How do I convert timestamp to datetime.date in pandas dataframe?
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.
Answers:
If you need the datetime.date objects… then get them through with the .date attribute of the Timestamp
pd.to_datetime(df['mydates']).date
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())
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”.
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())
For me this works:
from datetime import datetime
df[ts] = [datetime.fromtimestamp(x) for x in df[ts]]
Much simpler than above:
df['mydates'].dt.date
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
I was trying to convert a timestamp column to date/time, here is what I came up with:
df['Timestamp'] = df['Timestamp'].apply(lambda timestamp: datetime.fromtimestamp(timestamp))
You have to know if the unit of the Unix timestamp is in seconds or milliseconds. Assume that it is in seconds and assume that you have the following pandas
print(df.head())
And you get:
timestamp XETHZUSD
0 1609459200 730.85
1 1609545600 775.01
2 1609632000 979.86
3 1609718400 1042.52
4 1609804800 1103.41
You can convert the timestamp to datetime as follows:
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
print(df.head())
And we get:
timestamp XETHZUSD
0 2021-01-01 730.85
1 2021-01-02 775.01
2 2021-01-03 979.86
3 2021-01-04 1042.52
4 2021-01-05 1103.41
If the Unix timestamp was in milliseconds, then you should have typed
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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.
If you need the datetime.date objects… then get them through with the .date attribute of the Timestamp
pd.to_datetime(df['mydates']).date
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())
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”.
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())
For me this works:
from datetime import datetime
df[ts] = [datetime.fromtimestamp(x) for x in df[ts]]
Much simpler than above:
df['mydates'].dt.date
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
I was trying to convert a timestamp column to date/time, here is what I came up with:
df['Timestamp'] = df['Timestamp'].apply(lambda timestamp: datetime.fromtimestamp(timestamp))
You have to know if the unit of the Unix timestamp is in seconds or milliseconds. Assume that it is in seconds and assume that you have the following pandas
print(df.head())
And you get:
timestamp XETHZUSD
0 1609459200 730.85
1 1609545600 775.01
2 1609632000 979.86
3 1609718400 1042.52
4 1609804800 1103.41
You can convert the timestamp to datetime as follows:
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
print(df.head())
And we get:
timestamp XETHZUSD
0 2021-01-01 730.85
1 2021-01-02 775.01
2 2021-01-03 979.86
3 2021-01-04 1042.52
4 2021-01-05 1103.41
If the Unix timestamp was in milliseconds, then you should have typed
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')