Question

我正在使用pd.read_csv读取3个不同的数据集。数据的一列是以秒为单位的时间，我想使用为pd.read_csv date_parser参数创建的函数。当所有数据都是整数时，它可以正常工作。但是，当我有字符串或浮点数时，我做的函数不起作用。我认为问题出在我的函数的datetime.datetime.fromtimestamp（float（time_in_secs）部分。是否有人知道如何使它适用于我的所有数据集。我完全被困住了。这三个不同的数据集看起来一样。

数据集1

555、1404803485、800

555、1408906759、900

数据集2

231，1404803485，通过

231，1404803490，失败

数据集3

16010925、1403890894、40.5819880696

16010925、1903929273、40.5819880696

def dateparse(time_in_secs):

if isinstance(time_in_secs, str):
    if time_in_secs == '\\N':
        time_in_secs = 0

tm = datetime.datetime.fromtimestamp(float(time_in_secs))
tm = tm - datetime.timedelta(
    minutes=tm.minute % 10, seconds=tm.second, microseconds=tm.microsecond)
return tm


pd.read_csv('dataset_here.csv',
           delimiter=',', index_col=[0,1], parse_dates=['Timestamp'], 
                date_parser=dateparse, names=['Serial', 'Timestamp', 'result'])

Answer 1

我相信需要将所有字符串的时间都转换为0，因为float的解决方案效果很好：

def dateparse(time_in_secs):

    if isinstance(time_in_secs, str):
        #https://stackoverflow.com/a/45372194
        #time_in_secs = 86400
        time_in_secs = 0

    #print (time_in_secs)
    tm = datetime.datetime.fromtimestamp(float(time_in_secs))
    tm = tm - datetime.timedelta(
    minutes=tm.minute % 10, seconds=tm.second, microseconds=tm.microsecond)
    return tm

更多一般解决方案-尝试将值转换为浮点数，如果不可能，请分配默认值：

def dateparse(time_in_secs):

    if isinstance(time_in_secs, str):
        try:
            time_in_secs = float(time_in_secs)
        except ValueError:
            #https://stackoverflow.com/a/45372194
            #time_in_secs = 86400
            time_in_secs = 0

    #print (time_in_secs)
    tm = datetime.datetime.fromtimestamp(float(time_in_secs))
    tm = tm - datetime.timedelta(
    minutes=tm.minute % 10, seconds=tm.second, microseconds=tm.microsecond)
    return tm

示例：在Windows下测试：

import pandas as pd
import datetime

def dateparse(time_in_secs):

    if isinstance(time_in_secs, str):
        try:
            time_in_secs = float(time_in_secs)
        except ValueError:
            #https://stackoverflow.com/a/45372194
            #time_in_secs = 0
            time_in_secs = 86400

    print (time_in_secs)
    tm = datetime.datetime.fromtimestamp(float(time_in_secs))
    tm = tm - datetime.timedelta(
    minutes=tm.minute % 10, seconds=tm.second, microseconds=tm.microsecond)
    return tm

temp=u"""16010925,test,40.5819880696
16010925,1903929273,40.5819880696"""
#after testing replace 'pd.compat.StringIO(temp)' to 'filename.csv'
df = pd.read_csv(pd.compat.StringIO(temp), index_col=[0,1], parse_dates=['Timestamp'], 
                date_parser=dateparse, names=['Serial', 'Timestamp', 'result'])

print (df)
                                 result
Serial   Timestamp                     
16010925 1970-01-02 01:00:00  40.581988
         2030-05-02 07:10:00  40.581988

print (df.index.get_level_values(1))
DatetimeIndex(['1970-01-02 01:00:00', '2030-05-02 07:10:00'], 
              dtype='datetime64[ns]', name='Timestamp', freq=None)

date_parser和read_csv的功能不起作用

1 个答案: