Python pandas integer YYYYMMDD to datetime

Question

Apologies in advance for this, but after two hours of searching and trying I cannot get the right answer here. I have a data frame, populated via pandas io sql.read_frame(). The column that is proving to be too much for me is of dtype int64. The integers is of the format YYYYMMDD. for example 20070530 - 30th of may 2007. I have tried a range of approaches, the most obvious being;

pd.to_datetime(dt['Date']) and pd.to_datetime(str(dt['Date']))

with multiple variations on the functions different parameters.

The result has been, at best, that the date interpreted as being the time. The date is set to 1970-01-01 - outcome as per above example 1970-01-01 00:00:00.020070530

I also tried various .map() functions found in simular posts.

I have noticed that according to np.date_range() can interpret string values of the format YYYYMMDD, but that is the closest I have come to seeing a solution.

If anyone has an answer, I would be very greatful!

EDIT: In view of the answer from Ed Chum, the problem is most likely related to encoding. rep() on a subset of the dataFrame yields:

OrdNo LstInvDt 0
9 20070620 1
11 20070830 2
19 20070719 3
21 20070719 4
23 20070719 5
26 20070911 7
29 20070918 8
31 0070816 9
34 20070925 10

This is when LstInvDt is dtype int64.

question from:https://stackoverflow.com/questions/27506367/python-pandas-integer-yyyymmdd-to-datetime

Answer 1

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