PyTables +熊猫选择问题

Question

我有结构化的这样一个HDF5（PyTables）文件：

/<User>/<API Key> 
ex: 
/Dan/A4N5 
/Dan/B8P0 
/Dave/D3Y7 

每个表的结构，像这样与存储在时代一个会话ID和时间：

sessionID  time 
0 3ODE3Nzll 1467590400 
1 lMGVkMDc4 1467590400 
2 jNzIzNmY1 1467590400 
... 

我希望Pandas遍历每个表并获取指定日期和指定日期前一天之间的所有行。目前我有这样的代码：

scriptPath = os.path.dirname(os.path.abspath(__file__)) 
argdate = "2016/07/14" 
dayTimestamp = datetime.datetime(int(argdate[0:4]), int(argdate[5:7]), int(argdate[8:10]), tzinfo=pytz.utc) 
yesterdayTimestamp = dayTimestamp - datetime.timedelta(days=1) 
with pd.HDFStore(os.path.join(scriptPath, "userdatabase.h5")) as db: 
    for table in db.keys(): 
     print(table) 
     tableSplit = table.split('/') 
     client = tableSplit[1] 
     apiKey = tableSplit[2] 
     df = db.select('{}/{}'.format(client, apiKey), where='time<=int(dayTimestamp.timestamp()) & time>=int(yesterdayTimestamp.timestamp())') 
     print(df) 

但是抛出一个错误的选择位于行。

File "tester.py", line 570, in database 
    df = db.select('{}/{}'.format(client, apiKey), where='time<=int(dayTimestamp.timestamp()) & time>=int(yesterdayTimestamp.timestamp())') 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 680, in select 
    return it.get_result() 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 1364, in get_result 
    results = self.func(self.start, self.stop, where) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 673, in func 
    columns=columns, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 4021, in read 
    if not self.read_axes(where=where, **kwargs): 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 3222, in read_axes 
    self.selection = Selection(self, where=where, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 4580, in __init__ 
    self.terms = self.generate(where) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py", line 4593, in generate 
    return Expr(where, queryables=q, encoding=self.table.encoding) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/pytables.py", line 517, in __init__ 
    self.terms = self.parse() 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 727, in parse 
    return self._visitor.visit(self.expr) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 311, in visit 
    return visitor(node, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 317, in visit_Module 
    return self.visit(expr, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 311, in visit 
    return visitor(node, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 320, in visit_Expr 
    return self.visit(node.value, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 311, in visit 
    return visitor(node, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 655, in visit_BoolOp 
    return reduce(visitor, operands) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 647, in visitor 
    lhs = self._try_visit_binop(x) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 643, in _try_visit_binop 
    return self.visit(bop) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 311, in visit 
    return visitor(node, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 628, in visit_Compare 
    return self.visit(binop) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 311, in visit 
    return visitor(node, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 401, in visit_BinOp 
    op, op_class, left, right = self._possibly_transform_eq_ne(node) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 354, in _possibly_transform_eq_ne 
    right = self.visit(node.right, side='right') 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 311, in visit 
    return visitor(node, **kwargs) 
    File "/usr/local/lib/python3.4/dist-packages/pandas/computation/expr.py", line 615, in visit_Call_legacy 
    return self.const_type(res(*args, **keywords), self.env) 
TypeError: 'str' object is not callable 

1）如何解决这个错误？

2）是否可以遍历HDF5文件层次结构，以便可以将Pandas表分组？

Answer 1

这里是一个工作演示：

import io 
import pandas as pd 

df = pd.read_csv(io.StringIO(""" 
sessionID  time 
3ODE3Nzll 1467590400 
lMGVkMDc4 1467590400 
jNzIzNmY1 1467590400 
3ODE3Nzll 1467676800 
lMGVkMDc4 1467676800 
jNzIzNmY1 1467676800 
"""), sep='\s+') 

filename = 'c:/temp/aaa.h5' 

store = pd.HDFStore(filename) 

store.append('/aaa/df1', df, data_columns=True) 
store.append('/bbb/df1', df, data_columns=True) 

# let's double # of rows 
df = pd.concat([df] * 2, ignore_index=True) 

# and write it to HDFStore 
store.append('/aaa/df2', df, data_columns=True) 

print(store) 

argdate = "2016/07/04" 
ts_from = int(pd.to_datetime(argdate).timestamp()) 
ts_to = ts_from + 24*60*60 

client_flt = '/aaa/' 
#qry = '(time >= {0}) & (time <= {1})'.format(dayTimestamp, dayTimestamp + 24*60*60) 
qry = 'time >= ts_from & time <= ts_to' 
print('WHERE:\t%s' %qry) 

for k in store: 
    if k.startswith(client_flt): 
     x = store.select(k, where=qry) 
     print(k) 
     print(x) 

输出：

<class 'pandas.io.pytables.HDFStore'> 
File path: c:/temp/aaa.h5 
/aaa/df1   frame_table (typ->appendable,nrows->6,ncols->2,indexers->[index],dc->[sessionID,time]) 
/aaa/df2   frame_table (typ->appendable,nrows->12,ncols->2,indexers->[index],dc->[sessionID,time]) 
/bbb/df1   frame_table (typ->appendable,nrows->6,ncols->2,indexers->[index],dc->[sessionID,time]) 

WHERE: time >= ts_from & time <= ts_to 
/aaa/df1 
    sessionID  time 
0 3ODE3Nzll 1467590400 
1 lMGVkMDc4 1467590400 
2 jNzIzNmY1 1467590400 
/aaa/df2 
    sessionID  time 
0 3ODE3Nzll 1467590400 
1 lMGVkMDc4 1467590400 
2 jNzIzNmY1 1467590400 
6 3ODE3Nzll 1467590400 
7 lMGVkMDc4 1467590400 
8 jNzIzNmY1 1467590400