[英]python dataframe converting multiple datetime formats
I have a pandas.dataframe like this ('col' column has two formats):我有一个这样的pandas.dataframe('col'列有两种格式):
col val
'12/1/2013' value1
'1/22/2014 12:00:01 AM' value2
'12/10/2013' value3
'12/31/2013' value4
I want to convert them into datetime, and I am considering using:我想将它们转换为日期时间,我正在考虑使用:
test_df['col']= test_df['col'].map(lambda x: datetime.strptime(x, '%m/%d/%Y'))
test_df['col']= test_df['col'].map(lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M %p'))
Obviously either of them works for the whole df.显然,它们中的任何一个都适用于整个 df。 I'm thinking about using try and except but didn't get any luck, any suggestions?我正在考虑使用 try 和 except 但没有任何运气,有什么建议吗?
Just use to_datetime
, it's man/woman enough to handle both those formats:只需使用to_datetime
,男人/女人就足以处理这两种格式:
In [4]:
df['col'] = pd.to_datetime(df['col'])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 0 to 3
Data columns (total 2 columns):
col 4 non-null datetime64[ns]
val 4 non-null object
dtypes: datetime64[ns](1), object(1)
memory usage: 96.0+ bytes
The df now looks likes this: df 现在看起来像这样:
In [5]:
df
Out[5]:
col val
0 2013-12-01 00:00:00 value1
1 2014-01-22 00:00:01 value2
2 2013-12-10 00:00:00 value3
3 2013-12-31 00:00:00 value4
I had two different date formats in the same column Temps
, similar to the OP, which look like the following;我在同一列Temps
有两种不同的日期格式,类似于 OP,如下所示;
01.03.2017 00:00:00.000
01/03/2017 00:13
The timings are as follows for the two different code snippets;两个不同代码片段的时间如下;
v['Timestamp1'] = pd.to_datetime(v.Temps)
Took 25.5408718585968 seconds耗时 25.5408718585968 秒
v['Timestamp'] = pd.to_datetime(v.Temps, format='%d/%m/%Y %H:%M', errors='coerce')
mask = v.Timestamp.isnull()
v.loc[mask, 'Timestamp'] = pd.to_datetime(v[mask]['Temps'], format='%d.%m.%Y %H:%M:%S.%f',
errors='coerce')
Took 0.2923243045806885 seconds花了 0.2923243045806885 秒
In other words, if you have a small number of known formats for your datetimes, don't use to_datetime without a format!换句话说,如果您的日期时间有少量已知格式,请不要在没有格式的情况下使用 to_datetime!
You can create a new column :您可以创建一个新列:
test_df['col1'] = pd.Timestamp(test_df['col']).to_datetime()
and then drop col and rename col1.然后删除 col 并重命名 col1。
It works for me.这个对我有用。 I had two formats in my column 'fecha_hechos'.我的专栏“fecha_hechos”中有两种格式。 The formats where:其中的格式:
what I did was:我所做的是:
carpetas_cdmx['Timestamp'] = pd.to_datetime(carpetas_cdmx.fecha_hechos, format='%Y/%m/%d %H:%M:%S', errors='coerce')
mask = carpetas_cdmx.Timestamp.isnull()
carpetas_cdmx.loc[mask, 'Timestamp'] = pd.to_datetime(carpetas_cdmx[mask]['fecha_hechos'], format='%d/%m/%Y %H:%M',errors='coerce')
were: carpetas_cdmx
is my DataFrame and fecha_hechos
the column with my formats是: carpetas_cdmx
是我的 DataFrame 和fecha_hechos
列与我的格式
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