統計データの可視化

ライブラリの読み込み

In [1]:
# iris データセットの読み込み
from sklearn.datasets import load_iris
iris = load_iris()

データセットの概要

In [2]:
print(iris.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%[email protected])
    :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 [3]:
# データセットの形状(特徴量:4 個、データ数:150 件)
print(iris.data.shape)
(150, 4)
In [4]:
# 花の種類
print(iris.target_names)
['setosa' 'versicolor' 'virginica']

Attributeの説明

  • sepal length:ガクの長さ
  • sepal width:ガクの幅
  • petal length:花弁の長さ
  • petal width:花弁の幅

データフレームの利用

In [11]:
# ライブラリの読み込み
import pandas as pd

# irisデータをデータフレームに編入
df = pd.DataFrame(iris.data, columns=iris.feature_names)

# target行がデフォルトでは 0,1,2 なので、これを種類名に置き換え
df['target'] = iris.target_names[iris.target]
# df['target_num'] = iris.target
In [12]:
# 先頭5件の表示
df.head()
Out[12]:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
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
In [13]:
# 概要の表示
df.describe()
Out[13]:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333
std 0.828066 0.435866 1.765298 0.762238
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

Matplotlib 2Dプロット

In [14]:
# ライブラリの読み込み(定番:matplotlib)
import matplotlib.pyplot as plt
In [16]:
# sepal length (cm) と sepal width (cm) をそれぞれ x,y に設定
x = df.loc[:, "sepal length (cm)"]
y = df.loc[:, "sepal width (cm)"]

# 散布図の表示
plt.scatter(x,y)
plt.show()

Matplotlib 3Dプロット

In [17]:
# Axes3D の読み込み
from mpl_toolkits.mplot3d import Axes3D
In [20]:
# sepal length (cm), sepal width (cm), petal length (cm) をそれぞれ x,y に設定
x = df.loc[:, "sepal length (cm)"]
y = df.loc[:, "sepal width (cm)"]
z = df.loc[:, "petal length (cm)"]

# 3Dでプロット
fig = plt.figure()
ax = Axes3D(fig)

# ax.plot(x, y, z, marker=”マーカーの種類”, color=”カラーコード”, ms=マーカーの大きさ, mew=マーカーの輪郭の太さ・・)
ax.plot(x, y, z, marker="x", color="#0000FF", ms=4, mew=0.5, linestyle='None')

# 軸ラベル
ax.set_xlabel('sepal length')
ax.set_ylabel('sepal width')
ax.set_zlabel('petal length')

# 表示
plt.show()

Seaborn ペアプロット

各特徴量のペアごとに散布図を表示させることができます。

In [19]:
# ライブラリの読み込み
import seaborn as sns

# ペアプロット
sns.pairplot(df, hue="target")
Out[19]:
<seaborn.axisgrid.PairGrid at 0x1a19a5a240>
In [ ]: