如何将以下多维类似xml的数据解析为数据框

如果您更喜欢解析器的功能，这里有一个针对您的问题的pyparsing刺：

from pyparsing import Suppress,QuotedString,Word,alphas,nums,alphanums,Keyword,Optional
import datetime

# define UTC timezone for sake of eval
if hasattr(datetime,"timezone"):
    UTC = datetime.timezone(datetime.timedelta(0),"UTC")
else:
    UTC = None

_ = Suppress
evaltokens = lambda s,l,t: eval(''.join(t))

timevalue = 'datetime.datetime' + QuotedString('(', endQuoteChar=')', unquoteResults=False)
timevalue.setParseAction(evaltokens)

strvalue = 'u' + QuotedString("'", unquoteResults=False)
strvalue.setParseAction(evaltokens)

nonevalue = Keyword("None").setParseAction(lambda s,l,t: [None])
intvalue = Word(nums).setParseAction(lambda s,l,t: int(t[0]))

COMMA = Optional(_(","))

valuedexpr = lambda expr: (Word(alphas) + "(" + "value" + "=" + expr + ")").setParseAction(lambda t: t[4])

lineexpr = (_("Aggregate(aggregate_dimension_value_list=[") +
            valuedexpr(timevalue)("timestamp") + COMMA +
            (nonevalue | valuedexpr(strvalue))("s1") + COMMA +
            (nonevalue | valuedexpr(strvalue))("s2") + COMMA +
        "]" + COMMA +
        "quantity=" + intvalue("qty"))

用于lineexpr.searchString从每个聚合中提取数据：

for data in lineexpr.searchString(sample):
    print data.dump()
    print data.qty
    print

给予：

[datetime.datetime(2013, 8, 28, 19, 30), None, u'VIRTUALLY_LABELED_CASE', ']', 'quantity=', 127]
- qty: 127
- s1: None
- s2: VIRTUALLY_LABELED_CASE
- timestamp: 2013-08-28 19:30:00
127

[datetime.datetime(2013, 8, 28, 19, 30), u'PPTransMergeNonCon', u'PRIME_BIN_RANDOM_STOW', ']', 'quantity=', 15]
- qty: 15
- s1: PPTransMergeNonCon
- s2: PRIME_BIN_RANDOM_STOW
- timestamp: 2013-08-28 19:30:00
15

[datetime.datetime(2013, 8, 27, 21, 0), u'PPTransFRA1', u'PRIME_BIN_RANDOM_STOW', ']', 'quantity=', 8]
- qty: 8
- s1: PPTransFRA1
- s2: PRIME_BIN_RANDOM_STOW
- timestamp: 2013-08-27 21:00:00
8

dump()将显示所有可用的命名结果值-请注意如何使用可以直接访问数量属性data.qty。在中为您设置了结果名称“ qty”的定义"quantity=" + intvalue("qty")。timestamp，s1和s2可以类似地访问。（其中还有一点eval不足之处，清理工作留给读者练习。）

编辑：

这是经过修改的pyparsing解析器，用于处理原始的原始XML风格的东西。所做的更改确实很小：

from pyparsing import Suppress,QuotedString,Word,alphas,nums,alphanums,Keyword,Optional, ungroup
import datetime

# define UTC timezone for sake of eval
if hasattr(datetime,"timezone"):
    UTC = datetime.timezone(datetime.timedelta(0),"UTC")
else:
    UTC = None

_ = Suppress
evaltokens = lambda s,l,t: eval(''.join(t))

timevalue = 'datetime.datetime' + QuotedString('(', endQuoteChar=')', unquoteResults=False)
replUTC = lambda s,l,t: ''.join(t).replace("< UTC>","UTC").replace("<UTC >","UTC").replace("<UTC>","UTC")
timevalue.setParseAction(replUTC, evaltokens)

strvalue = 'u' + QuotedString("'", unquoteResults=False)
strvalue.setParseAction(evaltokens)

nonevalue = Keyword("None").setParseAction(lambda s,l,t: [None])
intvalue = Word(nums).setParseAction(lambda s,l,t: int(t[0]))

COMMA = Optional(_(","))
LT,GT,LPAR,RPAR,LBRACK,RBRACK = map(Suppress,"<>()[]")

#~ valuedexpr = lambda expr: (Word(alphas) + "(" + "value" + "=" + expr + ")").setParseAction(lambda t: t[4])
valuedexpr = lambda expr: ungroup(LT + (Word(alphas) + "(" + "value" + "=" + expr("value") + ")" + GT).setParseAction(lambda t: t.value))

#~ lineexpr = (_("Aggregate(aggregate_dimension_value_list=[") +
            #~ valuedexpr(timevalue)("timestamp") + COMMA +
            #~ (nonevalue | valuedexpr(strvalue))("s1") + COMMA +
            #~ (nonevalue | valuedexpr(strvalue))("s2") + COMMA +
        #~ "]" + COMMA +
        #~ "quantity=" + intvalue("qty"))

lineexpr = (LT + "Aggregate" + LPAR + "aggregate_dimension_value_list" + "=" + LBRACK +
            valuedexpr(timevalue)("timestamp") + COMMA +
            (nonevalue | valuedexpr(strvalue))("s1") + COMMA +
            (nonevalue | valuedexpr(strvalue))("s2") + 
        RBRACK + COMMA +
        "quantity=" + intvalue("qty") + RPAR + GT)

从您粘贴的文本（其中某些文本格式不正确）中给出：

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 26, 20, 30), u'PPTransCGN1', u'PRIME_BIN_RANDOM_STOW', 'quantity=', 992]
- qty: 992
- s1: PPTransCGN1
- s2: PRIME_BIN_RANDOM_STOW
- timestamp: 2013-08-26 20:30:00
992

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 23, 19, 30), None, u'TOTE', 'quantity=', 87]
- qty: 87
- s1: None
- s2: TOTE
- timestamp: 2013-08-23 19:30:00
87

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 27, 17, 30), u'PPTransMUC3', u'TOTE', 'quantity=', 14]
- qty: 14
- s1: PPTransMUC3
- s2: TOTE
- timestamp: 2013-08-27 17:30:00
14

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 27, 20, 30), u'PPTransEUK5', u'PRIME_BIN_RANDOM_STOW', 'quantity=', 339]
- qty: 339
- s1: PPTransEUK5
- s2: PRIME_BIN_RANDOM_STOW
- timestamp: 2013-08-27 20:30:00
339

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 26, 20, 30), u'PPTransCGN1', u'TOTE', 'quantity=', 1731]
- qty: 1731
- s1: PPTransCGN1
- s2: TOTE
- timestamp: 2013-08-26 20:30:00
1731

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 26, 19, 30), u'PPTransEUK5', u'TOTE', 'quantity=', 28]
- qty: 28
- s1: PPTransEUK5
- s2: TOTE
- timestamp: 2013-08-26 19:30:00
28

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 28, 19, 30), u'PPTransORY1', u'PRIME_BIN_RANDOM_STOW', 'quantity=', 69]
- qty: 69
- s1: PPTransORY1
- s2: PRIME_BIN_RANDOM_STOW
- timestamp: 2013-08-28 19:30:00
69

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 26, 19, 30), u'PPTransMAD4', u'PRIME_BIN_RANDOM_STOW', 'quantity=', 47]
- qty: 47
- s1: PPTransMAD4
- s2: PRIME_BIN_RANDOM_STOW
- timestamp: 2013-08-26 19:30:00
47

['Aggregate', 'aggregate_dimension_value_list', '=', datetime.datetime(2013, 8, 26, 21, 0), None, None, 'quantity=', 78]
- qty: 78
- s1: None
- s2: None
- timestamp: 2013-08-26 21:00:00
78