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Edit and run



In [1]:

    
import requests
import pandas as pd



In [2]:

    
r=requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')

r is response



In [3]:

    
print(r.headers)
print(r.text[:100])









    



{'ETag': '"22e9fc8d-11c7-2996b79a79ec0"', 'Last-Modified': 'Tue, 09 Mar 1993 00:27:15 GMT', 'Content-Type': 'text/plain; charset=UTF-8', 'Date': 'Fri, 03 Feb 2017 00:44:45 GMT', 'Connection': 'close', 'Accept-Ranges': 'bytes', 'Content-Length': '4551', 'Server': 'Apache/2.2.15 (CentOS)'}
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,



In [4]:

    
with open('iris.data','w') as fp:
    fp.write(r.text)



In [5]:

    
df=pd.read_csv('iris.data',names=['sepal length', 'sepal width', 'petal length', 'petal width', 'class'])



In [6]:

    
df.head()









    Out[6]:






  
    
      
      sepal length
      sepal width
      petal length
      petal width
      class
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      Iris-setosa
    
    
      1
      4.9
      3.0
      1.4
      0.2
      Iris-setosa
    
    
      2
      4.7
      3.2
      1.3
      0.2
      Iris-setosa
    
    
      3
      4.6
      3.1
      1.5
      0.2
      Iris-setosa
    
    
      4
      5.0
      3.6
      1.4
      0.2
      Iris-setosa



In [7]:

    
df['sepal length'][:10]









    Out[7]:





0    5.1
1    4.9
2    4.7
3    4.6
4    5.0
5    5.4
6    4.6
7    5.0
8    4.4
9    4.9
Name: sepal length, dtype: float64

Another way that we can perform this data slicing is to use the .ix[row,column] notation. Let's select the first two columns and first four rows using this notation. :3 means 0 to 3



In [8]:

    
df.ix[:3,1]









    Out[8]:





0    3.5
1    3.0
2    3.2
3    3.1
Name: sepal width, dtype: float64



In [9]:

    
df[(df['class']=='Iris-setosa') & (df['sepal width']>3.5)]









    Out[9]:






  
    
      
      sepal length
      sepal width
      petal length
      petal width
      class
    
  
  
    
      4
      5.0
      3.6
      1.4
      0.2
      Iris-setosa
    
    
      5
      5.4
      3.9
      1.7
      0.4
      Iris-setosa
    
    
      10
      5.4
      3.7
      1.5
      0.2
      Iris-setosa
    
    
      14
      5.8
      4.0
      1.2
      0.2
      Iris-setosa
    
    
      15
      5.7
      4.4
      1.5
      0.4
      Iris-setosa
    
    
      16
      5.4
      3.9
      1.3
      0.4
      Iris-setosa
    
    
      18
      5.7
      3.8
      1.7
      0.3
      Iris-setosa
    
    
      19
      5.1
      3.8
      1.5
      0.3
      Iris-setosa
    
    
      21
      5.1
      3.7
      1.5
      0.4
      Iris-setosa
    
    
      22
      4.6
      3.6
      1.0
      0.2
      Iris-setosa
    
    
      32
      5.2
      4.1
      1.5
      0.1
      Iris-setosa
    
    
      33
      5.5
      4.2
      1.4
      0.2
      Iris-setosa
    
    
      44
      5.1
      3.8
      1.9
      0.4
      Iris-setosa
    
    
      46
      5.1
      3.8
      1.6
      0.2
      Iris-setosa
    
    
      48
      5.3
      3.7
      1.5
      0.2
      Iris-setosa



In [10]:

    
df[(df['class']=='Iris-setosa') & (df['sepal width']>3.5)].describe()









    Out[10]:






  
    
      
      sepal length
      sepal width
      petal length
      petal width
    
  
  
    
      count
      15.000000
      15.000000
      15.000000
      15.000000
    
    
      mean
      5.293333
      3.866667
      1.480000
      0.273333
    
    
      std
      0.315021
      0.225726
      0.214476
      0.103280
    
    
      min
      4.600000
      3.600000
      1.000000
      0.100000
    
    
      25%
      5.100000
      3.700000
      1.400000
      0.200000
    
    
      50%
      5.300000
      3.800000
      1.500000
      0.200000
    
    
      75%
      5.450000
      3.950000
      1.550000
      0.400000
    
    
      max
      5.800000
      4.400000
      1.900000
      0.400000



In [11]:

    
df.corr()









    Out[11]:






  
    
      
      sepal length
      sepal width
      petal length
      petal width
    
  
  
    
      sepal length
      1.000000
      -0.109369
      0.871754
      0.817954
    
    
      sepal width
      -0.109369
      1.000000
      -0.420516
      -0.356544
    
    
      petal length
      0.871754
      -0.420516
      1.000000
      0.962757
    
    
      petal width
      0.817954
      -0.356544
      0.962757
      1.000000

df.corr 解释了相关性



In [12]:

    
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
import numpy as np



In [13]:

    
fig,ax=plt.subplots(figsize=(6,4))
ax.hist(df['petal width'],color='black')
ax.set_ylabel('Count',fontsize=12)
ax.set_xlabel('Width',fontsize=12)
plt.title('Iris Petal Width',fontsize=14,y=1.01)









    Out[13]:





<matplotlib.text.Text at 0x7fe2fc8f3438>



In [14]:

    
fig,ax=plt.subplots(2,2,figsize=(6,4))
ax[0][0].hist(df['petal width'],color='black')
ax[0][0].set_ylabel('Count',fontsize=12)
ax[0][0].set_xlabel('Width',fontsize=12)
ax[0][0].set_title('Iris Petal Width',fontsize=14,y=1.01)

ax[0][1].hist(df['petal length'],color='black')
ax[0][1].set_ylabel('Count',fontsize=12)
ax[0][1].set_xlabel('Length',fontsize=12)
ax[0][1].set_title('Iris Petal Length',fontsize=14,y=1.01)

ax[1][0].hist(df['sepal width'],color='black')
ax[1][0].set_ylabel('Count',fontsize=12)
ax[1][0].set_xlabel('Width',fontsize=12)
ax[1][0].set_title('Iris Sepal Width',fontsize=14,y=1.01)

ax[1][1].hist(df['sepal length'],color='black')
ax[1][1].set_ylabel('Count',fontsize=12)
ax[1][1].set_xlabel('Length',fontsize=12)
ax[1][1].set_title('Iris Sepal Length',fontsize=14,y=1.01)

plt.tight_layout()



In [15]:

    
fig,ax=plt.subplots(figsize=(6,6))
ax.scatter(df['petal width'],df['petal length'],color='green')
ax.set_xlabel('Petal Width')
ax.set_ylabel('Petal Length')
ax.set_title('Petal Scatterplot')









    Out[15]:





<matplotlib.text.Text at 0x7fe2fbfac390>



In [16]:

    
fig,ax=plt.subplots(figsize=(6,6))
ax.plot(df['petal length'],color='blue')
ax.set_xlabel('Specimen Number')
ax.set_ylabel('Petal Length')
ax.set_title('Petal Length Plot')









    Out[16]:





<matplotlib.text.Text at 0x7fe2faa16cf8>



In [17]:

    
import seaborn as sns
sns.pairplot(df,hue='class')









    Out[17]:





<seaborn.axisgrid.PairGrid at 0x7fe2fa9f9b00>



In [18]:

    
fig,ax=plt.subplots(2,2,figsize=(7,7))
sns.set(style='white',palette='muted')
sns.violinplot(x=df['class'],y=df['sepal length'],ax=ax[0,0])
sns.violinplot(x=df['class'],y=df['sepal width'],ax=ax[0,1])
sns.violinplot(x=df['class'],y=df['petal length'],ax=ax[1,0])
sns.violinplot(x=df['class'],y=df['petal width'],ax=ax[1,1])
fig.suptitle('Violin Plots',fontsize=16,y=1.03)
for i in ax.flat:
    plt.setp(i.get_xticklabels(),rotation=-90)
fig.tight_layout()



In [ ]:

	sepal length	sepal width	petal length	petal width	class
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

	sepal length	sepal width	petal length	petal width
count	15.000000	15.000000	15.000000	15.000000
mean	5.293333	3.866667	1.480000	0.273333
std	0.315021	0.225726	0.214476	0.103280
min	4.600000	3.600000	1.000000	0.100000
25%	5.100000	3.700000	1.400000	0.200000
50%	5.300000	3.800000	1.500000	0.200000
75%	5.450000	3.950000	1.550000	0.400000
max	5.800000	4.400000	1.900000	0.400000

	sepal length	sepal width	petal length	petal width
sepal length	1.000000	-0.109369	0.871754	0.817954
sepal width	-0.109369	1.000000	-0.420516	-0.356544
petal length	0.871754	-0.420516	1.000000	0.962757
petal width	0.817954	-0.356544	0.962757	1.000000