x2a0166efa20>

运用pandas自带的boxplot函数绘制所有数值型特征的箱型图¶

In [9]:

iris.boxplot(by="species", figsize=(12, 6))

Out[9]:

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002A016F194A8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A017214DD8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002A0172365F8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A01725BDD8>]],
      dtype=object)

In [10]:

# Another multivariate visualization technique pandas has is parallel_coordinates
# Parallel coordinates plots each feature on a separate column & then draws lines
# connecting the features for each data sample
from pandas.tools.plotting import parallel_coordinates
parallel_coordinates(iris, "species")

Out[10]:

<matplotlib.axes._subplots.AxesSubplot at 0x2a0174b5518>

Andrews曲线¶

In [11]:

# Andrews Curves involve using attributes of samples as coefficients for Fourier series and then plotting these
from pandas.tools.plotting import andrews_curves
andrews_curves(iris, "species")

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0x2a01868f358>