在计算

时间:2018-06-06 10:55:13

标签: python python-3.x pandas dataframe

我在DataFrame中有两列(serverTs,FTs),它们是Unix Time格式的时间戳。 在我的代码中,我需要从另一个中减去一个。当我这样做时,我收到一个错误,说我不能减去字符串。所以我将serverTs和FTs的类型添加为整数。

file = r'S:\Работа с клиентами\Клиенты\BigTV Rating\fts_check.csv'
col_names = ["Day", "vcId", "FTs", "serverTs", "locHost", "tnsTmsec", "Hits", "Uniqs"]
df_empty = pd.DataFrame()
with open(file) as fl:
    chunk_iter = pd.read_csv(fl, sep='\t', names=col_names, dtype={'serverTs': np.int32, 'FTs': np.int32}, chunksize = 100000)
    for chunk in chunk_iter:
        chunk['diff'] = np.array(chunk['serverTs'])-np.array(chunk['FTs'])
        chunk = chunk[chunk['diff'] > 180]
        df_empty = pd.concat([df_empty,chunk])  

但程序给了我一个错误:

  

TypeError Traceback(最近一次调用   最后)pandas / _libs / parsers.pyx in   pandas._libs.parsers.TextReader._convert_tokens()

     

TypeError:无法将数组从dtype('O')转换为dtype('int32')   根据规则'安全'

     

在处理上述异常期间,发生了另一个异常:

     

ValueError Traceback(最近一次调用   最后)in()         6 #dtype = {'serverTs':np.int32,'FTs':np.int32},         7 #chunk_iter = chunk_iter.astype({'serverTs':np.int32,'FTs':np.int32})   ----> 8为chunk_iter中的块:         9 #print(chunk [chunk ['FTs'] =='NaN'])        10 #chunk [['serverTs','FTs']] = chunk [['serverTs','FTs']]。astype('int32')

     

C:\ ProgramData \ Anaconda3 \ lib \ site-packages \ pandas \ io \ parsers.py in   下一个(个体经营)1040 def 下一个(个体经营):1041尝试:    - > 1042返回self.get_chunk()1043,除了StopIteration:1044 self.close()

     

C:\ ProgramData \ Anaconda3 \ lib \ site-packages \ pandas \ io \ parsers.py in   get_chunk(self,size)1104提高StopIteration
  1105 size = min(size,self.nrows - self._currow)    - > 1106 return self.read(nrows = size)1107 1108

     

C:\ ProgramData \ Anaconda3 \ lib \ site-packages \ pandas \ io \ parsers.py in   read(self,nrows)1067引发ValueError('skipfooter   不支持迭代')1068    - > 1069 ret = self._engine.read(nrows)1070 1071 if self.options.get('as_recarray'):

     

C:\ ProgramData \ Anaconda3 \ lib \ site-packages \ pandas \ io \ parsers.py in   读(self,nrows)1837 def read(self,nrows = None):1838
  尝试:    - > 1839 data = self._reader.read(nrows)1840除StopIteration:1841 if self._first_chunk:

     

pandas._libs.parsers.TextReader.read()中的pandas / _libs / parsers.pyx

     

pandas / _libs / parsers.pyx in   pandas._libs.parsers.TextReader._read_low_memory()

     

pandas / _libs / parsers.pyx in   pandas._libs.parsers.TextReader._read_rows()

     

pandas / _libs / parsers.pyx in   pandas._libs.parsers.TextReader._convert_column_data()

     

pandas / _libs / parsers.pyx in   pandas._libs.parsers.TextReader._convert_tokens()

     

ValueError:基数为10的int()的无效文字:'FTs'

我正在使用SQL查询从Hadoop获取数据,因此我检查了带字母的任何符号,但只有数字。此外,如果FT有任何不是数字的字符,它就不能出现在数据库中。 可能是什么问题?

1 个答案:

答案 0 :(得分:1)

这里的问题是你传递namesdtypes参数。这会导致header充当None。所以考虑一下:

In [1]: import pandas as pd, numpy as np

In [2]: dt={'serverTs': np.int32, 'FTs': np.int32}

In [3]: import io

In [4]: s = """FTs,serverTs
   ...: 0,1
   ...: 1,2
   ...: """

In [5]: pd.read_csv(io.StringIO(s))
Out[5]:
   FTs  serverTs
0    0         1
1    1         2

In [6]: pd.read_csv(io.StringIO(s), dtype=dt)
Out[6]:
   FTs  serverTs
0    0         1
1    1         2

工作正常。但是,如果我通过names

In [8]: names = 'FTs','serverTs'

In [9]: pd.read_csv(io.StringIO(s), dtype=dt, names=names)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()

TypeError: Cannot cast array from dtype('O') to dtype('int32') according to the rule 'safe'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-9-18dcd5477b7e> in <module>()
----> 1 pd.read_csv(io.StringIO(s), dtype=dt, names=names)

/Users/juan/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
    707                     skip_blank_lines=skip_blank_lines)
    708
--> 709         return _read(filepath_or_buffer, kwds)
    710
    711     parser_f.__name__ = name

/Users/juan/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    453
    454     try:
--> 455         data = parser.read(nrows)
    456     finally:
    457         parser.close()

/Users/juan/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
   1067                 raise ValueError('skipfooter not supported for iteration')
   1068
-> 1069         ret = self._engine.read(nrows)
   1070
   1071         if self.options.get('as_recarray'):

/Users/juan/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
   1837     def read(self, nrows=None):
   1838         try:
-> 1839             data = self._reader.read(nrows)
   1840         except StopIteration:
   1841             if self._first_chunk:

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()

ValueError: invalid literal for int() with base 10: 'FTs'

In [10]:

因此,一种解决方案是传递正确的标头索引:

In [10]: pd.read_csv(io.StringIO(s), dtype=dt, names=names, header=0)
Out[10]:
   FTs  serverTs
0    0         1
1    1         2

或者更好的是,根本不会通过namespandas无论如何会为你推断:

In [11]: pd.read_csv(io.StringIO(s), dtype=dt)
Out[11]:
   FTs  serverTs
0    0         1
1    1         2