I'm trying to resample this Timestamp column of this Dataframe:
Transit.head():
Timestamp Plate Gate
0 2013-11-01 21:02:17 4f5716dcd615f21f658229a8570483a8 65
1 2013-11-01 16:12:39 0abba297ac142f63c604b3989d0ce980 64
2 2013-11-01 11:06:10 faafae756ce1df66f34f80479d69411d 57
And Here's What I've Done:
Transit.drop_duplicates(inplace=True)
Transit.Timestamp = pd.to_datetime(Transit.Timestamp)
Transit['Timestamp'].resample('1H').pad()
But I got This Error:
Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'
Any Suggestion Would Be Much Appreciated.
Apparently, sometimes your df_orders.index is a DatetimeIndex and sometimes it is an Int64Index. You should add a check like if df_orders.index.dtype != pd.DatetimeIndex ....
Use existing date column as index If your dataframe already has a date column, you can use use it as an index, of type DatetimeIndex:
For more examples on how to manipulate date and time values in pandas dataframes, see Pandas Dataframe Examples: Manipulating Date and Time If your dataframe already has a date column, you can use use it as an index, of type DatetimeIndex: df: original dataframe. Note that added for the missing periods.
Create DatetimeIndex
by DataFrame.set_index
- solution for upsampling and downsampling:
df = Transit.set_index('Timestamp').resample('1H').pad()
print (df)
Plate Gate
Timestamp
2013-11-01 11:00:00 NaN NaN
2013-11-01 12:00:00 faafae756ce1df66f34f80479d69411d 57.0
2013-11-01 13:00:00 faafae756ce1df66f34f80479d69411d 57.0
2013-11-01 14:00:00 faafae756ce1df66f34f80479d69411d 57.0
2013-11-01 15:00:00 faafae756ce1df66f34f80479d69411d 57.0
2013-11-01 16:00:00 faafae756ce1df66f34f80479d69411d 57.0
2013-11-01 17:00:00 0abba297ac142f63c604b3989d0ce980 64.0
2013-11-01 18:00:00 0abba297ac142f63c604b3989d0ce980 64.0
2013-11-01 19:00:00 0abba297ac142f63c604b3989d0ce980 64.0
2013-11-01 20:00:00 0abba297ac142f63c604b3989d0ce980 64.0
2013-11-01 21:00:00 0abba297ac142f63c604b3989d0ce980 64.0
For downsampling is possible use parameter on
:
df = Transit.resample('D', on='Timestamp').mean()
print (df)
Gate
Timestamp
2013-11-01 62
EDIT: For remove all rows with duplicated Timestamp
add parameter subset
to DataFrame.drop_duplicates
:
Transit.drop_duplicates(subset=['Timestamp'], inplace=True)
Transit.Timestamp = pd.to_datetime(Transit.Timestamp)
df = Transit.set_index('Timestamp').resample('1H').pad()
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