pandas 避免熊猫中 pd.to_datetime 的错误
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avoiding error from pd.to_datetime in pandas
提问by Satya
I have a huge dataframe more than 100 mln rows. In that I have a date columns, unfortunately have improper formatted (mixed) date strings.
我有一个超过 1 亿行的巨大数据框。因为我有一个日期列，不幸的是格式不正确（混合）日期字符串。
Now I did convert it to datetime by:
现在我确实通过以下方式将其转换为日期时间：
df['TRX_DATE'] = pd.to_datetime(df['TRX_DATE'],coerce=True)
# without any error
# Now i want to calculate week day from that date columns
df['day_type'] = [x.strftime('%A') for x in d['TRX_DATE']]
###ValueError: month out of range
If it would a single field I can manage with dateutil parser. But in this case I am getting out of idea, how to handle that.
如果是单个字段，我可以使用 dateutil 解析器进行管理。但在这种情况下，我不知道如何处理。
Just intersted, if the week conversion line can have something like if anything getting out of range place a default...
只是感兴趣，如果周转换线可以有类似的东西，如果任何超出范围的地方设置默认值...
Have the idea but as a newbie. Don't have that much experience to do that.
有想法，但作为一个新手。没有那么多经验去做那件事。
It would be great help if someone can give a code line to handle that.
如果有人可以提供代码行来处理它，那将是非常有帮助的。
回答by jezrael
I think you can parse to_datetimewith parameter errors='coerce'and then use strftimefor converting to weekday as locale's full name:
我认为您可以to_datetime使用参数进行解析，errors='coerce'然后strftime用于转换为工作日作为语言环境的全名：
print df
TRX_DATE some value
0 2010-08-15 13:00:00 27.065
1 2010-08-16 13:10:00 25.610
2 2010-08-17 02:30:00 17.000
3 2010-06-18 02:40:00 17.015
4 2010-18-19 02:50:00 16.910
df['TRX_DATE'] = pd.to_datetime(df['TRX_DATE'],errors='coerce')
df['day_type'] = df['TRX_DATE'].dt.strftime('%A')
print df
TRX_DATE some value day_type
0 2010-08-15 13:00:00 27.065 Sunday
1 2010-08-16 13:10:00 25.610 Monday
2 2010-08-17 02:30:00 17.000 Tuesday
3 2010-06-18 02:40:00 17.015 Friday
4 NaT 16.910 NaT
回答by PhilChang
[x.strftime('%A') for x in df['TRX_DATE'] if not isinstance(x, pandas.tslib.NaTType)]
