

In [2]:

    
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from pandas import Series,DataFrame
import seaborn as sns



In [3]:

    
#https://www.kaggle.com/residentmario/bivariate-plotting-with-pandas/data
pokemon = pd.read_csv("Pokemon.csv", index_col=0)
pokemon.head()









    Out[3]:







  
    
      
      Name
      Type 1
      Type 2
      Total
      HP
      Attack
      Defense
      Sp. Atk
      Sp. Def
      Speed
      Generation
      Legendary
    
    
      #
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      Bulbasaur
      Grass
      Poison
      318
      45
      49
      49
      65
      65
      45
      1
      False
    
    
      2
      Ivysaur
      Grass
      Poison
      405
      60
      62
      63
      80
      80
      60
      1
      False
    
    
      3
      Venusaur
      Grass
      Poison
      525
      80
      82
      83
      100
      100
      80
      1
      False
    
    
      3
      VenusaurMega Venusaur
      Grass
      Poison
      625
      80
      100
      123
      122
      120
      80
      1
      False
    
    
      4
      Charmander
      Fire
      NaN
      309
      39
      52
      43
      60
      50
      65
      1
      False



In [ ]:

Multivariate plotting 多变量

df.plot.scatter()
df.plot.box()
sns.heatmap
pd.plotting.parallel_coordinates

参考



In [17]:

    
sns.lmplot(x='Attack',y='Defense',hue='Legendary',fit_reg=False,markers=['x','o'],data = pokemon)
plt.show()



In [22]:

    
sns.heatmap(
    pokemon.loc[:, ['HP', 'Attack', 'Sp. Atk', 'Defense', 'Sp. Def', 'Speed']].corr(),
    annot=True
)
plt.show()



In [23]:

    
import pandas as pd
from pandas.plotting import parallel_coordinates

p = (pokemon[(pokemon['Type 1'].isin(["Psychic", "Fighting"]))]
         .loc[:, ['Type 1', 'Attack', 'Sp. Atk', 'Defense', 'Sp. Def']]
    )

parallel_coordinates(p, 'Type 1')
plt.show()

非常实用的方法是将Seaborn的分类图分为三类，将分类变量每个级别的每个观察结果显示出来，显示每个观察分布的抽象表示，以及应用统计估计显示的权重趋势和置信区间：

第一个包括函数swarmplot()和stripplot()
第二个包括函数boxplot()和violinplot()
第三个包括函数barplot()和pointplt()



In [8]:

    
sns.swarmplot(x='Generation',y='Defense',hue='Legendary',data = pokemon)
plt.show()
sns.stripplot(x='Generation',y='Defense',hue='Legendary',data = pokemon)
plt.show()



In [9]:

    
sns.boxplot(x="Generation", y="Total", hue='Legendary', data=pokemon)
plt.show()
sns.violinplot(x='Generation',y='Defense',hue='Legendary',data = pokemon,
               split=True, inner="stick", palette="Set3");
plt.show()



In [24]:

    
sns.barplot(x="Generation", y="Defense", hue="Legendary", data=pokemon)
plt.show()
sns.countplot(x="Generation",hue="Legendary", data=pokemon )
plt.show()
sns.pointplot(x="Generation", y="Defense", hue="Legendary", data=pokemon)
plt.show()



In [29]:

    
# sns.swarmplot(x='Generation',y='Defense',hue='Legendary',data = pokemon)
sns.factorplot(x="Generation", y="Defense", hue="Legendary", data=pokemon, kind="swarm");
sns.factorplot(x="Generation", y="Defense", hue="Legendary",data=pokemon, kind="box")
sns.factorplot(x="Generation", y="Defense", hue="Legendary",data=pokemon, kind="bar")
sns.factorplot(x="Generation", y="Defense", col="Legendary", data=pokemon, kind="bar")
sns.factorplot(x="Generation", y="Defense", row="Legendary", data=pokemon, kind="bar")
# hue row,col 三个关系
#sns.factorplot(x="Generation", y="Defense", hue="Legendary", col="time", data=pokemon, kind="bar")
plt.show()



In [27]:

    
g = sns.FacetGrid(pokemon, col ="Generation", row="Legendary")
g.map(sns.kdeplot, "Attack")
plt.show()



In [31]:

    
sns.pairplot(pokemon[['HP', 'Attack', 'Defense']])
plt.show()



In [20]:

    
g = sns.PairGrid(pokemon,
                 x_vars=["Generation","Legendary"],
                 y_vars=["Attack","Defense","Sp. Atk",  "Sp. Def"],
                 aspect=.85, size=6)
g.map(sns.violinplot,palette="pastel")
plt.show()

Faceting



In [ ]:

	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
#
1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False

Multivariate plotting 多变量

非常实用的方法是将Seaborn的分类图分为三类，将分类变量每个级别的每个观察结果显示出来，显示每个观察分布的抽象表示，以及应用统计估计显示的权重趋势和置信区间：

1. Facet Grid 2 . Pair Plot

Faceting