python - 在计算中无法将 dtype int 识别为 int

Question

我在 DataFrame 中有两列(serverTs、FTs)，它们是 Unix 时间格式的时间戳。在我的代码中，我需要从另一个中减去一个。当我这样做时，我收到一条错误消息，说我不能减去字符串。所以我将 serverT 和 FT 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])  

但是程序给我一个错误:

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) in () 6 #dtype={'serverTs': np.int32, 'FTs': np.int32}, 7 #chunk_iter = chunk_iter.astype({'serverTs': np.int32, 'FTs': np.int32}) ----> 8 for chunk in chunk_iter: 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 next(self) 1040 def next(self): 1041 try: -> 1042 return self.get_chunk() 1043 except StopIteration: 1044 self.close()

C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parsers.py in get_chunk(self, size) 1104 raise 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 raise ValueError('skipfooter not supported for iteration') 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 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'

我正在使用 SQL 查询从 Hadoop 中获取数据，因此我检查了任何带有字母的符号，但只有数字。此外，如果 FT 有任何不是数字的字符，它就不会出现在数据库中。可能是什么问题？

Answer 1

这里的问题是您传递了 names 和 dtypes 参数。这会导致 header 充当 None。所以考虑:

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，如果我传递 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]:

所以一种解决方案是传递正确的 header 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，根本不要传递 names，pandas 无论如何都会为您推断:

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