numpy下_大作业 (1)

其他 2021-01-30 21:10:45 阅读次数: 0

大作业

导入数据

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris # 导入数据集
iris = load_iris() # 载入数据
X= iris.data
y = iris.target


target_dict = {
    
    0:'Iris-setosa',1:'Iris-versicolor',2:'Iris-virginica'}
def target(entry):
    if entry in target_dict:
        return target_dict[entry]
    else:
        return entry

target_1 = np.vectorize(target)
y = target_1(y)
y

array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica'], dtype='<U15')

iris_data = np.c_[X, y]
# iris_col = np.array(['sepallength', 'sepalwidth', 'petalength', 'patalwidth', 'species'])
# iris_data = np.insert(iris_data, 0, values = iris_col, axis = 0)
iris_data

array([['5.1', '3.5', '1.4', '0.2', 'Iris-setosa'],
       ['4.9', '3.0', '1.4', '0.2', 'Iris-setosa'],
       ['4.7', '3.2', '1.3', '0.2', 'Iris-setosa'],
       ['4.6', '3.1', '1.5', '0.2', 'Iris-setosa'],
       ['5.0', '3.6', '1.4', '0.2', 'Iris-setosa'],
       ['5.4', '3.9', '1.7', '0.4', 'Iris-setosa'],
       ['4.6', '3.4', '1.4', '0.3', 'Iris-setosa'],
       ['5.0', '3.4', '1.5', '0.2', 'Iris-setosa'],
       ['4.4', '2.9', '1.4', '0.2', 'Iris-setosa'],
       ['4.9', '3.1', '1.5', '0.1', 'Iris-setosa'],
       ['5.4', '3.7', '1.5', '0.2', 'Iris-setosa'],
       ['4.8', '3.4', '1.6', '0.2', 'Iris-setosa'],
       ['4.8', '3.0', '1.4', '0.1', 'Iris-setosa'],
       ['4.3', '3.0', '1.1', '0.1', 'Iris-setosa'],
       ['5.8', '4.0', '1.2', '0.2', 'Iris-setosa'],
       ['5.7', '4.4', '1.5', '0.4', 'Iris-setosa'],
       ['5.4', '3.9', '1.3', '0.4', 'Iris-setosa'],
       ['5.1', '3.5', '1.4', '0.3', 'Iris-setosa'],
       ['5.7', '3.8', '1.7', '0.3', 'Iris-setosa'],
       ['5.1', '3.8', '1.5', '0.3', 'Iris-setosa'],
       ['5.4', '3.4', '1.7', '0.2', 'Iris-setosa'],
       ['5.1', '3.7', '1.5', '0.4', 'Iris-setosa'],
       ['4.6', '3.6', '1.0', '0.2', 'Iris-setosa'],
       ['5.1', '3.3', '1.7', '0.5', 'Iris-setosa'],
       ['4.8', '3.4', '1.9', '0.2', 'Iris-setosa'],
       ['5.0', '3.0', '1.6', '0.2', 'Iris-setosa'],
       ['5.0', '3.4', '1.6', '0.4', 'Iris-setosa'],
       ['5.2', '3.5', '1.5', '0.2', 'Iris-setosa'],
       ['5.2', '3.4', '1.4', '0.2', 'Iris-setosa'],
       ['4.7', '3.2', '1.6', '0.2', 'Iris-setosa'],
       ['4.8', '3.1', '1.6', '0.2', 'Iris-setosa'],
       ['5.4', '3.4', '1.5', '0.4', 'Iris-setosa'],
       ['5.2', '4.1', '1.5', '0.1', 'Iris-setosa'],
       ['5.5', '4.2', '1.4', '0.2', 'Iris-setosa'],
       ['4.9', '3.1', '1.5', '0.2', 'Iris-setosa'],
       ['5.0', '3.2', '1.2', '0.2', 'Iris-setosa'],
       ['5.5', '3.5', '1.3', '0.2', 'Iris-setosa'],
       ['4.9', '3.6', '1.4', '0.1', 'Iris-setosa'],
       ['4.4', '3.0', '1.3', '0.2', 'Iris-setosa'],
       ['5.1', '3.4', '1.5', '0.2', 'Iris-setosa'],
       ['5.0', '3.5', '1.3', '0.3', 'Iris-setosa'],
       ['4.5', '2.3', '1.3', '0.3', 'Iris-setosa'],
       ['4.4', '3.2', '1.3', '0.2', 'Iris-setosa'],
       ['5.0', '3.5', '1.6', '0.6', 'Iris-setosa'],
       ['5.1', '3.8', '1.9', '0.4', 'Iris-setosa'],
       ['4.8', '3.0', '1.4', '0.3', 'Iris-setosa'],
       ['5.1', '3.8', '1.6', '0.2', 'Iris-setosa'],
       ['4.6', '3.2', '1.4', '0.2', 'Iris-setosa'],
       ['5.3', '3.7', '1.5', '0.2', 'Iris-setosa'],
       ['5.0', '3.3', '1.4', '0.2', 'Iris-setosa'],
       ['7.0', '3.2', '4.7', '1.4', 'Iris-versicolor'],
       ['6.4', '3.2', '4.5', '1.5', 'Iris-versicolor'],
       ['6.9', '3.1', '4.9', '1.5', 'Iris-versicolor'],
       ['5.5', '2.3', '4.0', '1.3', 'Iris-versicolor'],
       ['6.5', '2.8', '4.6', '1.5', 'Iris-versicolor'],
       ['5.7', '2.8', '4.5', '1.3', 'Iris-versicolor'],
       ['6.3', '3.3', '4.7', '1.6', 'Iris-versicolor'],
       ['4.9', '2.4', '3.3', '1.0', 'Iris-versicolor'],
       ['6.6', '2.9', '4.6', '1.3', 'Iris-versicolor'],
       ['5.2', '2.7', '3.9', '1.4', 'Iris-versicolor'],
       ['5.0', '2.0', '3.5', '1.0', 'Iris-versicolor'],
       ['5.9', '3.0', '4.2', '1.5', 'Iris-versicolor'],
       ['6.0', '2.2', '4.0', '1.0', 'Iris-versicolor'],
       ['6.1', '2.9', '4.7', '1.4', 'Iris-versicolor'],
       ['5.6', '2.9', '3.6', '1.3', 'Iris-versicolor'],
       ['6.7', '3.1', '4.4', '1.4', 'Iris-versicolor'],
       ['5.6', '3.0', '4.5', '1.5', 'Iris-versicolor'],
       ['5.8', '2.7', '4.1', '1.0', 'Iris-versicolor'],
       ['6.2', '2.2', '4.5', '1.5', 'Iris-versicolor'],
       ['5.6', '2.5', '3.9', '1.1', 'Iris-versicolor'],
       ['5.9', '3.2', '4.8', '1.8', 'Iris-versicolor'],
       ['6.1', '2.8', '4.0', '1.3', 'Iris-versicolor'],
       ['6.3', '2.5', '4.9', '1.5', 'Iris-versicolor'],
       ['6.1', '2.8', '4.7', '1.2', 'Iris-versicolor'],
       ['6.4', '2.9', '4.3', '1.3', 'Iris-versicolor'],
       ['6.6', '3.0', '4.4', '1.4', 'Iris-versicolor'],
       ['6.8', '2.8', '4.8', '1.4', 'Iris-versicolor'],
       ['6.7', '3.0', '5.0', '1.7', 'Iris-versicolor'],
       ['6.0', '2.9', '4.5', '1.5', 'Iris-versicolor'],
       ['5.7', '2.6', '3.5', '1.0', 'Iris-versicolor'],
       ['5.5', '2.4', '3.8', '1.1', 'Iris-versicolor'],
       ['5.5', '2.4', '3.7', '1.0', 'Iris-versicolor'],
       ['5.8', '2.7', '3.9', '1.2', 'Iris-versicolor'],
       ['6.0', '2.7', '5.1', '1.6', 'Iris-versicolor'],
       ['5.4', '3.0', '4.5', '1.5', 'Iris-versicolor'],
       ['6.0', '3.4', '4.5', '1.6', 'Iris-versicolor'],
       ['6.7', '3.1', '4.7', '1.5', 'Iris-versicolor'],
       ['6.3', '2.3', '4.4', '1.3', 'Iris-versicolor'],
       ['5.6', '3.0', '4.1', '1.3', 'Iris-versicolor'],
       ['5.5', '2.5', '4.0', '1.3', 'Iris-versicolor'],
       ['5.5', '2.6', '4.4', '1.2', 'Iris-versicolor'],
       ['6.1', '3.0', '4.6', '1.4', 'Iris-versicolor'],
       ['5.8', '2.6', '4.0', '1.2', 'Iris-versicolor'],
       ['5.0', '2.3', '3.3', '1.0', 'Iris-versicolor'],
       ['5.6', '2.7', '4.2', '1.3', 'Iris-versicolor'],
       ['5.7', '3.0', '4.2', '1.2', 'Iris-versicolor'],
       ['5.7', '2.9', '4.2', '1.3', 'Iris-versicolor'],
       ['6.2', '2.9', '4.3', '1.3', 'Iris-versicolor'],
       ['5.1', '2.5', '3.0', '1.1', 'Iris-versicolor'],
       ['5.7', '2.8', '4.1', '1.3', 'Iris-versicolor'],
       ['6.3', '3.3', '6.0', '2.5', 'Iris-virginica'],
       ['5.8', '2.7', '5.1', '1.9', 'Iris-virginica'],
       ['7.1', '3.0', '5.9', '2.1', 'Iris-virginica'],
       ['6.3', '2.9', '5.6', '1.8', 'Iris-virginica'],
       ['6.5', '3.0', '5.8', '2.2', 'Iris-virginica'],
       ['7.6', '3.0', '6.6', '2.1', 'Iris-virginica'],
       ['4.9', '2.5', '4.5', '1.7', 'Iris-virginica'],
       ['7.3', '2.9', '6.3', '1.8', 'Iris-virginica'],
       ['6.7', '2.5', '5.8', '1.8', 'Iris-virginica'],
       ['7.2', '3.6', '6.1', '2.5', 'Iris-virginica'],
       ['6.5', '3.2', '5.1', '2.0', 'Iris-virginica'],
       ['6.4', '2.7', '5.3', '1.9', 'Iris-virginica'],
       ['6.8', '3.0', '5.5', '2.1', 'Iris-virginica'],
       ['5.7', '2.5', '5.0', '2.0', 'Iris-virginica'],
       ['5.8', '2.8', '5.1', '2.4', 'Iris-virginica'],
       ['6.4', '3.2', '5.3', '2.3', 'Iris-virginica'],
       ['6.5', '3.0', '5.5', '1.8', 'Iris-virginica'],
       ['7.7', '3.8', '6.7', '2.2', 'Iris-virginica'],
       ['7.7', '2.6', '6.9', '2.3', 'Iris-virginica'],
       ['6.0', '2.2', '5.0', '1.5', 'Iris-virginica'],
       ['6.9', '3.2', '5.7', '2.3', 'Iris-virginica'],
       ['5.6', '2.8', '4.9', '2.0', 'Iris-virginica'],
       ['7.7', '2.8', '6.7', '2.0', 'Iris-virginica'],
       ['6.3', '2.7', '4.9', '1.8', 'Iris-virginica'],
       ['6.7', '3.3', '5.7', '2.1', 'Iris-virginica'],
       ['7.2', '3.2', '6.0', '1.8', 'Iris-virginica'],
       ['6.2', '2.8', '4.8', '1.8', 'Iris-virginica'],
       ['6.1', '3.0', '4.9', '1.8', 'Iris-virginica'],
       ['6.4', '2.8', '5.6', '2.1', 'Iris-virginica'],
       ['7.2', '3.0', '5.8', '1.6', 'Iris-virginica'],
       ['7.4', '2.8', '6.1', '1.9', 'Iris-virginica'],
       ['7.9', '3.8', '6.4', '2.0', 'Iris-virginica'],
       ['6.4', '2.8', '5.6', '2.2', 'Iris-virginica'],
       ['6.3', '2.8', '5.1', '1.5', 'Iris-virginica'],
       ['6.1', '2.6', '5.6', '1.4', 'Iris-virginica'],
       ['7.7', '3.0', '6.1', '2.3', 'Iris-virginica'],
       ['6.3', '3.4', '5.6', '2.4', 'Iris-virginica'],
       ['6.4', '3.1', '5.5', '1.8', 'Iris-virginica'],
       ['6.0', '3.0', '4.8', '1.8', 'Iris-virginica'],
       ['6.9', '3.1', '5.4', '2.1', 'Iris-virginica'],
       ['6.7', '3.1', '5.6', '2.4', 'Iris-virginica'],
       ['6.9', '3.1', '5.1', '2.3', 'Iris-virginica'],
       ['5.8', '2.7', '5.1', '1.9', 'Iris-virginica'],
       ['6.8', '3.2', '5.9', '2.3', 'Iris-virginica'],
       ['6.7', '3.3', '5.7', '2.5', 'Iris-virginica'],
       ['6.7', '3.0', '5.2', '2.3', 'Iris-virginica'],
       ['6.3', '2.5', '5.0', '1.9', 'Iris-virginica'],
       ['6.5', '3.0', '5.2', '2.0', 'Iris-virginica'],
       ['6.2', '3.4', '5.4', '2.3', 'Iris-virginica'],
       ['5.9', '3.0', '5.1', '1.8', 'Iris-virginica']], dtype='<U32')

outfile = r'.\iris.data'
pd.DataFrame(iris_data).to_csv(outfile)

iris_data = np.loadtxt(outfile, dtype=object, delimiter=',')
iris_data


array([['', '0', '1', '2', '3', '4'],
       ['0', '5.1', '3.5', '1.4', '0.2', 'Iris-setosa'],
       ['1', '4.9', '3.0', '1.4', '0.2', 'Iris-setosa'],
       ['2', '4.7', '3.2', '1.3', '0.2', 'Iris-setosa'],
       ['3', '4.6', '3.1', '1.5', '0.2', 'Iris-setosa'],
       ['4', '5.0', '3.6', '1.4', '0.2', 'Iris-setosa'],
       ['5', '5.4', '3.9', '1.7', '0.4', 'Iris-setosa'],
       ['6', '4.6', '3.4', '1.4', '0.3', 'Iris-setosa'],
       ['7', '5.0', '3.4', '1.5', '0.2', 'Iris-setosa'],
       ['8', '4.4', '2.9', '1.4', '0.2', 'Iris-setosa'],
       ['9', '4.9', '3.1', '1.5', '0.1', 'Iris-setosa'],
       ['10', '5.4', '3.7', '1.5', '0.2', 'Iris-setosa'],
       ['11', '4.8', '3.4', '1.6', '0.2', 'Iris-setosa'],
       ['12', '4.8', '3.0', '1.4', '0.1', 'Iris-setosa'],
       ['13', '4.3', '3.0', '1.1', '0.1', 'Iris-setosa'],
       ['14', '5.8', '4.0', '1.2', '0.2', 'Iris-setosa'],
       ['15', '5.7', '4.4', '1.5', '0.4', 'Iris-setosa'],
       ['16', '5.4', '3.9', '1.3', '0.4', 'Iris-setosa'],
       ['17', '5.1', '3.5', '1.4', '0.3', 'Iris-setosa'],
       ['18', '5.7', '3.8', '1.7', '0.3', 'Iris-setosa'],
       ['19', '5.1', '3.8', '1.5', '0.3', 'Iris-setosa'],
       ['20', '5.4', '3.4', '1.7', '0.2', 'Iris-setosa'],
       ['21', '5.1', '3.7', '1.5', '0.4', 'Iris-setosa'],
       ['22', '4.6', '3.6', '1.0', '0.2', 'Iris-setosa'],
       ['23', '5.1', '3.3', '1.7', '0.5', 'Iris-setosa'],
       ['24', '4.8', '3.4', '1.9', '0.2', 'Iris-setosa'],
       ['25', '5.0', '3.0', '1.6', '0.2', 'Iris-setosa'],
       ['26', '5.0', '3.4', '1.6', '0.4', 'Iris-setosa'],
       ['27', '5.2', '3.5', '1.5', '0.2', 'Iris-setosa'],
       ['28', '5.2', '3.4', '1.4', '0.2', 'Iris-setosa'],
       ['29', '4.7', '3.2', '1.6', '0.2', 'Iris-setosa'],
       ['30', '4.8', '3.1', '1.6', '0.2', 'Iris-setosa'],
       ['31', '5.4', '3.4', '1.5', '0.4', 'Iris-setosa'],
       ['32', '5.2', '4.1', '1.5', '0.1', 'Iris-setosa'],
       ['33', '5.5', '4.2', '1.4', '0.2', 'Iris-setosa'],
       ['34', '4.9', '3.1', '1.5', '0.2', 'Iris-setosa'],
       ['35', '5.0', '3.2', '1.2', '0.2', 'Iris-setosa'],
       ['36', '5.5', '3.5', '1.3', '0.2', 'Iris-setosa'],
       ['37', '4.9', '3.6', '1.4', '0.1', 'Iris-setosa'],
       ['38', '4.4', '3.0', '1.3', '0.2', 'Iris-setosa'],
       ['39', '5.1', '3.4', '1.5', '0.2', 'Iris-setosa'],
       ['40', '5.0', '3.5', '1.3', '0.3', 'Iris-setosa'],
       ['41', '4.5', '2.3', '1.3', '0.3', 'Iris-setosa'],
       ['42', '4.4', '3.2', '1.3', '0.2', 'Iris-setosa'],
       ['43', '5.0', '3.5', '1.6', '0.6', 'Iris-setosa'],
       ['44', '5.1', '3.8', '1.9', '0.4', 'Iris-setosa'],
       ['45', '4.8', '3.0', '1.4', '0.3', 'Iris-setosa'],
       ['46', '5.1', '3.8', '1.6', '0.2', 'Iris-setosa'],
       ['47', '4.6', '3.2', '1.4', '0.2', 'Iris-setosa'],
       ['48', '5.3', '3.7', '1.5', '0.2', 'Iris-setosa'],
       ['49', '5.0', '3.3', '1.4', '0.2', 'Iris-setosa'],
       ['50', '7.0', '3.2', '4.7', '1.4', 'Iris-versicolor'],
       ['51', '6.4', '3.2', '4.5', '1.5', 'Iris-versicolor'],
       ['52', '6.9', '3.1', '4.9', '1.5', 'Iris-versicolor'],
       ['53', '5.5', '2.3', '4.0', '1.3', 'Iris-versicolor'],
       ['54', '6.5', '2.8', '4.6', '1.5', 'Iris-versicolor'],
       ['55', '5.7', '2.8', '4.5', '1.3', 'Iris-versicolor'],
       ['56', '6.3', '3.3', '4.7', '1.6', 'Iris-versicolor'],
       ['57', '4.9', '2.4', '3.3', '1.0', 'Iris-versicolor'],
       ['58', '6.6', '2.9', '4.6', '1.3', 'Iris-versicolor'],
       ['59', '5.2', '2.7', '3.9', '1.4', 'Iris-versicolor'],
       ['60', '5.0', '2.0', '3.5', '1.0', 'Iris-versicolor'],
       ['61', '5.9', '3.0', '4.2', '1.5', 'Iris-versicolor'],
       ['62', '6.0', '2.2', '4.0', '1.0', 'Iris-versicolor'],
       ['63', '6.1', '2.9', '4.7', '1.4', 'Iris-versicolor'],
       ['64', '5.6', '2.9', '3.6', '1.3', 'Iris-versicolor'],
       ['65', '6.7', '3.1', '4.4', '1.4', 'Iris-versicolor'],
       ['66', '5.6', '3.0', '4.5', '1.5', 'Iris-versicolor'],
       ['67', '5.8', '2.7', '4.1', '1.0', 'Iris-versicolor'],
       ['68', '6.2', '2.2', '4.5', '1.5', 'Iris-versicolor'],
       ['69', '5.6', '2.5', '3.9', '1.1', 'Iris-versicolor'],
       ['70', '5.9', '3.2', '4.8', '1.8', 'Iris-versicolor'],
       ['71', '6.1', '2.8', '4.0', '1.3', 'Iris-versicolor'],
       ['72', '6.3', '2.5', '4.9', '1.5', 'Iris-versicolor'],
       ['73', '6.1', '2.8', '4.7', '1.2', 'Iris-versicolor'],
       ['74', '6.4', '2.9', '4.3', '1.3', 'Iris-versicolor'],
       ['75', '6.6', '3.0', '4.4', '1.4', 'Iris-versicolor'],
       ['76', '6.8', '2.8', '4.8', '1.4', 'Iris-versicolor'],
       ['77', '6.7', '3.0', '5.0', '1.7', 'Iris-versicolor'],
       ['78', '6.0', '2.9', '4.5', '1.5', 'Iris-versicolor'],
       ['79', '5.7', '2.6', '3.5', '1.0', 'Iris-versicolor'],
       ['80', '5.5', '2.4', '3.8', '1.1', 'Iris-versicolor'],
       ['81', '5.5', '2.4', '3.7', '1.0', 'Iris-versicolor'],
       ['82', '5.8', '2.7', '3.9', '1.2', 'Iris-versicolor'],
       ['83', '6.0', '2.7', '5.1', '1.6', 'Iris-versicolor'],
       ['84', '5.4', '3.0', '4.5', '1.5', 'Iris-versicolor'],
       ['85', '6.0', '3.4', '4.5', '1.6', 'Iris-versicolor'],
       ['86', '6.7', '3.1', '4.7', '1.5', 'Iris-versicolor'],
       ['87', '6.3', '2.3', '4.4', '1.3', 'Iris-versicolor'],
       ['88', '5.6', '3.0', '4.1', '1.3', 'Iris-versicolor'],
       ['89', '5.5', '2.5', '4.0', '1.3', 'Iris-versicolor'],
       ['90', '5.5', '2.6', '4.4', '1.2', 'Iris-versicolor'],
       ['91', '6.1', '3.0', '4.6', '1.4', 'Iris-versicolor'],
       ['92', '5.8', '2.6', '4.0', '1.2', 'Iris-versicolor'],
       ['93', '5.0', '2.3', '3.3', '1.0', 'Iris-versicolor'],
       ['94', '5.6', '2.7', '4.2', '1.3', 'Iris-versicolor'],
       ['95', '5.7', '3.0', '4.2', '1.2', 'Iris-versicolor'],
       ['96', '5.7', '2.9', '4.2', '1.3', 'Iris-versicolor'],
       ['97', '6.2', '2.9', '4.3', '1.3', 'Iris-versicolor'],
       ['98', '5.1', '2.5', '3.0', '1.1', 'Iris-versicolor'],
       ['99', '5.7', '2.8', '4.1', '1.3', 'Iris-versicolor'],
       ['100', '6.3', '3.3', '6.0', '2.5', 'Iris-virginica'],
       ['101', '5.8', '2.7', '5.1', '1.9', 'Iris-virginica'],
       ['102', '7.1', '3.0', '5.9', '2.1', 'Iris-virginica'],
       ['103', '6.3', '2.9', '5.6', '1.8', 'Iris-virginica'],
       ['104', '6.5', '3.0', '5.8', '2.2', 'Iris-virginica'],
       ['105', '7.6', '3.0', '6.6', '2.1', 'Iris-virginica'],
       ['106', '4.9', '2.5', '4.5', '1.7', 'Iris-virginica'],
       ['107', '7.3', '2.9', '6.3', '1.8', 'Iris-virginica'],
       ['108', '6.7', '2.5', '5.8', '1.8', 'Iris-virginica'],
       ['109', '7.2', '3.6', '6.1', '2.5', 'Iris-virginica'],
       ['110', '6.5', '3.2', '5.1', '2.0', 'Iris-virginica'],
       ['111', '6.4', '2.7', '5.3', '1.9', 'Iris-virginica'],
       ['112', '6.8', '3.0', '5.5', '2.1', 'Iris-virginica'],
       ['113', '5.7', '2.5', '5.0', '2.0', 'Iris-virginica'],
       ['114', '5.8', '2.8', '5.1', '2.4', 'Iris-virginica'],
       ['115', '6.4', '3.2', '5.3', '2.3', 'Iris-virginica'],
       ['116', '6.5', '3.0', '5.5', '1.8', 'Iris-virginica'],
       ['117', '7.7', '3.8', '6.7', '2.2', 'Iris-virginica'],
       ['118', '7.7', '2.6', '6.9', '2.3', 'Iris-virginica'],
       ['119', '6.0', '2.2', '5.0', '1.5', 'Iris-virginica'],
       ['120', '6.9', '3.2', '5.7', '2.3', 'Iris-virginica'],
       ['121', '5.6', '2.8', '4.9', '2.0', 'Iris-virginica'],
       ['122', '7.7', '2.8', '6.7', '2.0', 'Iris-virginica'],
       ['123', '6.3', '2.7', '4.9', '1.8', 'Iris-virginica'],
       ['124', '6.7', '3.3', '5.7', '2.1', 'Iris-virginica'],
       ['125', '7.2', '3.2', '6.0', '1.8', 'Iris-virginica'],
       ['126', '6.2', '2.8', '4.8', '1.8', 'Iris-virginica'],
       ['127', '6.1', '3.0', '4.9', '1.8', 'Iris-virginica'],
       ['128', '6.4', '2.8', '5.6', '2.1', 'Iris-virginica'],
       ['129', '7.2', '3.0', '5.8', '1.6', 'Iris-virginica'],
       ['130', '7.4', '2.8', '6.1', '1.9', 'Iris-virginica'],
       ['131', '7.9', '3.8', '6.4', '2.0', 'Iris-virginica'],
       ['132', '6.4', '2.8', '5.6', '2.2', 'Iris-virginica'],
       ['133', '6.3', '2.8', '5.1', '1.5', 'Iris-virginica'],
       ['134', '6.1', '2.6', '5.6', '1.4', 'Iris-virginica'],
       ['135', '7.7', '3.0', '6.1', '2.3', 'Iris-virginica'],
       ['136', '6.3', '3.4', '5.6', '2.4', 'Iris-virginica'],
       ['137', '6.4', '3.1', '5.5', '1.8', 'Iris-virginica'],
       ['138', '6.0', '3.0', '4.8', '1.8', 'Iris-virginica'],
       ['139', '6.9', '3.1', '5.4', '2.1', 'Iris-virginica'],
       ['140', '6.7', '3.1', '5.6', '2.4', 'Iris-virginica'],
       ['141', '6.9', '3.1', '5.1', '2.3', 'Iris-virginica'],
       ['142', '5.8', '2.7', '5.1', '1.9', 'Iris-virginica'],
       ['143', '6.8', '3.2', '5.9', '2.3', 'Iris-virginica'],
       ['144', '6.7', '3.3', '5.7', '2.5', 'Iris-virginica'],
       ['145', '6.7', '3.0', '5.2', '2.3', 'Iris-virginica'],
       ['146', '6.3', '2.5', '5.0', '1.9', 'Iris-virginica'],
       ['147', '6.5', '3.0', '5.2', '2.0', 'Iris-virginica'],
       ['148', '6.2', '3.4', '5.4', '2.3', 'Iris-virginica'],
       ['149', '5.9', '3.0', '5.1', '1.8', 'Iris-virginica']],
      dtype=object)

2. 求出鸢尾属植物萼片长度的平均值、中位数和标准差（第1列，sepallength）

【知识点：统计相关】

  • 如何计算numpy数组的均值，中位数，标准差？
sepalLength = np.loadtxt(outfile, dtype = float, delimiter = ',', skiprows=1, usecols = [1])
print(sepalLength)

[5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.  5.  5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.
 5.5 4.9 4.4 5.1 5.  4.5 4.4 5.  5.1 4.8 5.1 4.6 5.3 5.  7.  6.4 6.9 5.5
 6.5 5.7 6.3 4.9 6.6 5.2 5.  5.9 6.  6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
 6.3 6.1 6.4 6.6 6.8 6.7 6.  5.7 5.5 5.5 5.8 6.  5.4 6.  6.7 6.3 5.6 5.5
 5.5 6.1 5.8 5.  5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.  6.9 5.6 7.7 6.3 6.7 7.2
 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.  6.9 6.7 6.9 5.8 6.8
 6.7 6.7 6.3 6.5 6.2 5.9]

print(np.mean(sepalLength))
# 5.843333333333334
print(np.median(sepalLength))
# 5.8
print(np.std(sepalLength))
# 0.8253012917851409

5.843333333333334
5.8
0.8253012917851409

3.创建一种标准化形式的鸢尾属植物萼片长度，其值正好介于0和1之间，这样最小值为0，最大值为1（第1列，sepallength）。

【知识点：统计相关】

  • 如何标准化数组？
amax = np.amax(sepalLength)
amin  = np.min(sepalLength)
x = (sepalLength - amin)/(amax - amin)
print(x[0:10])

[0.22222222 0.16666667 0.11111111 0.08333333 0.19444444 0.30555556
 0.08333333 0.19444444 0.02777778 0.16666667]

# 方法二
x = (sepalLength - amin)/np.ptp(sepalLength)
print(x[0:10])

[0.22222222 0.16666667 0.11111111 0.08333333 0.19444444 0.30555556
 0.08333333 0.19444444 0.02777778 0.16666667]

4. 找到鸢尾属植物萼片长度的第5和第95百分位数（第1列，sepallength）。

【知识点：统计相关】

  • 如何找到numpy数组的百分位数？
x = np.percentile(sepalLength, [5, 95])
print(x)

[4.6   7.255]

5. 把iris_data数据集中的20个随机位置修改为np.nan值。

【知识点：随机抽样】

  • 如何在数组中的随机位置修改值？
import numpy as np
iris_data = np.loadtxt(outfile, dtype = object, delimiter=',', skiprows= 1)
iris_data = iris_data [:, 1:]
i, j = iris_data.shape
print (i, j)

150 5

# 方法1
np.random.seed(20200621)
iris_data[np.random.randint(i, size= 20), np.random.randint(j, size = 20)] = np.nan
print(iris_data[0:10])

[['5.1' '3.5' '1.4' '0.2' 'Iris-setosa']
 ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa']
 ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa']
 ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa']
 ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa']
 ['5.4' nan '1.7' '0.4' 'Iris-setosa']
 ['4.6' '3.4' '1.4' '0.3' 'Iris-setosa']
 ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa']
 ['4.4' '2.9' '1.4' '0.2' nan]
 ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa']]

# 方法2
np.random.seed(20200620)
# 参数意思分别 是从a 中以概率P，随机选择3个, p没有指定的时候相当于是一致的分布
a1 = np.random.choice(a=5, size=3, replace=False, p=None)
iris_data[np.random.choice(i, size=20), np.random.choice(j, size=20)] = np.nan
print(iris_data[0:10])

[['5.1' '3.5' nan '0.2' 'Iris-setosa']
 ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa']
 ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa']
 ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa']
 ['5.0' '3.6' '1.4' nan 'Iris-setosa']
 ['5.4' nan '1.7' '0.4' 'Iris-setosa']
 ['4.6' '3.4' '1.4' '0.3' 'Iris-setosa']
 ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa']
 ['4.4' '2.9' '1.4' '0.2' nan]
 ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa']]

6. 在iris_data的sepallength中查找缺失值的个数和位置（第1列）。

【知识点：逻辑函数、搜索】

  • 如何在numpy数组中找到缺失值的位置？
import numpy as np
outfile = r'.\iris.data'
iris_data = np.loadtxt(outfile, dtype=float, delimiter=',', skiprows=1, usecols=[0, 1, 2,
3])
i, j = iris_data.shape
np.random.seed(20200621)
iris_data[np.random.randint(i, size=20), np.random.randint(j, size=20)] = np.nan
sepallength = iris_data[:, 0]
sepallength
a = np.isnan(iris_data[:, 0])
print(sum(a)) # 6
print(np.where(a))
# (array([ 26, 44, 55, 63, 90, 115], dtype=int64),)

6
(array([ 26,  44,  55,  63,  90, 115], dtype=int64),)

7. 筛选具有 sepallength（第1列）< 5.0 并且 petallength（第3列）> 1.5 的 iris_data行。

【知识点：搜索】

  • 如何根据两个或多个条件筛选numpy数组？
import numpy as np
outfile = r'.\iris.data'
iris_data = np.loadtxt(outfile, dtype='float', delimiter=',', skiprows=1, usecols=[1, 2, 3, 4])
sepallength = iris_data[:, 0]
petallength = iris_data[:, 2]
index = np.where(np.logical_and(petallength > 1.5, sepallength < 5.0))
print(iris_data[index])

[[4.8 3.4 1.6 0.2]
 [4.8 3.4 1.9 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [4.9 2.4 3.3 1. ]
 [4.9 2.5 4.5 1.7]]

选择没有任何 nan 值的 iris_data行。

【知识点：逻辑函数、搜索】

  • 如何从numpy数组中删除包含缺失值的行？
import numpy as np
outfil = r'./iris.data'
iris_data = np.loadtxt(outfile, dtype=float, delimiter=',', skiprows=1, usecols=[1, 2, 3, 4])
i, j = iris_data.shape
np.random.seed(20200621)
iris_data[np.random.randint(i, size= 20), np.random.randint(j, size = 20)] = np.nan
b = np.isnan(iris_data)
x = iris_data[np.sum(b, axis=1) == 0]
print(x[0:10])

[[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]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]]

9. 计算 iris_data 中sepalLength（第1列）和petalLength（第3列）之间的相关系数。

【知识点：统计相关】

  • 如何计算numpy数组两列之间的相关系数？
iris_data = np.loadtxt(outfile, dtype = 'float', delimiter = ',', skiprows = 1, usecols = [1, 2, 3, 4])
sepalLength = iris_data[:, 0]
petalLength = iris_data[:, 2]
x = np.corrcoef(sepalLength,petalLength )
x

array([[1.        , 0.87175378],
       [0.87175378, 1.        ]])

10. 找出iris_data是否有任何缺失值。

  1. 找出iris_data是否有任何缺失值。
  • 如何查找给定数组是否具有空值？
x = np.isnan(iris_data)
x
print(np.any(x)) # False

False

11. 在numpy数组中将所有出现的nan替换为0。

【知识点：逻辑函数】

  • 如何在numpy数组中用0替换所有缺失值？
import numpy as np
outfile = r'.\iris.data'
iris_data = np.loadtxt(outfile, dtype=float, delimiter=',', skiprows=1, usecols=[1, 2, 3,
4])
i, j = iris_data.shape
np.random.seed(20200621)
iris_data[np.random.randint(i, size=20), np.random.randint(j, size=20)] = np.nan
iris_data[np.isnan(iris_data)] = 0
print(iris_data[0:10])

[[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]
 [5.4 0.  1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 0.  0.2]
 [4.9 3.1 1.5 0.1]]

12. 找出鸢尾属植物物种中的唯一值和唯一值出现的数量。

【知识点：数组操作】

  • 如何在numpy数组中查找唯一值的计数？
import numpy as np
outfile = r'.\iris.data'
iris_data = np.loadtxt(outfile, dtype=object, delimiter=',', skiprows=1, usecols=[5])
x = np.unique(iris_data, return_counts= True)
print(x)
# (array(['Iris‐setosa', 'Iris‐versicolor', 'Iris‐virginica'], dtype=object), array([50,
# 50, 50], dtype=int64))

(array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object), array([50, 50, 50], dtype=int64))

13. 将 iris_data 的花瓣长度（第3列）以形成分类变量的形式显示。定义：Less than 3 -->‘small’；3-5 --> ‘medium’；’>=5 --> ‘large’。

【知识点：统计相关】

  • 如何将数字转换为分类（文本）数组？
import numpy as np
outfile = r'.\iris.data'
iris_data = np.loadtxt(outfile, dtype=float, delimiter=',', skiprows=1, usecols=[1, 2, 3,
4])
petal_length_bin = np.digitize(iris_data[:, 3], [0, 3, 5, 10])
label_map = {
    
    1: 'small', 2: 'medium', 3: 'large', 4: np.nan}
petal_length_cat = [label_map[x] for x in petal_length_bin]
print(petal_length_cat[0:10])
# ['small', 'small', 'small', 'small', 'small', 'small', 'small', 'small', 'small',
#'small']

['small', 'small', 'small', 'small', 'small', 'small', 'small', 'small', 'small', 'small']

14. 在 iris_data 中创建一个新列，其中 volume 是 (pi x petallength x sepallength ^ 2）/ 3 。

【知识点：数组操作】

  • 如何从numpy数组的现有列创建新列？
import numpy as np
outfile = r'.\iris.data'
iris_data = np.loadtxt(outfile, dtype=object, delimiter=',', skiprows=1)
sepalLength = iris_data[:, 1].astype(float)
petalLength = iris_data[:, 3].astype(float)
volume = (np.pi * petalLength * sepalLength ** 2) / 3
volume = volume[:, np.newaxis]
# 这样改变维度的作用往往是将一维的数据转变成一个矩阵，与代码后面的权重矩阵进行相乘， 
# 否则单单的数据是不能呢这样相乘的哦。这样改变维度的作用往往是将一维的数据转变成一个矩阵，
# 与代码后面的权重矩阵进行相乘， 否则单单的数据是不能呢这样相乘的哦。
iris_data = np.concatenate([iris_data, volume], axis=1)
print(iris_data[0:10])
# [['5.1' '3.5' '1.4' '0.2' 'Iris‐setosa' 38.13265162927291]
# ['4.9' '3.0' '1.4' '0.2' 'Iris‐setosa' 35.200498485922445]
# ['4.7' '3.2' '1.3' '0.2' 'Iris‐setosa' 30.0723720777127]
# ['4.6' '3.1' '1.5' '0.2' 'Iris‐setosa' 33.238050274980004]
# ['5.0' '3.6' '1.4' '0.2' 'Iris‐setosa' 36.65191429188092]

[['0' '5.1' '3.5' '1.4' '0.2' 'Iris-setosa' 38.13265162927291]
 ['1' '4.9' '3.0' '1.4' '0.2' 'Iris-setosa' 35.200498485922445]
 ['2' '4.7' '3.2' '1.3' '0.2' 'Iris-setosa' 30.0723720777127]
 ['3' '4.6' '3.1' '1.5' '0.2' 'Iris-setosa' 33.238050274980004]
 ['4' '5.0' '3.6' '1.4' '0.2' 'Iris-setosa' 36.65191429188092]
 ['5' '5.4' '3.9' '1.7' '0.4' 'Iris-setosa' 51.911677007917746]
 ['6' '4.6' '3.4' '1.4' '0.3' 'Iris-setosa' 31.022180256648003]
 ['7' '5.0' '3.4' '1.5' '0.2' 'Iris-setosa' 39.269908169872416]
 ['8' '4.4' '2.9' '1.4' '0.2' 'Iris-setosa' 28.38324242763259]
 ['9' '4.9' '3.1' '1.5' '0.1' 'Iris-setosa' 37.714819806345474]]

15. 随机抽鸢尾属植物的种类，使得Iris-setosa的数量是Iris-versicolor和Iris-virginica数量的两倍.

【知识点：随机抽样】

  • 如何在numpy中进行概率抽样？
import numpy as np
species = np.array(['Iris‐setosa', 'Iris‐versicolor', 'Iris‐virginica'])
species_out = np.random.choice(species,10000,  p = [0.5, 0.25, 0.25])
print(np.unique(species_out, return_counts = True))

(array(['Iris‐setosa', 'Iris‐versicolor', 'Iris‐virginica'], dtype='<U15'), array([5057, 2445, 2498], dtype=int64))

16. 根据 sepallength 列对数据集进行排序。

【知识点：排序】

  • 如何按列对2D数组进行排序？
iris_data = np.loadtxt(outfile, dtype = object, delimiter = ',', skiprows = 1)
sepalLength = iris_data[:, 1]
index = np.argsort(sepalLength)
print(iris_data[index][0:10])

[['13' '4.3' '3.0' '1.1' '0.1' 'Iris-setosa']
 ['42' '4.4' '3.2' '1.3' '0.2' 'Iris-setosa']
 ['38' '4.4' '3.0' '1.3' '0.2' 'Iris-setosa']
 ['8' '4.4' '2.9' '1.4' '0.2' 'Iris-setosa']
 ['41' '4.5' '2.3' '1.3' '0.3' 'Iris-setosa']
 ['22' '4.6' '3.6' '1.0' '0.2' 'Iris-setosa']
 ['3' '4.6' '3.1' '1.5' '0.2' 'Iris-setosa']
 ['6' '4.6' '3.4' '1.4' '0.3' 'Iris-setosa']
 ['47' '4.6' '3.2' '1.4' '0.2' 'Iris-setosa']
 ['2' '4.7' '3.2' '1.3' '0.2' 'Iris-setosa']]

17. 在鸢尾属植物数据集中找到最常见的花瓣长度值（第3列）。

【知识点：数组操作】

  • 如何在numpy数组中找出出现次数最多的值？
iris_data = np.loadtxt(outfile, dtype = object, delimiter = ',', skiprows = 1)
petalLength = iris_data[:, 3]
vals, counts = np.unique(petalLength, return_counts=True)
print(vals[np.argmax(counts)]) # 1.5 #取出counts中元素最大值所对应的索引，
print(np.amax(counts)) # 14

1.4
13

18. 在鸢尾花数据集的 petalwidth（第4列）中查找第一次出现的值大于1.0的位置。

【知识点：搜索】

  • 如何找到第一次出现大于给定值的位置？
iris_data = np.loadtxt(outfile, dtype=float, delimiter=',', skiprows=1, usecols=[1, 2, 3,
4])
petalWidth = iris_data[:,3]
index = np.where(petalWidth > 1.0)
print(index)
print(index[0][0]) # 50

(array([ 50,  51,  52,  53,  54,  55,  56,  58,  59,  61,  63,  64,  65,
        66,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,
        82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  94,  95,
        96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
       109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
       122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
       135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
       148, 149], dtype=int64),)
50

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