Question :
I need to merge 2 pandas dataframes together on dates, but they currently have different date types. 1 is timestamp (imported from excel) and the other is datetime.date.
Any advice?
I’ve tried pd.to_datetime().date but this only works on a single item(e.g. df.ix[0,0]), it won’t let me apply to the entire series (e.g. df['mydates']) or the dataframe.
Answer #1:
I got some help from a colleague.
This appears to solve the problem posted above
pd.to_datetime(df['mydates']).apply(lambda x: x.date())
Answer #2:
Much simpler than above:
df['mydates'].dt.date
Answer #3:
For me this works:
from datetime import datetime
df[ts] = [datetime.fromtimestamp(x) for x in df[ts]]
Answer #4:
Another question was marked as dupe pointing to this, but it didn’t include this answer, which seems the most straightforward (perhaps this method did not yet exist when this question was posted/answered):
The pandas doc shows a pandas.Timestamp.to_pydatetime method to “Convert a Timestamp object to a native Python datetime object”.
Answer #5:
If you need the datetime.date objects… then get them through with the .date attribute of the Timestamp
pd.to_datetime(df['mydates']).date
Answer #6:
I found the following to be the most effective, when I ran into a similar issue. For instance, with the dataframe df with a series of timestmaps in column ts.
df.ts.apply(lambda x: pd.datetime.fromtimestamp(x).date())
This makes the conversion, you can leave out the .date() suffix for datetimes. Then to alter the column on the dataframe. Like so…
df.loc[:, 'ts'] = df.ts.apply(lambda x: pd.datetime.fromtimestamp(x).date())
Answer #7:
Assume time column is in timestamp integer msec format
1 day = 86400000 ms
Here you go:
day_divider = 86400000
df['time'] = df['time'].values.astype(dtype='datetime64[ms]') # for msec format
df['time'] = (df['time']/day_divider).values.astype(dtype='datetime64[D]') # for day format
