使用 pandas to_datetime时如何定义格式? [英] How to define format when use pandas to_datetime?

问题描述

我想基于具有以下格式的testresult.csv文件绘制RESULT与TIME的关系图，而我很难正确定义TIME列的数据类型.

I want to plot RESULT vs TIME based on a testresult.csv file that has following format, and I have trouble to get the TIME column's datatype defined properly.

TIME,RESULT  
03/24/2016 12:27:11 AM,2  
03/24/2016 12:28:41 AM,76  
03/24/2016 12:37:23 AM,19  
03/24/2016 12:38:44 AM,68  
03/24/2016 12:42:02 AM,44  
...

要读取csv文件，这是我编写的代码: raw_df = pd.read_csv('testresult.csv', index_col=None, parse_dates=['TIME'], infer_datetime_format=True)
这段代码可以运行，但是速度非常慢，并且我认为infer_datetime_format需要时间.因此，我尝试首先默认读取csv，然后使用to_datetime()将对象dtype'TIME'转换为datetime dtype，希望通过定义格式可以加快速度.

To read the csv file, this is the code I wrote: raw_df = pd.read_csv('testresult.csv', index_col=None, parse_dates=['TIME'], infer_datetime_format=True)
This code works, but it is extremely slow, and I assume that the infer_datetime_format takes time. So I tried to read in the csv by default first, and then convert the object dtype 'TIME' to datetime dtype by using to_datetime(), and I hope by defining the format, it might expedite the speed.

raw_df =  pd.read_csv('testresult.csv')
raw_df.loc['NEWTIME'] = pd.to_datetiem(raw_df['TIME'], format='%m/%d%Y %-I%M%S %p')

此代码报错:

"ValueError: '-' is a bad directive in format '%m/%d%Y %-I%M%S %p'"

请提出任何建议或提示.

Please any suggestion or hint will be helpful.

谢谢

推荐答案

您传递的格式无效. %和I之间的破折号不应存在.

The format you are passing is invalid. The dash between the % and the I is not supposed to be there.

df['TIME'] = pd.to_datetime(df['TIME'], format="%m/%d/%Y %I:%M:%S %p")

这会将您的TIME列转换为日期时间.

This will convert your TIME column to a datetime.

或者，您可以调整read_csv调用以执行此操作:

Alternatively, you can adjust your read_csv call to do this:

pd.read_csv('testresult.csv', parse_dates=['TIME'], 
    date_parser=lambda x: pd.to_datetime(x, format='%m/%d/%Y %I:%M:%S %p'))

同样，它使用适当的格式，没有多余的-，但是它也将格式传递给

Again, this uses the appropriate format with out the extra -, but it also passes in the format to the date_parser parameter instead of having pandas attempt to guess it with the infer_datetime_format parameter.

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