如何解决HDFStore异常问题:无法找到正确的原子类型

Question

我正在寻找关于什么类型的数据场景可能导致此异常的一般指导.我试过以各种方式按摩我的数据无济于事.

我已经搜索了这个例外几天了,经历了几次谷歌小组讨论,并没有提出调试的解决方案HDFStore Exception: cannot find the correct atom type.我正在阅读混合数据类型的简单csv文件:

Int64Index: 401125 entries, 0 to 401124
Data columns:
SalesID                     401125  non-null values
SalePrice                   401125  non-null values
MachineID                   401125  non-null values
ModelID                     401125  non-null values
datasource                  401125  non-null values
auctioneerID                380989  non-null values
YearMade                    401125  non-null values
MachineHoursCurrentMeter    142765  non-null values
UsageBand                   401125  non-null values
saledate                    401125  non-null values
fiModelDesc                 401125  non-null values
Enclosure_Type              401125  non-null values
...................................................
Stick_Length                401125  non-null values
Thumb                       401125  non-null values
Pattern_Changer             401125  non-null values
Grouser_Type                401125  non-null values
Backhoe_Mounting            401125  non-null values
Blade_Type                  401125  non-null values
Travel_Controls             401125  non-null values
Differential_Type           401125  non-null values
Steering_Controls           401125  non-null values
dtypes: float64(2), int64(6), object(45)

存储数据帧的代码:

In [30]: store = pd.HDFStore('test0.h5','w')
In [31]: for chunk in pd.read_csv('Train.csv', chunksize=10000):
   ....:     store.append('df', chunk, index=False)

请注意,如果我store.put在一次导入的数据帧上使用,我可以成功存储它,虽然很慢(我相信这是由于对象dtypes的酸洗,即使对象只是字符串数据).

是否存在可能引发此异常的NaN值考虑因素？

例外:

Exception: cannot find the correct atom type -> [dtype->object,items->Index([Usa
geBand, saledate, fiModelDesc, fiBaseModel, fiSecondaryDesc, fiModelSeries, fiMo
delDescriptor, ProductSize, fiProductClassDesc, state, ProductGroup, ProductGrou
pDesc, Drive_System, Enclosure, Forks, Pad_Type, Ride_Control, Stick, Transmissi
on, Turbocharged, Blade_Extension, Blade_Width, Enclosure_Type, Engine_Horsepowe
r, Hydraulics, Pushblock, Ripper, Scarifier, Tip_Control, Tire_Size, Coupler, Co
upler_System, Grouser_Tracks, Hydraulics_Flow, Track_Type, Undercarriage_Pad_Wid
th, Stick_Length, Thumb, Pattern_Changer, Grouser_Type, Backhoe_Mounting, Blade_
Type, Travel_Controls, Differential_Type, Steering_Controls], dtype=object)] lis
t index out of range

更新1

Jeff关于存储在数据框中的列表的提示让我调查了嵌入式逗号. pandas.read_csv正确解析文件,双引号内确实有一些嵌入式逗号.所以这些字段本身不是python列表,但在文本中有逗号.这里有些例子:

3     Hydraulic Excavator, Track - 12.0 to 14.0 Metric Tons
6     Hydraulic Excavator, Track - 21.0 to 24.0 Metric Tons
8       Hydraulic Excavator, Track - 3.0 to 4.0 Metric Tons
11      Track Type Tractor, Dozer - 20.0 to 75.0 Horsepower
12    Hydraulic Excavator, Track - 19.0 to 21.0 Metric Tons

但是,当我从pd.read_csv块中删除此列并附加到我的HDFStore时,我仍然得到相同的异常.当我尝试单独追加每一列时,我得到以下新异常:

In [6]: for chunk in pd.read_csv('Train.csv', header=0, chunksize=50000):
   ...:     for col in chunk.columns:
   ...:         store.append(col, chunk[col], data_columns=True)

Exception: cannot properly create the storer for: [_TABLE_MAP] [group->/SalesID
(Group) '',value-><class 'pandas.core.series.Series'>,table->True,append->True,k
wargs->{'data_columns': True}]

我会继续进行故障排除.这是几百条记录的链接:

https://docs.google.com/spreadsheet/ccc?key=0AutqBaUiJLbPdHFvaWNEMk5hZ1NTNlVyUVduYTZTeEE&usp=sharing

更新2

好的,我在我的工作计算机上尝试了以下内容并获得了以下结果:

In [4]: store = pd.HDFStore('test0.h5','w')

In [5]: for chunk in pd.read_csv('Train.csv', chunksize=10000):
   ...:     store.append('df', chunk, index=False, data_columns=True)
   ...:

Exception: cannot find the correct atom type -> [dtype->object,items->Index([fiB
aseModel], dtype=object)] [fiBaseModel] column has a min_itemsize of [13] but it
emsize [9] is required!

我想我知道这里发生了什么.如果我fiBaseModel为第一个块获取字段的最大长度,我得到这个:

In [16]: lens = df.fiBaseModel.apply(lambda x: len(x))

In [17]: max(lens[:10000])
Out[17]: 9

第二个块:

In [18]: max(lens[10001:20000])
Out[18]: 13

因此,为此列创建的存储表为9个字节,因为这是第一个块的最大值.当它在后续块中遇到较长的文本字段时,它会抛出异常.

我对此的建议是截断后续块中的数据(带有警告)或允许用户指定列的最大存储空间并截断超出它的任何内容.也许熊猫已经可以做到这一点,我还没来得及真正潜入深渊HDFStore.

更新3

尝试使用pd.read_csv导入csv数据集.我将所有对象的字典传递给dtypes参数.然后我迭代文件并将每个块存储到HDFStore中,传递一个大值min_itemsize.我得到以下异常:

AttributeError: 'NoneType' object has no attribute 'itemsize'

我的简单代码:

store = pd.HDFStore('test0.h5','w')
objects = dict((col,'object') for col in header)

for chunk in pd.read_csv('Train.csv', header=0, dtype=objects,
    chunksize=10000, na_filter=False):
    store.append('df', chunk, min_itemsize=200)

我试图调试和检查堆栈跟踪中的项目.这是表格在异常中的样子:

ipdb> self.table
/df/table (Table(10000,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": StringCol(itemsize=200, shape=(53,), dflt='', pos=1)}
  byteorder := 'little'
  chunkshape := (24,)
  autoIndex := True
  colindexes := {
    "index": Index(6, medium, shuffle, zlib(1)).is_CSI=False}

更新4

现在,我正在尝试迭代地确定数据帧的对象列中最长字符串的长度.我是这样做的:

    def f(x):
        if x.dtype != 'object':
            return
        else:
            return len(max(x.fillna(''), key=lambda x: len(str(x))))

lengths = pd.DataFrame([chunk.apply(f) for chunk in pd.read_csv('Train.csv', chunksize=50000)])
lens = lengths.max().dropna().to_dict()

In [255]: lens
Out[255]:
{'Backhoe_Mounting': 19.0,
 'Blade_Extension': 19.0,
 'Blade_Type': 19.0,
 'Blade_Width': 19.0,
 'Coupler': 19.0,
 'Coupler_System': 19.0,
 'Differential_Type': 12.0
 ... etc... }

一旦我得到了最大字符串列长度的字典,我尝试append通过min_itemsize参数传递给它:

In [262]: for chunk in pd.read_csv('Train.csv', chunksize=50000, dtype=types):
   .....:     store.append('df', chunk, min_itemsize=lens)

Exception: cannot find the correct atom type -> [dtype->object,items->Index([Usa
geBand, saledate, fiModelDesc, fiBaseModel, fiSecondaryDesc, fiModelSeries, fiMo
delDescriptor, ProductSize, fiProductClassDesc, state, ProductGroup, ProductGrou
pDesc, Drive_System, Enclosure, Forks, Pad_Type, Ride_Control, Stick, Transmissi
on, Turbocharged, Blade_Extension, Blade_Width, Enclosure_Type, Engine_Horsepowe
r, Hydraulics, Pushblock, Ripper, Scarifier, Tip_Control, Tire_Size, Coupler, Co
upler_System, Grouser_Tracks, Hydraulics_Flow, Track_Type, Undercarriage_Pad_Wid
th, Stick_Length, Thumb, Pattern_Changer, Grouser_Type, Backhoe_Mounting, Blade_
Type, Travel_Controls, Differential_Type, Steering_Controls], dtype=object)] [va
lues_block_2] column has a min_itemsize of [64] but itemsize [58] is required!

违规列传递的min_itemsize为64,但异常表明需要58项.这可能是个错误？

在[266]中:pd.版本 [266]:'0.11.0.dev-eb07c5a'

Answer 1

您提供的链接可以很好地存储框架.逐列只表示指定data_columns = True.它将单独处理列并在有问题的列上升.

要诊断

store = pd.HDFStore('test0.h5','w')
In [31]: for chunk in pd.read_csv('Train.csv', chunksize=10000):
   ....:     store.append('df', chunk, index=False, data_columns=True)

在生产中,您可能希望将data_columns限制为要查询的列(也可以是None,在这种情况下,您只能查询索引/列)

更新:

你可能会遇到另一个问题.read_csv根据它在每个块中看到的内容来转换dtypes,因此使用10,000的chunksize时,追加操作失败,因为块1和2在某些列中有整数查找数据,然后在块3中你有一些NaN所以因为浮点数.要么预先指定dtypes,使用更大的chunksize,要么运行两次操作以保证块之间的dtypes.

在这种情况下,我更新了pytables.py以获得更有用的异常(以及告诉您列是否存在不兼容的数据)

谢谢你的报道!