python dataframe converting multiple datetime formats

Just use to_datetime, it's man/woman enough to handle both those formats:

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:

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

It works for me. I had two formats in my column 'fecha_hechos'. The formats where:

• 2015/03/02
• 10/02/2010

what I did was:

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

I had two different date formats in the same column Temps, similar to the OP, which look like the following;

01.03.2017 00:00:00.000
01/03/2017 00:13

The timings are as follows for the two different code snippets;

v['Timestamp1'] = pd.to_datetime(v.Temps)

Took 25.5408718585968 seconds

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

In other words, if you have a small number of known formats for your datetimes, don't use to_datetime without a format!

You can create a new column :

test_df['col1'] = pd.Timestamp(test_df['col']).to_datetime()

and then drop col and rename col1.