pandas python数据帧转换多种日期时间格式
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python dataframe converting multiple datetime formats
提问by datadatadata
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. I'm thinking about using try and except but didn't get any luck, any suggestions?
显然，它们中的任何一个都适用于整个 df。我正在考虑使用 try 和 except 但没有任何运气，有什么建议吗？
采纳答案by EdChum
Just use to_datetime, it's man/woman enough to handle both those formats:
只需使用to_datetime，就足以处理这两种格式的男人/女人：
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
回答by morganics
I had two different date formats in the same column Temps, similar to the OP, which look like the following;
我在同一列中有两种不同的日期格式Temps，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序如下；
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！
回答by Alex
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。
回答by Joselin Ceron
It works for me. I had two formats in my column 'fecha_hechos'. The formats where:
这个对我有用。我的专栏“fecha_hechos”中有两种格式。其中的格式：
- 2015/03/02
- 10/02/2010
- 2015/03/02
- 10/02/2010
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_cdmxis my DataFrame
and fecha_hechosthe column with my formats
是：carpetas_cdmx是我的 DataFrame 和fecha_hechos带有我的格式的列
