Question

我正在尝试加载一个格式如下的csv文件：

                  40010  40015  40020  40025  40030  40035  40040  40045  
2008-11-03 00:00    786    212    779    227    220    131    680   1006   
2008-11-03 00:03    760    200    765    234    225    133    694   1063   
2008-11-03 00:06    757    205    769    237    230    136    726   1051   
2008-11-03 00:09    781    207    765    240    235    137    711   1040   
2008-11-03 00:12    759    203    751    232    225    134    717   1088
...

该文件以逗号分隔。这里没有固定的宽度。

我希望行索引是日期时间，所以这是我在加载文件时正在做的事情：

def dateparse (timestamp):   
    return datetime.datetime.strptime(timestamp, '%Y-%m-%d %I:%M')

global_data_train = pd.read_csv('RTAHistorical.csv', sep=",",parse_dates=True, date_parser=dateparse, header=0, index_col=0, skip_blank_lines = True, engine='python')

但我收到以下错误：

TypeError: strptime() argument 1 must be str, not numpy.ndarray

正如我看到some people成功使用相同的方法，我不太明白这个错误。

我做错了什么？

Answer 1

对我而言，将格式更改为%Y-%m-%d %H:%M：

def dateparse (timestamp):   
    return pd.datetime.strptime(timestamp, '%Y-%m-%d %H:%M')

样品：

import pandas as pd
from pandas.compat import StringIO

temp=u"""40010,40015,40020,40025,40030,40035,40040,40045
2008-11-03 00:00,786,212,779,227,220,131,680,1006
2008-11-03 00:03,760,200,765,234,225,133,694,1063
2008-11-03 00:06,757,205,769,237,230,136,726,1051
2008-11-03 00:09,781,207,765,240,235,137,711,1040
2008-11-03 00:12,759,203,751,232,225,134,717,1088"""
#after testing replace StringIO(temp) to filename
def dateparse (timestamp):   
    return pd.datetime.strptime(timestamp, '%Y-%m-%d %H:%M')

global_data_train = pd.read_csv(StringIO(temp), 
                                sep=",", 
                                parse_dates=True, 
                                date_parser=dateparse, 
                                header=0, 
                                index_col=0, 
                                skip_blank_lines = True, 
                                engine='python')
print (global_data_train)
                     40010  40015  40020  40025  40030  40035  40040  40045
2008-11-03 00:00:00    786    212    779    227    220    131    680   1006
2008-11-03 00:03:00    760    200    765    234    225    133    694   1063
2008-11-03 00:06:00    757    205    769    237    230    136    726   1051
2008-11-03 00:09:00    781    207    765    240    235    137    711   1040
2008-11-03 00:12:00    759    203    751    232    225    134    717   1088

print (global_data_train.index)
DatetimeIndex(['2008-11-03 00:00:00', '2008-11-03 00:03:00',
               '2008-11-03 00:06:00', '2008-11-03 00:09:00',
               '2008-11-03 00:12:00'],
              dtype='datetime64[ns]', freq=None)

也可以省略date_parser=dateparse。

import pandas as pd
from pandas.compat import StringIO

temp=u"""40010,40015,40020,40025,40030,40035,40040,40045
2008-11-03 00:00,786,212,779,227,220,131,680,1006
2008-11-03 00:03,760,200,765,234,225,133,694,1063
2008-11-03 00:06,757,205,769,237,230,136,726,1051
2008-11-03 00:09,781,207,765,240,235,137,711,1040
2008-11-03 00:12,759,203,751,232,225,134,717,1088"""
#after testing replace StringIO(temp) to filename
global_data_train = pd.read_csv(StringIO(temp), 
                                parse_dates=True, 
                                skip_blank_lines = True)
print (global_data_train)
                     40010  40015  40020  40025  40030  40035  40040  40045
2008-11-03 00:00:00    786    212    779    227    220    131    680   1006
2008-11-03 00:03:00    760    200    765    234    225    133    694   1063
2008-11-03 00:06:00    757    205    769    237    230    136    726   1051
2008-11-03 00:09:00    781    207    765    240    235    137    711   1040
2008-11-03 00:12:00    759    203    751    232    225    134    717   1088

print (global_data_train.index)
DatetimeIndex(['2008-11-03 00:00:00', '2008-11-03 00:03:00',
               '2008-11-03 00:06:00', '2008-11-03 00:09:00',
               '2008-11-03 00:12:00'],
              dtype='datetime64[ns]', freq=None)

使用pandas read_csv解析时间戳时出错

1 个答案: