发布时间:2019-09-15 10:00:08编辑:auto阅读(1785)
# -*- coding: utf-8 -*- ''' about numpy.genfromtxt, means generate from txt file https://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html numpy.genfromtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding='bytes') See also numpy.loadtxt equivalent function when no data is missing. ''' from cryptography.hazmat.primitives.serialization import Encoding # ndarray_fromtxt_lt = numpy.loadtxt('data.txt',delimiter=',',dtype=numpy.str) # # ndarray_fromtxt_lt = numpy.loadtxt(open('data.txt','r',encoding='utf-8'),delimiter=',',dtype=numpy.str) # # ndarray_fromtxt_lt = numpy.loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin) # print(type(ndarray_fromtxt_lt)) # print(ndarray_fromtxt_lt) # ndarray_data = numpy.genfromtxt(fname='data.txt', dtype=str,delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows) # file = open('data.txt',encoding='utf-8') ''' -使用场景:数据转换 (编码转换、值转换) -关键参数:converters The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: converters = {3: lambda s: float(s or 0)}. -它是函数的集合,可以编写函数(或者使用lambda)对某列的值进行转换,常用的场景有:编码转换、值转换等 -特别注意!!! - 1、转换函数的输入,默认都是bytes类型,跟encoding参数有关,跟dtype参数无关。 - dtype影响数据的最终呈现形式 - encoding影响数据处理过程 - 关于encoding参数的官方说明: - Override this value to receive unicode arrays and pass strings as input to converters. - If set to None the system default is used. The default value is ‘bytes’. - 2、转换函数的返回的类型,必须跟设置的dtype保持一致,否则会造成不可预料的数据丢失。 - 例如,genfromtxt设置dtype=str,即所有列的类型都是str,那么,转换函数的返回类型也必须是str - 3、如果数据中含有中文,可能会跟Windows系统默认的ascii字符集冲突,需要转码为utf-8 ''' import os os.remove('data.txt') fo = open('data.txt','a',encoding='utf-8') fo.write("001,张三,man,24\n") fo.write("002,李四,man,24\n") fo.close() import numpy def convUTF8(x): return x.decode('utf-8') def convAdd(x): return str(x,encoding='utf-8') + '+' '''正确示范:单列处理''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=str, converters={1: convUTF8}) print(ndarry_1) # [['001' '张三' 'man' '24'] # ['002' '李四' 'man' '24']] '''正确示范:多列处理''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=str, converters={1: convUTF8, 2: lambda x: x.decode('utf-8') }) print(ndarry_1) # [['001' '张三' 'man' '24'] # ['002' '李四' 'man' '24']] '''错误示范:因为转换函数的返回类型没有跟输入类型保持一致,会造成数据丢失''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=str, converters={1: lambda x: 1}) print(ndarry_1) # [('', 1, '', '') ('', 1, '', '')] '''错误示范:对于一个列,只能一个转换函数,且只能处理一次,设置多次,是无效的''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=str, converters={1: convUTF8, 0: convAdd, 0: convAdd}) print(ndarry_1) # [['001+' '张三' 'man' '24'] # ['002+' '李四' 'man' '24']] ''' -使用场景:设置列的格式 -关键参数:dtype - Data type of the resulting array. - If None, the dtypes will be determined by the contents of each column, individually. - 作为ndarray中的元素,dtype可以设置数据类型 ''' os.remove('data.txt') fo = open('data.txt','a',encoding='utf-8') fo.write("001,zhangsan,man,24\n") fo.write("002,lisi,man,24\n") fo.close() '''错误示范:dtype=None,自动格式,但是差强人意。对于非数字的列,默认是bytes''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=None) print(ndarry_1) # [(1, b'zhangsan', b'man', 24) (2, b'lisi', b'man', 24)] '''正确示范:全部列设置统一的数据类型。对于不含有中文的数据,dtype=str是可以的,如果含有中文,除了设置dtype=str以外,还要用converters做转码''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=str) print(ndarry_1) # [['001' 'zhangsan' 'man' '24'] # ['002' 'lisi' 'man' '24']] '''正确示范:逐列设置数据类型。需要另外了解dtype的种类。''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=[('c0','<i8'),('c1','<U32'),('c2','|S3'),('c3','f4')]) print(ndarry_1) # [(1, 'zhangsan', b'man', 24.0) (2, 'lisi', b'man', 24.0)] ''' -使用场景:数据切片 -关键参数:dtype - Data type of the resulting array. - If None, the dtypes will be determined by the contents of each column, individually. - 作为ndarray中的元素,dtype可以设置数据类型 ''' ''' -使用场景:缺省值的处理 -关键参数:dtype - Data type of the resulting array. - If None, the dtypes will be determined by the contents of each column, individually. - 作为ndarray中的元素,dtype可以设置数据类型 ''' ''' -使用场景:数据切片 -关键参数:skip_header - 起始行 -关键参数:max_rows - 最大行数 -关键参数:usecols - 保留列 -关键参数:comments (执行顺序是最后的,先做行列切片,再做删除注释行) - 注释符号。 - 如果是行首注释,正行都会被舍弃; - 如果是行中其他位置的注释,会报错,以为改行被保留下来了,但是注释符号后面的字段丢失了。 ''' os.remove('data.txt') fo = open('data.txt','a',encoding='utf-8') fo.write("001,zhangsan,man,24\n") fo.write("002,lisi,man,24\n") fo.write("#003,wangwu,man,24\n") fo.write("004,chenhua,wom,18\n") fo.close() '''正确示范:注意执行顺,先做行列切片,再做删除注释行''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=str, skip_header=1,max_rows=3, usecols=[0,2], comments='#', ) print(ndarry_1) # [['002' 'man'] # ['004' 'wom']] ''' -使用场景:填补缺失值(当dtype=None时,填补缺失值这个功能较好用,当dtype=str时,这个功能不生效,要再摸索) -关键参数:missing_values - 标记为缺失 -关键参数:filling_values - 对缺失的位置进行填补 ''' os.remove('data.txt') fo = open('data.txt','a',encoding='utf-8') fo.write("10,11,,13\n") fo.write("10,21,22,23\n") fo.write("10,31,32,33\n") fo.close() '''正确示范:某列的缺失进行具体设置(其他列是默认缺失),全部列统一默认填补''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=None, missing_values={0:10}, filling_values=999 ) print(ndarry_1) # [[999 11 999 13] # [999 21 22 23] # [999 31 32 33]] '''正确示范:某列的缺失,某列的填补''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=None, missing_values={0:10}, filling_values={0:777,2:999} ) print(ndarry_1) # [[777 11 999 13] # [777 21 22 23] # [777 31 32 33]] '''正确示范:所有列的缺失进行统一设置(但是默认缺失还是生效了)''' ndarry_1 = numpy.genfromtxt(fname='data.txt',delimiter=',',dtype=None, missing_values=10, filling_values={0:777,2:999} ) print(ndarry_1) # [[777 11 999 13] # [777 21 22 23] # [777 31 32 33]]
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