【Scikit-learn】糖尿病患者のデータセットを確認、相関マップを表示［Python］

from sklearn.datasets import load_diabetes

diabetes = load_diabetes()

print(diabetes.keys())

＜セル１＞

from sklearn.datasets import load_diabetes
import pandas as pd

diabetes = load_diabetes()

print(diabetes.keys())

実行結果
dict_keys(['data', 'target', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])

print(diabetes.DESCR)

実行結果
.. _diabetes_dataset:

Diabetes dataset
----------------

Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after baseline.

**Data Set Characteristics:**

  :Number of Instances: 442

  :Number of Attributes: First 10 columns are numeric predictive values

  :Target: Column 11 is a quantitative measure of disease progression one year after baseline

  :Attribute Information:
      - Age
      - Sex
      - Body mass index
      - Average blood pressure
      - S1
      - S2
      - S3
      - S4
      - S5
      - S6

Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).

Source URL:
https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html

For more information see:
Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499.
(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)

import pandas as pd

df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)

df["target"] = diabetes.target

df

実行結果

df.isnull().sum()

実行結果
age       0
sex       0
bmi       0
bp        0
s1        0
s2        0
s3        0
s4        0
s5        0
s6        0
target    0
dtype: int64

corr = df.corr()

corr

実行結果

import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline

plt.figure(figsize=(10,7))

sns.heatmap(corr, annot=True)

import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline

plt.figure(figsize=(10,7))

sns.heatmap(corr, annot=True)

実行結果