Chp4-4
2019 年 12 月 17 日
In [7]: #loadtxt import numpy as np x=np.loadtxt('C:\Python\Scripts\my_data\global-earthquakes.csv', delimiter=',')# 注意调用方法时的参数传递语法 print(type(x)) print(x.shape) print('\n') print(x[:2,:3]) #ndarray 的二维切取 x_int=np.loadtxt('global-earthquakes.csv',delimiter=',',dtype=int) print('\n') print(x_int[:2,:3]) <class 'numpy.ndarray'> (59209, 8) [[1.973e+03 1.000e+00 1.000e+00] [1.973e+03 1.000e+00 1.000e+00]] [[1973 1 1] [1973 1 1]] In [1]: #loadtxt import numpy as np x=np.loadtxt('C:\Python\Scripts\my_data\iris.csv', 1delimiter=',')# 注意 Iris 中除了数还有字符串 print(type(x)) print(x.shape) print('\n') print(x[:2,:3]) #ndarray 的二维切取 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-1-85eb4be19fe4> in <module>() 1 #loadtxt 2 import numpy as np ----> 3 x=np.loadtxt('C:\Python\Scripts\my_data\iris.csv', delimiter=',')# 注意 Iris 中除了数还有字符串 4 print(type(x)) 5 print(x.shape) c:\python\lib\site-packages\numpy\lib\npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding) 1099 # converting the data 1100 X = None -> 1101 for x in read_data(_loadtxt_chunksize): 1102 if X is None: 1103 X = np.array(x, dtype) c:\python\lib\site-packages\numpy\lib\npyio.py in read_data(chunk_size) 1026 1027 # Convert each value according to its column and store -> 1028 items = [conv(val) for (conv, val) in zip(converters, vals)] 1029 1030 # Then pack it according to the dtype's nesting c:\python\lib\site-packages\numpy\lib\npyio.py in <listcomp>(.0) 1026 1027 # Convert each value according to its column and store -> 1028 items = [conv(val) for (conv, val) in zip(converters, vals)] 1029 1030 # Then pack it according to the dtype's nesting c:\python\lib\site-packages\numpy\lib\npyio.py in floatconv(x) 744 if '0x' in x: 745 return float.fromhex(x) --> 746 return float(x) 747 748 typ = dtype.type ValueError: could not convert string to float: 'setosa' In [2]: #loadtxt import numpy as np x=np.loadtxt('C:\Python\Scripts\my_data\iris.csv',delimiter=',', dtype=str) # 强制把所有数据用字符串形式导入 print(type(x)) print(x.shape) print('\n') print(x[:2,:3]) #ndarray 的二维切取 <class 'numpy.ndarray'> (150, 5) [['5.1' '3.5' '1.4'] ['4.9' '3.0' '1.4']] In [3]: #read_csv import pandas as pd data=pd.read_csv('C:\Python\Scripts\my_data\iris.csv', header=None, names=['sepal_len','sepal_wid','petal_len', 'petal_wid','target']) print(type(data)) print('\n') print(data.head()) print('\n') <class 'pandas.core.frame.DataFrame'> sepal_len sepal_wid petal_len petal_wid 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 [4]: print(data.values[:2,:5]) # 二维数组访问与常规一样 print(type(data.values)) print('\n') x=data.values[:,:4] print(x[:2,:]) print(type(x)) print('\n') [[5.1 3.5 1.4 0.2 'setosa'] [4.9 3.0 1.4 0.2 'setosa']] <class 'numpy.ndarray'> [[5.1 3.5 1.4 0.2] [4.9 3.0 1.4 0.2]] <class 'numpy.ndarray'> In [9]: import urllib target_page='https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a1a' a2a=urllib.request.urlopen(target_page) from sklearn.datasets import load_svmlight_file x_train, y_train = load_svmlight_file(a2a) # 这里是 Python 风格的多重赋值 print (x_train.shape, y_train.shape) print(x_train[:1][:100]) #x_train 是个稀疏矩阵，返回的是矩阵中非零元素的位置 print(type(x_train)) print('\n') print(y_train[:10]) print(type(y_train)) (1605, 119) (1605,) (0, 2) 1.0 (0, 10) 1.0 (0, 13) 1.0 (0, 18) 1.0 (0, 38) 1.0 (0, 41) 1.0 (0, 54) 1.0 (0, 63) 1.0 (0, 66) 1.0 (0, 72) 1.0 (0, 74) 1.0 (0, 75) 1.0 (0, 79) 1.0 (0, 82) 1.0 <class 'scipy.sparse.csr.csr_matrix'> [-1. -1. -1. -1. -1. -1. -1. -1. -1. 1.] <class 'numpy.ndarray'> In [51]: # 文本信息结构化示例 from sklearn.datasets import fetch_20newsgroups my_news=fetch_20newsgroups(categories=['sci.med']) # 下载医学新闻数据集 print(type(my_news),'\n') print(twenty_sci_news.data[0],'\n') from sklearn.feature_extraction.text import CountVectorizer count_vect=CountVectorizer() word_count=count_vect.fit_transform(my_news.data) # 返回一个稀疏矩阵对象 print(type(word_count)) print(word_count.shape,'\n') print(word_count[0]) # 第一行上非零元素的坐标（元组表示）以及频次 word_list=count_vect.get_feature_names() for n in word_count[0].indices: print(word_list[n],'\t appears ', word_count[0,n], 'times') # 打印第一篇新闻的字频情况 <class 'sklearn.utils.Bunch'> From: nyeda@cnsvax.uwec.edu (David Nye) Subject: Re: Post Polio Syndrome Information Needed Please !!! Organization: University of Wisconsin Eau Claire Lines: 21 [reply to keith@actrix.gen.nz (Keith Stewart)] >My wife has become interested through an acquaintance in Post-Polio >Syndrome This apparently is not recognised in New Zealand and different >symptons ( eg chest complaints) are treated separately. Does anone have >any information on it It would help if you (and anyone else asking for medical information on some subject) could ask specific questions, as no one is likely to type in a textbook chapter covering all aspects of the subject. If you are looking for a comprehensive review, ask your local hospital librarian. Most are happy to help with a request of this sort. Briefly, this is a condition in which patients who have significant residual weakness from childhood polio notice progression of the weakness as they get older. One theory is that the remaining motor neurons have to work harder and so die sooner. David Nye (nyeda@cnsvax.uwec.edu). Midelfort Clinic, Eau Claire WI This is patently absurd; but whoever wishes to become a philosopher must learn not to be frightened by absurdities. -- Bertrand Russell <class 'scipy.sparse.csr.csr_matrix'> (594, 16257) (0, 12891) 1 (0, 2742) 1 (0, 1496) 1 (0, 3189) 1 (0, 6580) 1 (0, 2628) 1 (0, 8837) 1 (0, 10048) 1 (0, 11268) 1 (0, 16003) 1 (0, 15944) 1 (0, 3175) 1 (0, 1495) 1 (0, 11052) 1 (0, 15952) 1 (0, 3798) 1 (0, 9701) 1 (0, 13693) 1 (0, 4977) 1 (0, 13621) 1 (0, 7147) 1 (0, 16049) 1 (0, 10245) 1 (0, 9960) 1 (0, 12405) 1 : : (0, 14822) 6 (0, 12475) 1 (0, 564) 1 (0, 8990) 1 (0, 3757) 2 (0, 5470) 2 (0, 15998) 1 (0, 10599) 4 (0, 15299) 1 (0, 10745) 1 (0, 11417) 1 (0, 10178) 1 (0, 7896) 3 (0, 14389) 2 (0, 11477) 3 (0, 11548) 2 (0, 12157) 1 (0, 14117) 3 (0, 10497) 2 (0, 4661) 2 (0, 5508) 2 (0, 15454) 2 (0, 3842) 2 (0, 10498) 2 (0, 6585) 2 russell appears 1 times bertrand appears 1 times absurdities appears 1 times by appears 1 times frightened appears 1 times be appears 1 times learn appears 1 times must appears 1 times philosopher appears 1 times wishes appears 1 times whoever appears 1 times but appears 1 times absurd appears 1 times patently appears 1 times wi appears 1 times clinic appears 1 times midelfort appears 1 times sooner appears 1 times die appears 1 times so appears 1 times harder appears 1 times work appears 1 times neurons appears 1 times motor appears 1 times remaining appears 1 times that appears 1 times theory appears 1 times older appears 1 times get appears 1 times they appears 1 times progression appears 1 times notice appears 1 times childhood appears 1 times weakness appears 2 times residual appears 1 times significant appears 1 times who appears 1 times patients appears 1 times which appears 1 times condition appears 1 times briefly appears 1 times sort appears 1 times request appears 1 times with appears 1 times happy appears 1 times most appears 1 times librarian appears 1 times hospital appears 1 times local appears 1 times your appears 1 times review appears 1 times comprehensive appears 1 times looking appears 1 times the appears 3 times aspects appears 1 times all appears 1 times covering appears 1 times chapter appears 1 times textbook appears 1 times type appears 1 times likely appears 1 times one appears 2 times no appears 1 times as appears 2 times questions appears 1 times specific appears 1 times ask appears 2 times could appears 1 times some appears 1 times medical appears 1 times for appears 2 times asking appears 1 times else appears 1 times anyone appears 1 times you appears 2 times if appears 2 times help appears 2 times would appears 1 times it appears 2 times on appears 2 times any appears 1 times have appears 3 times anone appears 1 times does appears 1 times separately appears 1 times treated appears 1 times are appears 3 times complaints appears 1 times chest appears 1 times eg appears 1 times symptons appears 1 times different appears 1 times and appears 3 times zealand appears 1 times new appears 1 times recognised appears 1 times not appears 2 times is appears 5 times apparently appears 1 times this appears 4 times in appears 4 times acquaintance appears 1 times an appears 1 times through appears 1 times interested appears 1 times become appears 2 times has appears 1 times wife appears 1 times my appears 1 times stewart appears 1 times nz appears 1 times gen appears 1 times actrix appears 1 times keith appears 2 times to appears 6 times reply appears 1 times 21 appears 1 times lines appears 1 times claire appears 2 times eau appears 2 times wisconsin appears 1 times of appears 4 times university appears 1 times organization appears 1 times please appears 1 times needed appears 1 times information appears 3 times syndrome appears 2 times polio appears 3 times post appears 2 times re appears 1 times subject appears 3 times nye appears 2 times david appears 2 times edu appears 2 times uwec appears 2 times cnsvax appears 2 times nyeda appears 2 times from appears 2 times In [1]: import pandas as pd my_chunk=pd.read_csv('C:\Python\Scripts\my_data\iris.csv',header=None, names=['c1','c2','c3','c4','c5'], chunksize=20) print(type(my_chunk)) # 注意规定 chunksize 后，返回的数据类型不再是 DataFrame # 而是一个可迭代的 TextFileReader 对象 # 它保存了若干个 chunk 位置，但只有当被迭代器指到时，才会真正把对应的数据块读入到内存 for n,chunk in enumerate(my_chunk): # 枚举函数既返回元素序号，又返回每个元素 print(chunk.shape) if n <= 2: print(chunk) # 每一个 chunk 又是一个 DataFrame print('\n') if n <= 2: print(my_chunk.get_chunk(1),'\n') #get_chunk 函数是从当前位置起获取指定大小的数据块，返回也是 dataframe #get_chunk 会改变迭代器指针，体会一下哦 <class 'pandas.io.parsers.TextFileReader'> (20, 5) c1 c2 c3 c4 c5 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 5 5.4 3.9 1.7 0.4 setosa 6 4.6 3.4 1.4 0.3 setosa 7 5.0 3.4 1.5 0.2 setosa 8 4.4 2.9 1.4 0.2 setosa 9 4.9 3.1 1.5 0.1 setosa 10 5.4 3.7 1.5 0.2 setosa 11 4.8 3.4 1.6 0.2 setosa 12 4.8 3.0 1.4 0.1 setosa 13 4.3 3.0 1.1 0.1 setosa 14 5.8 4.0 1.2 0.2 setosa 15 5.7 4.4 1.5 0.4 setosa 16 5.4 3.9 1.3 0.4 setosa 17 5.1 3.5 1.4 0.3 setosa 18 5.7 3.8 1.7 0.3 setosa 19 5.1 3.8 1.5 0.3 setosa c1 c2 c3 c4 c5 20 5.4 3.4 1.7 0.2 setosa (20, 5) c1 c2 c3 c4 c5 21 5.1 3.7 1.5 0.4 setosa 22 4.6 3.6 1.0 0.2 setosa 23 5.1 3.3 1.7 0.5 setosa 24 4.8 3.4 1.9 0.2 setosa 25 5.0 3.0 1.6 0.2 setosa 26 5.0 3.4 1.6 0.4 setosa 27 5.2 3.5 1.5 0.2 setosa 28 5.2 3.4 1.4 0.2 setosa 29 4.7 3.2 1.6 0.2 setosa 30 4.8 3.1 1.6 0.2 setosa 31 5.4 3.4 1.5 0.4 setosa 32 5.2 4.1 1.5 0.1 setosa 33 5.5 4.2 1.4 0.2 setosa 34 4.9 3.1 1.5 0.1 setosa 35 5.0 3.2 1.2 0.2 setosa 36 5.5 3.5 1.3 0.2 setosa 37 4.9 3.1 1.5 0.1 setosa 38 4.4 3.0 1.3 0.2 setosa 39 5.1 3.4 1.5 0.2 setosa 40 5.0 3.5 1.3 0.3 setosa c1 c2 c3 c4 c5 41 4.5 2.3 1.3 0.3 setosa (20, 5) c1 c2 c3 c4 c5 42 4.4 3.2 1.3 0.2 setosa 43 5.0 3.5 1.6 0.6 setosa 44 5.1 3.8 1.9 0.4 setosa 45 4.8 3.0 1.4 0.3 setosa 46 5.1 3.8 1.6 0.2 setosa 47 4.6 3.2 1.4 0.2 setosa 48 5.3 3.7 1.5 0.2 setosa 49 5.0 3.3 1.4 0.2 setosa 50 7.0 3.2 4.7 1.4 versicolor 51 6.4 3.2 4.5 1.5 versicolor 52 6.9 3.1 4.9 1.5 versicolor 53 5.5 2.3 4.0 1.3 versicolor 54 6.5 2.8 4.6 1.5 versicolor 55 5.7 2.8 4.5 1.3 versicolor 56 6.3 3.3 4.7 1.6 versicolor 57 4.9 2.4 3.3 1.0 versicolor 58 6.6 2.9 4.6 1.3 versicolor 59 5.2 2.7 3.9 1.4 versicolor 60 5.0 2.0 3.5 1.0 versicolor 61 5.9 3.0 4.2 1.5 versicolor c1 c2 c3 c4 c5 62 6.0 2.2 4.0 1.0 versicolor (20, 5) (20, 5) (20, 5) (20, 5) (7, 5) In [40]: import numpy as np import pandas as pd import csv with open('C:\Python\Scripts\my_data\iris.csv','r') as my_data_stream: #with 命令保证后面缩进的命令块执行完毕后文件会关闭 # 用 open 命令以只读方式打开文件，创建的文件对象保存在 my_data_stream 中 # 用 csv.reader 对给定的文件对象读取，一次读取文件中的一行，作为列表对象 # 这里的 reader 返回的是迭代器对象 my_reader=csv.reader(my_data_stream,dialect='excel') for n,row in enumerate(my_reader): if n<=5: print(row) # 可见每个 row 都是一个列表 print(type(row),'\n') ['5.1', '3.5', '1.4', '0.2', 'setosa'] <class 'list'> ['4.9', '3.0', '1.4', '0.2', 'setosa'] <class 'list'> ['4.7', '3.2', '1.3', '0.2', 'setosa'] <class 'list'> ['4.6', '3.1', '1.5', '0.2', 'setosa'] <class 'list'> ['5.0', '3.6', '1.4', '0.2', 'setosa'] <class 'list'> ['5.4', '3.9', '1.7', '0.4', 'setosa'] <class 'list'> In [31]: import numpy as np import pandas as pd import csv def batch_read(filename, batch=5): # 注意自定义函数的语法 with open(filename,'r') as data_stream: #with 命令保证后面缩进的命令块执行完毕后文件会关闭 # 用 open 命令以只读方式打开文件，创建的文件对象保存在 data_stream 中 batch_output=list() # 初始化 batch_output 列表 # 用 csv.reader 对给定的文件对象读取，一次读取文件中的一行，作为列表的一个元素 # 这里的 reader 返回的是迭代器对象 for n,row in enumerate(csv.reader(data_stream,dialect='excel')): # 枚举函数返回包含行号和行内容的元组 #for 循环遍历 reader 返回的列表 if n>0 and n%batch==0: #yield 属生成器，类似于 return，但可迭代复用, # 下句把 batch_output 列表转换为 ndarray 返回 yield(np.array(batch_output)) batch_output=list() # 重置 batch_output batch_output.append(row) # 更新 batch_output yield(np.array(batch_output)) # 返回最后的几行 for batch_input in batch_read('C:\Python\Scripts\my_data\iris.csv',batch=7): # 注意允许修改默认参数 print(batch_input) print('\n') [['5.1' '3.5' '1.4' '0.2' 'setosa'] ['4.9' '3.0' '1.4' '0.2' 'setosa'] ['4.7' '3.2' '1.3' '0.2' 'setosa'] ['4.6' '3.1' '1.5' '0.2' 'setosa'] ['5.0' '3.6' '1.4' '0.2' 'setosa'] ['5.4' '3.9' '1.7' '0.4' 'setosa'] ['4.6' '3.4' '1.4' '0.3' 'setosa']] [['5.0' '3.4' '1.5' '0.2' 'setosa'] ['4.4' '2.9' '1.4' '0.2' 'setosa'] ['4.9' '3.1' '1.5' '0.1' 'setosa'] ['5.4' '3.7' '1.5' '0.2' 'setosa'] ['4.8' '3.4' '1.6' '0.2' 'setosa'] ['4.8' '3.0' '1.4' '0.1' 'setosa'] ['4.3' '3.0' '1.1' '0.1' 'setosa']] [['5.8' '4.0' '1.2' '0.2' 'setosa'] ['5.7' '4.4' '1.5' '0.4' 'setosa'] ['5.4' '3.9' '1.3' '0.4' 'setosa'] ['5.1' '3.5' '1.4' '0.3' 'setosa'] ['5.7' '3.8' '1.7' '0.3' 'setosa'] ['5.1' '3.8' '1.5' '0.3' 'setosa'] ['5.4' '3.4' '1.7' '0.2' 'setosa']] [['5.1' '3.7' '1.5' '0.4' 'setosa'] ['4.6' '3.6' '1.0' '0.2' 'setosa'] ['5.1' '3.3' '1.7' '0.5' 'setosa'] ['4.8' '3.4' '1.9' '0.2' 'setosa'] ['5.0' '3.0' '1.6' '0.2' 'setosa'] ['5.0' '3.4' '1.6' '0.4' 'setosa'] ['5.2' '3.5' '1.5' '0.2' 'setosa']] [['5.2' '3.4' '1.4' '0.2' 'setosa'] ['4.7' '3.2' '1.6' '0.2' 'setosa'] ['4.8' '3.1' '1.6' '0.2' 'setosa'] ['5.4' '3.4' '1.5' '0.4' 'setosa'] ['5.2' '4.1' '1.5' '0.1' 'setosa'] ['5.5' '4.2' '1.4' '0.2' 'setosa'] ['4.9' '3.1' '1.5' '0.1' 'setosa']] [['5.0' '3.2' '1.2' '0.2' 'setosa'] ['5.5' '3.5' '1.3' '0.2' 'setosa'] ['4.9' '3.1' '1.5' '0.1' 'setosa'] ['4.4' '3.0' '1.3' '0.2' 'setosa'] ['5.1' '3.4' '1.5' '0.2' 'setosa'] ['5.0' '3.5' '1.3' '0.3' 'setosa'] ['4.5' '2.3' '1.3' '0.3' 'setosa']] [['4.4' '3.2' '1.3' '0.2' 'setosa'] ['5.0' '3.5' '1.6' '0.6' 'setosa'] ['5.1' '3.8' '1.9' '0.4' 'setosa'] ['4.8' '3.0' '1.4' '0.3' 'setosa'] ['5.1' '3.8' '1.6' '0.2' 'setosa'] ['4.6' '3.2' '1.4' '0.2' 'setosa'] ['5.3' '3.7' '1.5' '0.2' 'setosa']] [['5.0' '3.3' '1.4' '0.2' 'setosa'] ['7.0' '3.2' '4.7' '1.4' 'versicolor'] ['6.4' '3.2' '4.5' '1.5' 'versicolor'] ['6.9' '3.1' '4.9' '1.5' 'versicolor'] ['5.5' '2.3' '4.0' '1.3' 'versicolor'] ['6.5' '2.8' '4.6' '1.5' 'versicolor'] ['5.7' '2.8' '4.5' '1.3' 'versicolor']] [['6.3' '3.3' '4.7' '1.6' 'versicolor'] ['4.9' '2.4' '3.3' '1.0' 'versicolor'] ['6.6' '2.9' '4.6' '1.3' 'versicolor'] ['5.2' '2.7' '3.9' '1.4' 'versicolor'] ['5.0' '2.0' '3.5' '1.0' 'versicolor'] ['5.9' '3.0' '4.2' '1.5' 'versicolor'] ['6.0' '2.2' '4.0' '1.0' 'versicolor']] [['6.1' '2.9' '4.7' '1.4' 'versicolor'] ['5.6' '2.9' '3.6' '1.3' 'versicolor'] ['6.7' '3.1' '4.4' '1.4' 'versicolor'] ['5.6' '3.0' '4.5' '1.5' 'versicolor'] ['5.8' '2.7' '4.1' '1.0' 'versicolor'] ['6.2' '2.2' '4.5' '1.5' 'versicolor'] ['5.6' '2.5' '3.9' '1.1' 'versicolor']] [['5.9' '3.2' '4.8' '1.8' 'versicolor'] ['6.1' '2.8' '4.0' '1.3' 'versicolor'] ['6.3' '2.5' '4.9' '1.5' 'versicolor'] ['6.1' '2.8' '4.7' '1.2' 'versicolor'] ['6.4' '2.9' '4.3' '1.3' 'versicolor'] ['6.6' '3.0' '4.4' '1.4' 'versicolor'] ['6.8' '2.8' '4.8' '1.4' 'versicolor']] [['6.7' '3.0' '5.0' '1.7' 'versicolor'] ['6.0' '2.9' '4.5' '1.5' 'versicolor'] ['5.7' '2.6' '3.5' '1.0' 'versicolor'] ['5.5' '2.4' '3.8' '1.1' 'versicolor'] ['5.5' '2.4' '3.7' '1.0' 'versicolor'] ['5.8' '2.7' '3.9' '1.2' 'versicolor'] ['6.0' '2.7' '5.1' '1.6' 'versicolor']] [['5.4' '3.0' '4.5' '1.5' 'versicolor'] ['6.0' '3.4' '4.5' '1.6' 'versicolor'] ['6.7' '3.1' '4.7' '1.5' 'versicolor'] ['6.3' '2.3' '4.4' '1.3' 'versicolor'] ['5.6' '3.0' '4.1' '1.3' 'versicolor'] ['5.5' '2.5' '4.0' '1.3' 'versicolor'] ['5.5' '2.6' '4.4' '1.2' 'versicolor']] [['6.1' '3.0' '4.6' '1.4' 'versicolor'] ['5.8' '2.6' '4.0' '1.2' 'versicolor'] ['5.0' '2.3' '3.3' '1.0' 'versicolor'] ['5.6' '2.7' '4.2' '1.3' 'versicolor'] ['5.7' '3.0' '4.2' '1.2' 'versicolor'] ['5.7' '2.9' '4.2' '1.3' 'versicolor'] ['6.2' '2.9' '4.3' '1.3' 'versicolor']] [['5.1' '2.5' '3.0' '1.1' 'versicolor'] ['5.7' '2.8' '4.1' '1.3' 'versicolor'] ['6.3' '3.3' '6.0' '2.5' 'virginica'] ['5.8' '2.7' '5.1' '1.9' 'virginica'] ['7.1' '3.0' '5.9' '2.1' 'virginica'] ['6.3' '2.9' '5.6' '1.8' 'virginica'] ['6.5' '3.0' '5.8' '2.2' 'virginica']] [['7.6' '3.0' '6.6' '2.1' 'virginica'] ['4.9' '2.5' '4.5' '1.7' 'virginica'] ['7.3' '2.9' '6.3' '1.8' 'virginica'] ['6.7' '2.5' '5.8' '1.8' 'virginica'] ['7.2' '3.6' '6.1' '2.5' 'virginica'] ['6.5' '3.2' '5.1' '2.0' 'virginica'] ['6.4' '2.7' '5.3' '1.9' 'virginica']] [['6.8' '3.0' '5.5' '2.1' 'virginica'] ['5.7' '2.5' '5.0' '2.0' 'virginica'] ['5.8' '2.8' '5.1' '2.4' 'virginica'] ['6.4' '3.2' '5.3' '2.3' 'virginica'] ['6.5' '3.0' '5.5' '1.8' 'virginica'] ['7.7' '3.8' '6.7' '2.2' 'virginica'] ['7.7' '2.6' '6.9' '2.3' 'virginica']] [['6.0' '2.2' '5.0' '1.5' 'virginica'] ['6.9' '3.2' '5.7' '2.3' 'virginica'] ['5.6' '2.8' '4.9' '2.0' 'virginica'] ['7.7' '2.8' '6.7' '2.0' 'virginica'] ['6.3' '2.7' '4.9' '1.8' 'virginica'] ['6.7' '3.3' '5.7' '2.1' 'virginica'] ['7.2' '3.2' '6.0' '1.8' 'virginica']] [['6.2' '2.8' '4.8' '1.8' 'virginica'] ['6.1' '3.0' '4.9' '1.8' 'virginica'] ['6.4' '2.8' '5.6' '2.1' 'virginica'] ['7.2' '3.0' '5.8' '1.6' 'virginica'] ['7.4' '2.8' '6.1' '1.9' 'virginica'] ['7.9' '3.8' '6.4' '2.0' 'virginica'] ['6.4' '2.8' '5.6' '2.2' 'virginica']] [['6.3' '2.8' '5.1' '1.5' 'virginica'] ['6.1' '2.6' '5.6' '1.4' 'virginica'] ['7.7' '3.0' '6.1' '2.3' 'virginica'] ['6.3' '3.4' '5.6' '2.4' 'virginica'] ['6.4' '3.1' '5.5' '1.8' 'virginica'] ['6.0' '3.0' '4.8' '1.8' 'virginica'] ['6.9' '3.1' '5.4' '2.1' 'virginica']] [['6.7' '3.1' '5.6' '2.4' 'virginica'] ['6.9' '3.1' '5.1' '2.3' 'virginica'] ['5.8' '2.7' '5.1' '1.9' 'virginica'] ['6.8' '3.2' '5.9' '2.3' 'virginica'] ['6.7' '3.3' '5.7' '2.5' 'virginica'] ['6.7' '3.0' '5.2' '2.3' 'virginica'] ['6.3' '2.5' '5.0' '1.9' 'virginica']] [['6.5' '3.0' '5.2' '2.0' 'virginica'] ['6.2' '3.4' '5.4' '2.3' 'virginica'] ['5.9' '3.0' '5.1' '1.8' 'virginica']]
