Python Pandas Group by date using datetime data

Question

I have a column Date_Time that I wish to groupby date time without creating a new column. Is this possible the current code I have does not work.

df = pd.groupby(df,by=[df['Date_Time'].date()]) 

Answer 1

You can use groupby by dates of column Date_Time by dt.date:

df = df.groupby([df['Date_Time'].dt.date]).mean() 

Sample:

df = pd.DataFrame({'Date_Time': pd.date_range('10/1/2001 10:00:00', periods=3, freq='10H'),                    'B':[4,5,6]})  print (df)    B           Date_Time 0  4 2001-10-01 10:00:00 1  5 2001-10-01 20:00:00 2  6 2001-10-02 06:00:00  print (df['Date_Time'].dt.date) 0    2001-10-01 1    2001-10-01 2    2001-10-02 Name: Date_Time, dtype: object  df = df.groupby([df['Date_Time'].dt.date])['B'].mean() print(df) Date_Time 2001-10-01    4.5 2001-10-02    6.0 Name: B, dtype: float64 

Another solution with resample:

df = df.set_index('Date_Time').resample('D')['B'].mean()  print(df) Date_Time 2001-10-01    4.5 2001-10-02    6.0 Freq: D, Name: B, dtype: float64 

Answer 2

resample

df.resample('D', on='Date_Time').mean()                B Date_Time       2001-10-01  4.5 2001-10-02  6.0 

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Grouper

As suggested by @JosephCottam

df.set_index('Date_Time').groupby(pd.Grouper(freq='D')).mean()                B Date_Time       2001-10-01  4.5 2001-10-02  6.0 

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Deprecated uses of TimeGrouper

You can set the index to be 'Date_Time' and use pd.TimeGrouper

df.set_index('Date_Time').groupby(pd.TimeGrouper('D')).mean().dropna()                B Date_Time       2001-10-01  4.5 2001-10-02  6.0