• tensorflow2.0学习笔记第一章第三节


    1.3鸢尾花数据读入

    # 从sklearn包datasets读入数据
    from sklearn import datasets
    from pandas import DataFrame
    import pandas as pd
    
    x_data = datasets.load_iris().data # 加载特征
    y_data = datasets.load_iris().target # 加载标签
    print('x_data from datasets:
    ',x_data)
    print('y_data from datasets:
    ',y_data)
    
    # columns为特征名称
    x_data = DataFrame(x_data,columns=['花萼长度','花萼宽度','花瓣长度','花瓣宽度'])
    pd.set_option('display.unicode.east_asian_width',True) #设置列名对其
    print('x_data add index: 
    ',x_data)
    x_data['类别'] = y_data # 新加一行标签为y_data
    
    print("x_data add a colunmn 
    ",x_data)
    x_data from datasets:
     [[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 3.9 1.7 0.4]
     [4.6 3.4 1.4 0.3]
     [5.  3.4 1.5 0.2]
     [4.4 2.9 1.4 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]
     [4.8 3.  1.4 0.1]
     [4.3 3.  1.1 0.1]
     [5.8 4.  1.2 0.2]
     [5.7 4.4 1.5 0.4]
     [5.4 3.9 1.3 0.4]
     [5.1 3.5 1.4 0.3]
     [5.7 3.8 1.7 0.3]
     [5.1 3.8 1.5 0.3]
     [5.4 3.4 1.7 0.2]
     [5.1 3.7 1.5 0.4]
     [4.6 3.6 1.  0.2]
     [5.1 3.3 1.7 0.5]
     [4.8 3.4 1.9 0.2]
     [5.  3.  1.6 0.2]
     [5.  3.4 1.6 0.4]
     [5.2 3.5 1.5 0.2]
     [5.2 3.4 1.4 0.2]
     [4.7 3.2 1.6 0.2]
     [4.8 3.1 1.6 0.2]
     [5.4 3.4 1.5 0.4]
     [5.2 4.1 1.5 0.1]
     [5.5 4.2 1.4 0.2]
     [4.9 3.1 1.5 0.2]
     [5.  3.2 1.2 0.2]
     [5.5 3.5 1.3 0.2]
     [4.9 3.6 1.4 0.1]
     [4.4 3.  1.3 0.2]
     [5.1 3.4 1.5 0.2]
     [5.  3.5 1.3 0.3]
     [4.5 2.3 1.3 0.3]
     [4.4 3.2 1.3 0.2]
     [5.  3.5 1.6 0.6]
     [5.1 3.8 1.9 0.4]
     [4.8 3.  1.4 0.3]
     [5.1 3.8 1.6 0.2]
     [4.6 3.2 1.4 0.2]
     [5.3 3.7 1.5 0.2]
     [5.  3.3 1.4 0.2]
     [7.  3.2 4.7 1.4]
     [6.4 3.2 4.5 1.5]
     [6.9 3.1 4.9 1.5]
     [5.5 2.3 4.  1.3]
     [6.5 2.8 4.6 1.5]
     [5.7 2.8 4.5 1.3]
     [6.3 3.3 4.7 1.6]
     [4.9 2.4 3.3 1. ]
     [6.6 2.9 4.6 1.3]
     [5.2 2.7 3.9 1.4]
     [5.  2.  3.5 1. ]
     [5.9 3.  4.2 1.5]
     [6.  2.2 4.  1. ]
     [6.1 2.9 4.7 1.4]
     [5.6 2.9 3.6 1.3]
     [6.7 3.1 4.4 1.4]
     [5.6 3.  4.5 1.5]
     [5.8 2.7 4.1 1. ]
     [6.2 2.2 4.5 1.5]
     [5.6 2.5 3.9 1.1]
     [5.9 3.2 4.8 1.8]
     [6.1 2.8 4.  1.3]
     [6.3 2.5 4.9 1.5]
     [6.1 2.8 4.7 1.2]
     [6.4 2.9 4.3 1.3]
     [6.6 3.  4.4 1.4]
     [6.8 2.8 4.8 1.4]
     [6.7 3.  5.  1.7]
     [6.  2.9 4.5 1.5]
     [5.7 2.6 3.5 1. ]
     [5.5 2.4 3.8 1.1]
     [5.5 2.4 3.7 1. ]
     [5.8 2.7 3.9 1.2]
     [6.  2.7 5.1 1.6]
     [5.4 3.  4.5 1.5]
     [6.  3.4 4.5 1.6]
     [6.7 3.1 4.7 1.5]
     [6.3 2.3 4.4 1.3]
     [5.6 3.  4.1 1.3]
     [5.5 2.5 4.  1.3]
     [5.5 2.6 4.4 1.2]
     [6.1 3.  4.6 1.4]
     [5.8 2.6 4.  1.2]
     [5.  2.3 3.3 1. ]
     [5.6 2.7 4.2 1.3]
     [5.7 3.  4.2 1.2]
     [5.7 2.9 4.2 1.3]
     [6.2 2.9 4.3 1.3]
     [5.1 2.5 3.  1.1]
     [5.7 2.8 4.1 1.3]
     [6.3 3.3 6.  2.5]
     [5.8 2.7 5.1 1.9]
     [7.1 3.  5.9 2.1]
     [6.3 2.9 5.6 1.8]
     [6.5 3.  5.8 2.2]
     [7.6 3.  6.6 2.1]
     [4.9 2.5 4.5 1.7]
     [7.3 2.9 6.3 1.8]
     [6.7 2.5 5.8 1.8]
     [7.2 3.6 6.1 2.5]
     [6.5 3.2 5.1 2. ]
     [6.4 2.7 5.3 1.9]
     [6.8 3.  5.5 2.1]
     [5.7 2.5 5.  2. ]
     [5.8 2.8 5.1 2.4]
     [6.4 3.2 5.3 2.3]
     [6.5 3.  5.5 1.8]
     [7.7 3.8 6.7 2.2]
     [7.7 2.6 6.9 2.3]
     [6.  2.2 5.  1.5]
     [6.9 3.2 5.7 2.3]
     [5.6 2.8 4.9 2. ]
     [7.7 2.8 6.7 2. ]
     [6.3 2.7 4.9 1.8]
     [6.7 3.3 5.7 2.1]
     [7.2 3.2 6.  1.8]
     [6.2 2.8 4.8 1.8]
     [6.1 3.  4.9 1.8]
     [6.4 2.8 5.6 2.1]
     [7.2 3.  5.8 1.6]
     [7.4 2.8 6.1 1.9]
     [7.9 3.8 6.4 2. ]
     [6.4 2.8 5.6 2.2]
     [6.3 2.8 5.1 1.5]
     [6.1 2.6 5.6 1.4]
     [7.7 3.  6.1 2.3]
     [6.3 3.4 5.6 2.4]
     [6.4 3.1 5.5 1.8]
     [6.  3.  4.8 1.8]
     [6.9 3.1 5.4 2.1]
     [6.7 3.1 5.6 2.4]
     [6.9 3.1 5.1 2.3]
     [5.8 2.7 5.1 1.9]
     [6.8 3.2 5.9 2.3]
     [6.7 3.3 5.7 2.5]
     [6.7 3.  5.2 2.3]
     [6.3 2.5 5.  1.9]
     [6.5 3.  5.2 2. ]
     [6.2 3.4 5.4 2.3]
     [5.9 3.  5.1 1.8]]
    y_data from datasets:
     [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]
    x_data add index: 
          花萼长度  花萼宽度  花瓣长度  花瓣宽度
    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
    ..        ...       ...       ...       ...
    145       6.7       3.0       5.2       2.3
    146       6.3       2.5       5.0       1.9
    147       6.5       3.0       5.2       2.0
    148       6.2       3.4       5.4       2.3
    149       5.9       3.0       5.1       1.8
    
    [150 rows x 4 columns]
    x_data add a colunmn 
          花萼长度  花萼宽度  花瓣长度  花瓣宽度  类别
    0         5.1       3.5       1.4       0.2     0
    1         4.9       3.0       1.4       0.2     0
    2         4.7       3.2       1.3       0.2     0
    3         4.6       3.1       1.5       0.2     0
    4         5.0       3.6       1.4       0.2     0
    ..        ...       ...       ...       ...   ...
    145       6.7       3.0       5.2       2.3     2
    146       6.3       2.5       5.0       1.9     2
    147       6.5       3.0       5.2       2.0     2
    148       6.2       3.4       5.4       2.3     2
    149       5.9       3.0       5.1       1.8     2
    
    [150 rows x 5 columns]
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  • 原文地址:https://www.cnblogs.com/wigginess/p/13048453.html
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