seaborn中的sns库可以画出很好看的图试用sns.pairplot()画出基于 “iris”数据集各两对属性的图



In [55]:

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

sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")
g = sns.pairplot(iris, hue="species")
sns.plt.show()



In [56]:

    
iris









    Out[56]:






  
    
      
      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
    
    
      5
      5.4
      3.9
      1.7
      0.4
      setosa
    
    
      6
      4.6
      3.4
      1.4
      0.3
      setosa
    
    
      7
      5.0
      3.4
      1.5
      0.2
      setosa
    
    
      8
      4.4
      2.9
      1.4
      0.2
      setosa
    
    
      9
      4.9
      3.1
      1.5
      0.1
      setosa
    
    
      10
      5.4
      3.7
      1.5
      0.2
      setosa
    
    
      11
      4.8
      3.4
      1.6
      0.2
      setosa
    
    
      12
      4.8
      3.0
      1.4
      0.1
      setosa
    
    
      13
      4.3
      3.0
      1.1
      0.1
      setosa
    
    
      14
      5.8
      4.0
      1.2
      0.2
      setosa
    
    
      15
      5.7
      4.4
      1.5
      0.4
      setosa
    
    
      16
      5.4
      3.9
      1.3
      0.4
      setosa
    
    
      17
      5.1
      3.5
      1.4
      0.3
      setosa
    
    
      18
      5.7
      3.8
      1.7
      0.3
      setosa
    
    
      19
      5.1
      3.8
      1.5
      0.3
      setosa
    
    
      20
      5.4
      3.4
      1.7
      0.2
      setosa
    
    
      21
      5.1
      3.7
      1.5
      0.4
      setosa
    
    
      22
      4.6
      3.6
      1.0
      0.2
      setosa
    
    
      23
      5.1
      3.3
      1.7
      0.5
      setosa
    
    
      24
      4.8
      3.4
      1.9
      0.2
      setosa
    
    
      25
      5.0
      3.0
      1.6
      0.2
      setosa
    
    
      26
      5.0
      3.4
      1.6
      0.4
      setosa
    
    
      27
      5.2
      3.5
      1.5
      0.2
      setosa
    
    
      28
      5.2
      3.4
      1.4
      0.2
      setosa
    
    
      29
      4.7
      3.2
      1.6
      0.2
      setosa
    
    
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      120
      6.9
      3.2
      5.7
      2.3
      virginica
    
    
      121
      5.6
      2.8
      4.9
      2.0
      virginica
    
    
      122
      7.7
      2.8
      6.7
      2.0
      virginica
    
    
      123
      6.3
      2.7
      4.9
      1.8
      virginica
    
    
      124
      6.7
      3.3
      5.7
      2.1
      virginica
    
    
      125
      7.2
      3.2
      6.0
      1.8
      virginica
    
    
      126
      6.2
      2.8
      4.8
      1.8
      virginica
    
    
      127
      6.1
      3.0
      4.9
      1.8
      virginica
    
    
      128
      6.4
      2.8
      5.6
      2.1
      virginica
    
    
      129
      7.2
      3.0
      5.8
      1.6
      virginica
    
    
      130
      7.4
      2.8
      6.1
      1.9
      virginica
    
    
      131
      7.9
      3.8
      6.4
      2.0
      virginica
    
    
      132
      6.4
      2.8
      5.6
      2.2
      virginica
    
    
      133
      6.3
      2.8
      5.1
      1.5
      virginica
    
    
      134
      6.1
      2.6
      5.6
      1.4
      virginica
    
    
      135
      7.7
      3.0
      6.1
      2.3
      virginica
    
    
      136
      6.3
      3.4
      5.6
      2.4
      virginica
    
    
      137
      6.4
      3.1
      5.5
      1.8
      virginica
    
    
      138
      6.0
      3.0
      4.8
      1.8
      virginica
    
    
      139
      6.9
      3.1
      5.4
      2.1
      virginica
    
    
      140
      6.7
      3.1
      5.6
      2.4
      virginica
    
    
      141
      6.9
      3.1
      5.1
      2.3
      virginica
    
    
      142
      5.8
      2.7
      5.1
      1.9
      virginica
    
    
      143
      6.8
      3.2
      5.9
      2.3
      virginica
    
    
      144
      6.7
      3.3
      5.7
      2.5
      virginica
    
    
      145
      6.7
      3.0
      5.2
      2.3
      virginica
    
    
      146
      6.3
      2.5
      5.0
      1.9
      virginica
    
    
      147
      6.5
      3.0
      5.2
      2.0
      virginica
    
    
      148
      6.2
      3.4
      5.4
      2.3
      virginica
    
    
      149
      5.9
      3.0
      5.1
      1.8
      virginica
    
  

150 rows × 5 columns



In [70]:

    
"""get the data corrspoding to 'versicolor' and 'setosa' from iris data"""
s = iris['species']
start = s[s == 'virginica'].index[0]
end = s[s == 'virginica'].index[-1]
df1 = iris.loc[start:end, :]

start = s[s == 'setosa'].index[0]
end = s[s == 'setosa'].index[-1]
df2 = iris.loc[start:end, :]

df = pd.concat([df1,df2],ignore_index=True)



In [71]:

    
#10-fold cross validation
from sklearn.cross_validation import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn import model_selection
from sklearn.metrics import classification_report

#get the test_data and label_data from df
X = df.loc[:,:'petal_width']
y = df.loc[:,'species']

model = LogisticRegression()    #choose the logistictRegression
cross_val_score(model,X,y,cv=10)









    Out[71]:





array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])



In [72]:

    
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y,test_size=0.5, random_state=42)
model = LogisticRegression()
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))









    



             precision    recall  f1-score   support

     setosa       1.00      1.00      1.00        20
  virginica       1.00      1.00      1.00        30

avg / total       1.00      1.00      1.00        50

为何正确率是百分之一百



In [ ]:

	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
5	5.4	3.9	1.7	0.4	setosa
6	4.6	3.4	1.4	0.3	setosa
7	5.0	3.4	1.5	0.2	setosa
8	4.4	2.9	1.4	0.2	setosa
9	4.9	3.1	1.5	0.1	setosa
10	5.4	3.7	1.5	0.2	setosa
11	4.8	3.4	1.6	0.2	setosa
12	4.8	3.0	1.4	0.1	setosa
13	4.3	3.0	1.1	0.1	setosa
14	5.8	4.0	1.2	0.2	setosa
15	5.7	4.4	1.5	0.4	setosa
16	5.4	3.9	1.3	0.4	setosa
17	5.1	3.5	1.4	0.3	setosa
18	5.7	3.8	1.7	0.3	setosa
19	5.1	3.8	1.5	0.3	setosa
20	5.4	3.4	1.7	0.2	setosa
21	5.1	3.7	1.5	0.4	setosa
22	4.6	3.6	1.0	0.2	setosa
23	5.1	3.3	1.7	0.5	setosa
24	4.8	3.4	1.9	0.2	setosa
25	5.0	3.0	1.6	0.2	setosa
26	5.0	3.4	1.6	0.4	setosa
27	5.2	3.5	1.5	0.2	setosa
28	5.2	3.4	1.4	0.2	setosa
29	4.7	3.2	1.6	0.2	setosa
...	...	...	...	...	...
120	6.9	3.2	5.7	2.3	virginica
121	5.6	2.8	4.9	2.0	virginica
122	7.7	2.8	6.7	2.0	virginica
123	6.3	2.7	4.9	1.8	virginica
124	6.7	3.3	5.7	2.1	virginica
125	7.2	3.2	6.0	1.8	virginica
126	6.2	2.8	4.8	1.8	virginica
127	6.1	3.0	4.9	1.8	virginica
128	6.4	2.8	5.6	2.1	virginica
129	7.2	3.0	5.8	1.6	virginica
130	7.4	2.8	6.1	1.9	virginica
131	7.9	3.8	6.4	2.0	virginica
132	6.4	2.8	5.6	2.2	virginica
133	6.3	2.8	5.1	1.5	virginica
134	6.1	2.6	5.6	1.4	virginica
135	7.7	3.0	6.1	2.3	virginica
136	6.3	3.4	5.6	2.4	virginica
137	6.4	3.1	5.5	1.8	virginica
138	6.0	3.0	4.8	1.8	virginica
139	6.9	3.1	5.4	2.1	virginica
140	6.7	3.1	5.6	2.4	virginica
141	6.9	3.1	5.1	2.3	virginica
142	5.8	2.7	5.1	1.9	virginica
143	6.8	3.2	5.9	2.3	virginica
144	6.7	3.3	5.7	2.5	virginica
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica