In [1]:

    

import pandas as pd
from sklearn.datasets import load_iris


    

In [2]:

    

data = load_iris()


    

In [3]:

    

print(type(data))


    

    




<class 'sklearn.utils.Bunch'>

In [4]:

    

print(issubclass(type(data), dict))


    

    




True

In [5]:

    

print(data.keys())


    

    




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

In [6]:

    

print(data['feature_names'])


    

    




['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

In [7]:

    

print(data.feature_names)


    

    




['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

In [8]:

    

print(data.filename)


    

    




/usr/local/lib/python3.7/site-packages/sklearn/datasets/data/iris.csv

In [9]:

    

print(data.DESCR)


    

    




.. _iris_dataset:

Iris plants dataset
--------------------

**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

The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.

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.

.. topic:: 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 [10]:

    

X = data.data
y = data.target


    

In [11]:

    

print(type(X))


    

    




<class 'numpy.ndarray'>

In [12]:

    

print(X.shape)


    

    




(150, 4)

In [13]:

    

print(X[:5])


    

    




[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]]

In [14]:

    

print(type(y))


    

    




<class 'numpy.ndarray'>

In [15]:

    

print(y.shape)


    

    




(150,)

In [16]:

    

print(y)


    

    




[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2]

In [17]:

    

df_X = pd.DataFrame(data.data, columns=data.feature_names)


    

In [18]:

    

print(df_X.head())


    

    




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

In [19]:

    

s_y = pd.Series(data.target)


    

In [20]:

    

print(s_y.head())


    

    




0    0
1    0
2    0
3    0
4    0
dtype: int64

In [21]:

    

print(df_X.describe())


    

    




       sepal length (cm)  sepal width (cm)  petal length (cm)  \
count         150.000000        150.000000         150.000000   
mean            5.843333          3.057333           3.758000   
std             0.828066          0.435866           1.765298   
min             4.300000          2.000000           1.000000   
25%             5.100000          2.800000           1.600000   
50%             5.800000          3.000000           4.350000   
75%             6.400000          3.300000           5.100000   
max             7.900000          4.400000           6.900000   

       petal width (cm)  
count        150.000000  
mean           1.199333  
std            0.762238  
min            0.100000  
25%            0.300000  
50%            1.300000  
75%            1.800000  
max            2.500000  

In [22]:

    

print(s_y.value_counts())


    

    




2    50
1    50
0    50
dtype: int64

In [23]:

    

X, y = load_iris(return_X_y=True)


    

In [24]:

    

print(type(X))


    

    




<class 'numpy.ndarray'>

In [25]:

    

print(X.shape)


    

    




(150, 4)

In [26]:

    

print(type(y))


    

    




<class 'numpy.ndarray'>

In [27]:

    

print(y.shape)


    

    




(150,)