python – pandas read_csv()输入本地日期时间字符串,tz_convert输入UTC

我正在使用pandas-0.8rc2来读取具有两列本地化的输入CSV
datetime字符串缺少UTC偏移信息,需要数据帧系列
正确转换为UTC.

我一直在尝试使用变通方法来缓解时间戳列的事实
代表索引,它们是数据. tz_localize和tz_convert显然有效
仅限系列/数据框的索引,而不是列.我非常想
学习更好的方法,而不是以下代码：

# test.py
import pandas

# input.csv:
# starting,ending,measure
# 2012-06-21 00:00,2012-06-23 07:00,77
# 2012-06-23 07:00,2012-06-23 16:30,65
# 2012-06-23 16:30,2012-06-25 08:00,77
# 2012-06-25 08:00,2012-06-26 12:00,0
# 2012-06-26 12:00,2012-06-27 08:00,77

df = pandas.read_csv('input.csv', parse_dates=[0,1])
print df

ser_starting = df.starting
ser_starting.index = ser_starting.values
ser_starting = ser_starting.tz_localize('US/Eastern')
ser_starting = ser_starting.tz_convert('UTC')

ser_ending = df.ending
ser_ending.index = ser_ending.values
ser_ending = ser_ending.tz_localize('US/Eastern')
ser_ending = ser_ending.tz_convert('UTC')

df.starting = ser_starting.index
print df
df.ending = ser_ending.index
print df

其次,代码遇到了一些奇怪的行为.它会更改时间戳
第二次分配的数据返回数据帧,无论顺序是什么
df.starting或df.ending：

$python test.py 
              starting               ending  measure
0  2012-06-21 00:00:00  2012-06-23 07:00:00       77
1  2012-06-23 07:00:00  2012-06-23 16:30:00       65
2  2012-06-23 16:30:00  2012-06-25 08:00:00       77
3  2012-06-25 08:00:00  2012-06-26 12:00:00        0
4  2012-06-26 12:00:00  2012-06-27 08:00:00       77
             starting               ending  measure
0 2012-06-21 04:00:00  2012-06-23 07:00:00       77
1 2012-06-23 11:00:00  2012-06-23 16:30:00       65
2 2012-06-23 20:30:00  2012-06-25 08:00:00       77
3 2012-06-25 12:00:00  2012-06-26 12:00:00        0
4 2012-06-26 16:00:00  2012-06-27 08:00:00       77
Traceback (most recent call last):
  File "test.py", line 28, in <module>
    print df
  File "/path/to/lib/python2.7/site-packages/pandas/core/frame.py", line 572, in __repr__
    if self._need_info_repr_():
  File "/path/to/lib/python2.7/site-packages/pandas/core/frame.py", line 560, in _need_info_repr_
    self.to_string(buf=buf)
  File "/path/to/lib/python2.7/site-packages/pandas/core/frame.py", line 1207, in to_string
    formatter.to_string(force_unicode=force_unicode)
  File "/path/to/lib/python2.7/site-packages/pandas/core/format.py", line 200, in to_string
    fmt_values = self._format_col(i)
  File "/path/to/lib/python2.7/site-packages/pandas/core/format.py", line 242, in _format_col
    space=self.col_space)
  File "/path/to/lib/python2.7/site-packages/pandas/core/format.py", line 462, in format_array
    return fmt_obj.get_result()
  File "/path/to/lib/python2.7/site-packages/pandas/core/format.py", line 589, in get_result
    fmt_values = [formatter(x) for x in self.values]
  File "/path/to/lib/python2.7/site-packages/pandas/core/format.py", line 597, in _format_datetime64
    base = stamp.strftime('%Y-%m-%d %H:%M:%s')
ValueError: year=1768 is before 1900; the datetime strftime() methods require year >= 1900

打印陈述只是为了证明问题.不正确的值
如果我避免使用repr和其他调用方法,将毫无例外地执行
strftime的.

奇怪的是,如果我继续调用df.{starting,ending}赋值
在repl,我通常最终得到一个正确的数据帧,时间戳：

In [151]: df
Out[151]: 
             starting              ending  measure
0 2012-06-21 04:00:00 2012-06-23 11:00:00  77
1 2012-06-23 11:00:00 2012-06-23 20:30:00  65
2 2012-06-23 20:30:00 2012-06-25 12:00:00  77
3 2012-06-25 12:00:00 2012-06-26 16:00:00   0
4 2012-06-26 16:00:00 2012-06-27 12:00:00  77

这是不可重复的,AFAICT,我无法描述确切的呼叫顺序
超过了上面的ValueError,但确实如此

如果我遇到一个bug,我会很感激.
或者,如果这是不受支持的API使用.

如上所述,我宁愿学习更好地使用pandas API
避免这样做.

解决方法:

它似乎潜伏在这里,所以我在这里创建了一个问题,很快就会看到并让你知道：

https://github.com/pydata/pandas/issues/1518

编辑：您遇到的错误已得到修复.我现在也要解决1900年前的显示问题.