python 解析m4a文件吗
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参考技术A 应该会有特定的模块来解析，像是python有专门解析Excel表格的模块一样。如何在不使用 Python 中的外部库的情况下解析 arff 文件
【中文标题】如何在不使用 Python 中的外部库的情况下解析 arff 文件【英文标题】：How can I parse an arff file without using external libraries in Python 【发布时间】：2014-04-06 21:51:52 【问题描述】：我需要在不使用任何外部库的情况下解析如下所示的 arff 文件。我不确定如何将属性与数值相关联。就像我怎么能说每行中的第一个数值是年龄，而第二个是性别？你能把我链接到一些用于解析类似场景的python代码吗？
@relation cleveland-14-heart-disease
@attribute 'age' real
@attribute 'sex' female, male
@attribute 'cp' typ_angina, asympt, non_anginal, atyp_angina
@attribute 'trestbps' real
@attribute 'chol' real
@attribute 'fbs' t, f
@attribute 'restecg' left_vent_hyper, normal, st_t_wave_abnormality
@attribute 'thalach' real
@attribute 'exang' no, yes
@attribute 'oldpeak' real
@attribute 'slope' up, flat, down
@attribute 'ca' real
@attribute 'thal' fixed_defect, normal, reversable_defect
@attribute 'class' negative, positive
@data
63,male,typ_angina,145,233,t,left_vent_hyper,150,no,2.3,down,0,fixed_defect,negative
37,male,non_anginal,130,250,f,normal,187,no,3.5,down,0,normal,negative
41,female,atyp_angina,130,204,f,left_vent_hyper,172,no,1.4,up,0,normal,negative
56,male,atyp_angina,120,236,f,normal,178,no,0.8,up,0,normal,negative
57,female,asympt,120,354,f,normal,163,yes,0.6,up,0,normal,negative
57,male,asympt,140,192,f,normal,148,no,0.4,flat,0,fixed_defect,negative
56,female,atyp_angina,140,294,f,left_vent_hyper,153,no,1.3,flat,0,normal,negative
44,male,atyp_angina,120,263,f,normal,173,no,0,up,0,reversable_defect,negative
52,male,non_anginal,172,199,t,normal,162,no,0.5,up,0,reversable_defect,negative
这是我编写的示例代码：
arr=[]
arff_file = open("heart_train.arff")
count=0
for line in arff_file:
count+=1
#line=line.strip("\n")
#line=line.split(',')
if not (line.startswith("@")):
if not (line.startswith("%")):
line=line.strip("\n")
line=line.split(',')
arr.append(line)
print(arr[1:30])
但是输出与我预期的非常不同：
[['37', 'male', 'non_anginal', '130', '250', 'f', 'normal', '187', 'no', '3.5', 'down', '0', 'normal', 'negative'], ['41', 'female', 'atyp_angina', '130', '204', 'f', 'left_vent_hyper', '172', 'no', '1.4', 'up', '0', 'normal', 'negative'], ['56', 'male', 'atyp_angina', '120', '236', 'f', 'normal', '178', 'no', '0.8', 'up', '0', 'normal', 'negative'], ['57', 'female', 'asympt', '120', '354', 'f', 'normal', '163', 'yes', '0.6', 'up', '0', 'normal', 'negative'], ['57', 'male', 'asympt', '140', '192', 'f', 'normal', '148', 'no', '0.4', 'flat', '0', 'fixed_defect', 'negative'], ['56', 'female', 'atyp_angina', '140', '294', 'f', 'left_vent_hyper', '153', 'no', '1.3', 'flat', '0', 'normal', 'negative'], ['44', 'male', 'atyp_angina', '120', '263', 'f', 'normal', '173', 'no', '0', 'up', '0', 'reversable_defect', 'negative'], ['52', 'male', 'non_anginal', '172', '199', 't', 'normal', '162', 'no', '0.5', 'up', '0', 'reversable_defect', 'negative'], ['57', 'male', 'non_anginal', '150', '168', 'f', 'normal', '174', 'no', '1.6', 'up', '0', 'normal', 'negative'], ['54', 'male', 'asympt', '140', '239', 'f', 'normal', '160', 'no', '1.2', 'up', '0', 'normal', 'negative'], ['48', 'female', 'non_anginal', '130', '275', 'f', 'normal', '139', 'no', '0.2', 'up', '0', 'normal', 'negative'], ['49', 'male', 'atyp_angina', '130', '266', 'f', 'normal', '171', 'no', '0.6', 'up', '0', 'normal', 'negative'], ['64', 'male', 'typ_angina', '110', '211', 'f', 'left_vent_hyper', '144', 'yes', '1.8', 'flat', '0', 'normal', 'negative'], ['58', 'female', 'typ_angina', '150', '283', 't', 'left_vent_hyper', '162', 'no', '1', 'up', '0', 'normal', 'negative'], ['50', 'female', 'non_anginal', '120', '219', 'f', 'normal', '158', 'no', '1.6', 'flat', '0', 'normal', 'negative'], ['58', 'female', 'non_anginal', '120', '340', 'f', 'normal', '172', 'no', '0', 'up', '0', 'normal', 'negative'], ['66', 'female', 'typ_angina', '150', '226', 'f', 'normal', '114', 'no', '2.6', 'down', '0', 'normal', 'negative'], ['43', 'male', 'asympt', '150', '247', 'f', 'normal', '171', 'no', '1.5', 'up', '0', 'normal', 'negative'], ['69', 'female', 'typ_angina', '140', '239', 'f', 'normal', '151', 'no', '1.8', 'up', '2', 'normal', 'negative'], ['59', 'male', 'asympt', '135', '234', 'f', 'normal', '161', 'no', '0.5', 'flat', '0', 'reversable_defect', 'negative'], ['44', 'male', 'non_anginal', '130', '233', 'f', 'normal', '179', 'yes', '0.4', 'up', '0', 'normal', 'negative'], ['42', 'male', 'asympt', '140', '226', 'f', 'normal', '178', 'no', '0', 'up', '0', 'normal', 'negative'], ['61', 'male', 'non_anginal', '150', '243', 't', 'normal', '137', 'yes', '1', 'flat', '0', 'normal', 'negative'], ['40', 'male', 'typ_angina', '140', '199', 'f', 'normal', '178', 'yes', '1.4', 'up', '0', 'reversable_defect', 'negative'], ['71', 'female', 'atyp_angina', '160', '302', 'f', 'normal', '162', 'no', '0.4', 'up', '2', 'normal', 'negative'], ['59', 'male', 'non_anginal', '150', '212', 't', 'normal', '157', 'no', '1.6', 'up', '0', 'normal', 'negative'], ['51', 'male', 'non_anginal', '110', '175', 'f', 'normal', '123', 'no', '0.6', 'up', '0', 'normal', 'negative'], ['65', 'female', 'non_anginal', '140', '417', 't', 'left_vent_hyper', '157', 'no', '0.8', 'up', '1', 'normal', 'negative'], ['53', 'male', 'non_anginal', '130', '197', 't', 'left_vent_hyper', '152', 'no', '1.2', 'down', '0', 'normal', 'negative']]
您知道如何获得由 arff 库（来自 Weka）创建的如下输出吗？
【问题讨论】：
这看起来很容易解析。你试过什么？当您发布一些代码时，Stack Overflow 会更好地工作。 请分享您到目前为止所尝试的内容？这样我们就能让你做得更好 @shaktimaan 此行也不起作用：` if ((line.startswith!="@") and (line.startswith!="%")):` @MonaJalal: 因为它的语法是line.startswith("@")。如果您希望它不等于使用if not (line.startswith("@"))
现在您正在创建一个list of list。尝试创建list of tuple。将您的最后一条语句更改为 arr.append(tuple(line))
【参考方案1】：
您说“没有外部库”，但您至少可以剪切并粘贴到您自己的代码中吗？您可能会发现the source code to the arff module 很有用（200 行，大约 5.6 KB）。
编辑：
您可能会发现此格式参考很有用：http://weka.wikispaces.com/ARFF+%28stable+version%29
编辑2：
只是为了好玩，我编写了自己的 .arrf 解析器；它几乎和 WEKA 代码一样长，但应该更具可读性——只有六个函数、一个调度表和一个非常模块化的类。您可以遍历一个类实例以将每个数据行作为一个命名元组。
看看你的想法：
from collections import namedtuple
from keyword import iskeyword
import re
def NotDone(msg):
raise NotImplemented(msg)
def nominal(spec):
"""
Create an ARFF nominal (enumerated) data type
"""
spec = spec.lstrip(" \t").rstrip(" \t")
good_values = set(val.strip() for val in spec.split(","))
def fn(s):
s = s.strip()
if s in good_values:
return s
else:
raise ValueError("'' is not a recognized value".format(s))
# patch docstring
fn.__name__ = "nominal"
fn.__doc__ = """
ARFF nominal (enumerated) data type
Legal values are
""".format(sorted(good_values))
return fn
def numeric(s):
"""
Convert string to int or float
"""
try:
return int(s)
except ValueError:
return float(s)
field_maker =
"date": (lambda spec: NotDone("date data type not implemented")),
"integer": (lambda spec: int),
"nominal": (lambda spec: nominal(spec)),
"numeric": (lambda spec: numeric),
"string": (lambda spec: str),
"real": (lambda spec: float),
"relational": (lambda spec: NotDone("relational data type not implemented")),
def file_lines(fname):
# lazy file reader; ensures file is closed when done,
# returns lines without trailing spaces or newline
with open(fname) as inf:
for line in inf:
yield line.rstrip()
def no_data_yet(*items):
raise ValueError("AarfRow not fully defined (haven't seen a @data directive yet)")
def make_field_name(s):
"""
Mangle string to make it a valid Python identifier
"""
s = s.lower() # force to lowercase
s = "_".join(re.findall("[a-z0-9]+", s)) # strip all invalid chars; join what's left with "_"
if iskeyword(s) or re.match("[0-9]", s): # if the result is a keyword or starts with a digit
s = "f_"+s # make it a safe field name
return s
class ArffReader:
line_types = ["blank", "comment", "relation", "attribute", "data"]
def __init__(self, fname):
# get input file
self.fname = fname
self.lines = file_lines(fname)
# prepare to read file header
self.relation = '(not specified)'
self.data_names = []
self.data_types = []
self.dtype = no_data_yet
# read file header
line_tests = [
(getattr(self, "line_is_".format(item)), getattr(self, "line_do_".format(item)))
for item in self.__class__.line_types
]
for line in self.lines:
for is_, do in line_tests:
if is_(line):
done = do(line)
break
if done:
break
# use header fields to build data type (and make it print as requested)
class ArffRow(namedtuple('ArffRow', self.data_names)):
__slots__ = ()
def __str__(self):
items = (getattr(self, field) for field in self._fields)
return "()".format(", ".join(repr(it) for it in items))
self.dtype = ArffRow
#
# figure out input-line type
#
def line_is_blank(self, line):
return not line
def line_is_comment(self, line):
return line.lower().startswith('%')
def line_is_relation(self, line):
return line.lower().startswith('@relation')
def line_is_attribute(self, line):
return line.lower().startswith('@attribute')
def line_is_data(self, line):
return line.lower().startswith('@data')
#
# handle input-line type
#
def line_do_blank(self, line):
pass
def line_do_comment(self, line):
pass
def line_do_relation(self, line):
self.relation = line[10:].strip()
def line_do_attribute(self, line):
m = re.match(
"^@attribute" # line starts with '@attribute'
"\s+" #
"(" # name is one of:
"(?:'[^']+')" # ' string in single-quotes '
"|(?:\"[^\"]+\")" # " string in double-quotes "
"|(?:[^ \t'\"]+)" # single_word_string (no spaces)
")" #
"\s+" #
"(" # type is one of:
"(?:[^]+)" # set, of, nominal, values
"|(?:\w+)" # datatype
")" #
"\s*" #
"(" # spec string
".*" # anything to end of line
")$", #
line, flags=re.I) # case-insensitive
if m:
name, type_, spec = m.groups()
self.data_names.append(make_field_name(name))
if type_[0] == '':
type_, spec = 'nominal', type_
self.data_types.append(field_maker[type_](spec))
else:
raise ValueError("failed parsing attribute line ''".format(line))
def line_do_data(self, line):
return True # flag end of header
#
# make the class iterable
#
def __iter__(self):
return self
def next(self):
"""
Return one data row at a time
"""
data = next(self.lines).split(',')
return self.dtype(*(fn(dat) for fn,dat in zip(self.data_types, data)))
它可以用作
for row in ArffReader('mydata.arff'):
print(row)
导致
(63.0, 'male', 'typ_angina', 145.0, 233.0, 't', 'left_vent_hyper', 150.0, 'no', 2.3, 'down', 0.0, 'fixed_defect', 'negative')
(37.0, 'male', 'non_anginal', 130.0, 250.0, 'f', 'normal', 187.0, 'no', 3.5, 'down', 0.0, 'normal', 'negative')
(41.0, 'female', 'atyp_angina', 130.0, 204.0, 'f', 'left_vent_hyper', 172.0, 'no', 1.4, 'up', 0.0, 'normal', 'negative')
(56.0, 'male', 'atyp_angina', 120.0, 236.0, 'f', 'normal', 178.0, 'no', 0.8, 'up', 0.0, 'normal', 'negative')
(57.0, 'female', 'asympt', 120.0, 354.0, 'f', 'normal', 163.0, 'yes', 0.6, 'up', 0.0, 'normal', 'negative')
(57.0, 'male', 'asympt', 140.0, 192.0, 'f', 'normal', 148.0, 'no', 0.4, 'flat', 0.0, 'fixed_defect', 'negative')
(56.0, 'female', 'atyp_angina', 140.0, 294.0, 'f', 'left_vent_hyper', 153.0, 'no', 1.3, 'flat', 0.0, 'normal', 'negative')
(44.0, 'male', 'atyp_angina', 120.0, 263.0, 'f', 'normal', 173.0, 'no', 0.0, 'up', 0.0, 'reversable_defect', 'negative')
(52.0, 'male', 'non_anginal', 172.0, 199.0, 't', 'normal', 162.0, 'no', 0.5, 'up', 0.0, 'reversable_defect', 'negative')
这些字段也可以按名称寻址，即
for patient in ArffReader('mydata.arff'):
print(" year old ".format(patient.age, patient.sex))
给了
63.0 year old male
37.0 year old male
41.0 year old female
56.0 year old male
57.0 year old female
57.0 year old male
56.0 year old female
44.0 year old male
52.0 year old male
你可以看到文件名
>>> print(repr(patient))
ArffRow(age=63.0, sex='male', cp='typ_angina', trestbps=145.0, chol=233.0, fbs='t', restecg='left_vent_hyper', thalach=150.0, exang='no', oldpeak=2.3, slope='down', ca=0.0, thal='fixed_defect', f_class='negative')
字段名称根据 ARFF 标头，强制小写（在 'class' 前面加上 'f_' 的情况下，因为class 是 Python 关键字，因此不能用作字段名称）。
【讨论】：
我知道我可以做到这一点，但我正计划复习我的知识。我认为这是一件好事。也感谢您的建议。 @HughBothwell 散布的评论正则表达式只是??(+1)以上是关于python 解析m4a文件吗的主要内容，如果未能解决你的问题，请参考以下文章