Python pandas integer YYYYMMDD to datetime

to_datetime accepts a format string:

In [92]:

t = 20070530
pd.to_datetime(str(t), format="%Y%m%d")
Out[92]:
Timestamp('2007-05-30 00:00:00')

example:

In [94]:

t = 20070530
df = pd.DataFrame({'date':[t]*10})
df
Out[94]:
       date
0  20070530
1  20070530
2  20070530
3  20070530
4  20070530
5  20070530
6  20070530
7  20070530
8  20070530
9  20070530
In [98]:

df['DateTime'] = df['date'].apply(lambda x: pd.to_datetime(str(x), format="%Y%m%d"))
df
Out[98]:
       date   DateTime
0  20070530 2007-05-30
1  20070530 2007-05-30
2  20070530 2007-05-30
3  20070530 2007-05-30
4  20070530 2007-05-30
5  20070530 2007-05-30
6  20070530 2007-05-30
7  20070530 2007-05-30
8  20070530 2007-05-30
9  20070530 2007-05-30
In [99]:

df.dtypes
Out[99]:
date                 int64
DateTime    datetime64[ns]
dtype: object

EDIT

Actually it’s quicker to convert the type to string and then convert the entire series to a datetime rather than calling apply on every value:

In [102]:

df['DateTime'] = pd.to_datetime(df['date'].astype(str), format="%Y%m%d")
df
Out[102]:
       date   DateTime
0  20070530 2007-05-30
1  20070530 2007-05-30
2  20070530 2007-05-30
3  20070530 2007-05-30
4  20070530 2007-05-30
5  20070530 2007-05-30
6  20070530 2007-05-30
7  20070530 2007-05-30
8  20070530 2007-05-30
9  20070530 2007-05-30

timings

In [104]:

%timeit df['date'].apply(lambda x: pd.to_datetime(str(x), format="%Y%m%d"))

100 loops, best of 3: 2.55 ms per loop
In [105]:

%timeit pd.to_datetime(df['date'].astype(str), format="%Y%m%d")
1000 loops, best of 3: 396 µs per loop