カテゴリー: プログラミング

import numpy as np
import pandas as pd
import sklearn
from sklearn.datasets import load_iris


iris_dataset = load_iris()

print(iris_dataset.keys())
#>>>  ['DESCR', 'target_names', 'feature_names', 'target', 'data']
#DESCRはデータセットの解説があるという事
#その他はデータの種類を示している


print(iris_dataset["DESCR"[:193]]+"\n...")
#データセットの詳細を知る為にDESCRを表示

import numpy as np

import pandas as pd

import sklearn

from sklearn.datasets import load_iris

iris_dataset = load_iris()

print(iris_dataset.keys())

#>>> ['DESCR', 'target_names', 'feature_names', 'target', 'data']

#DESCRはデータセットの解説があるという事

#その他はデータの種類を示している

print(iris_dataset["DESCR"[:193]]+"\n...")

#データセットの詳細を知る為にDESCRを表示

結果

150のデータと4つのデータの種類設定コード

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 ...

...

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 ...

...

targetnameの種類を知りたい場合（アイリスの名称一覧）

print(iris_dataset["target_names"])

1	print(iris_dataset["target_names"])

['setosa' 'versicolor' 'virginica']

1	['setosa' 'versicolor' 'virginica']

ローカル環境でJupyter

データセットの最初の５行だけ出す

データセットのダウンロード

データフレームを綺麗に表示

linspaceとsin関数でのグラフ

改行