Iris Dataset

In [28]:

    

%matplotlib inline

import matplotlib
import matplotlib.pyplot as plt
from sklearn import datasets
from pandas import DataFrame
matplotlib.style.use('ggplot')


    

In [22]:

    

iris = datasets.load_iris()


    

In [23]:

    

print(iris.keys())


    

    




dict_keys(['target', 'data', 'DESCR', 'feature_names', 'target_names'])

In [24]:

    

print(iris['DESCR'])


    

    




Iris Plants Database

Notes
-----
Data Set Characteristics:
    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
    :Summary Statistics:
    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
    ============== ==== ==== ======= ===== ====================
    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

References
----------
   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...

In [25]:

    

targets = {index:name for index, name in enumerate(iris['target_names'])}
print(targets)


    

    




{0: 'setosa', 1: 'versicolor', 2: 'virginica'}

In [26]:

    

frame = DataFrame(iris['data'], columns=iris['feature_names'])
frame.describe()


    

    
Out[26]:






  

    

      

      
sepal length (cm)
      
sepal width (cm)
      
petal length (cm)
      
petal width (cm)
    
  
  

    

      
count
      
150.000000
      
150.000000
      
150.000000
      
150.000000
    
    

      
mean
      
5.843333
      
3.054000
      
3.758667
      
1.198667
    
    

      
std
      
0.828066
      
0.433594
      
1.764420
      
0.763161
    
    

      
min
      
4.300000
      
2.000000
      
1.000000
      
0.100000
    
    

      
25%
      
5.100000
      
2.800000
      
1.600000
      
0.300000
    
    

      
50%
      
5.800000
      
3.000000
      
4.350000
      
1.300000
    
    

      
75%
      
6.400000
      
3.300000
      
5.100000
      
1.800000
    
    

      
max
      
7.900000
      
4.400000
      
6.900000
      
2.500000
    
  

In [38]:

    

figure = plt.figure()
axe = figure.gca()
axes = frame.plot(kind='kde', ax=axe)


    

In [36]:

    

fig = plt.figure()
axe = fig.gca()
axe = frame.plot(kind='box', ax=axe)


    

In [56]:

    

fig = plt.figure()
axe = fig.gca()
axe = frame.plot(x=iris.feature_names[0], y=iris.feature_names[1], kind='scatter', ax=axe)


    

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