Python - Pandas Series - Intraday Data - Daily Average

Question

I have a dataset (Pandas dataframe called df) that looks like:

             var1              var2   var3
0  2018-05-02 04:53:46         150785 2018-05-02 04:53:46
1  2018-05-02 06:38:58         150785 2018-05-02 06:38:58
2  2018-05-03 00:35:25         145510 2018-05-03 00:35:25
3  2018-05-03 06:33:53         145510 2018-05-03 06:33:53
4  2018-05-03 06:48:56         145510 2018-05-03 06:48:56

ie I have multiple intraday entries. How can I generate daily averages for this time series?

Answer 1

use pd.Grouper :

# df['var1'] = pd.to_datetime(df['var1'])
df = df.groupby(pd.Grouper(key='var1', freq='1d'))['var2'].mean().reset_index()
df

output:

    var1        var2
0   2018-05-02  150785
1   2018-05-03  145510

Answer 2

You can use Pandas' resample:

df_avg = df.resample('D').mean()

Eg.: Example Input Dataframe print(df) :

    DateTime                    S1          S2          S3
0   2020-07-15 16:27:01.221660  652.382636  661.053102  387.247039
1   2020-07-15 16:27:01.221660  953.427100  873.895507  397.911431
2   2020-07-16 16:27:01.221660  652.382636  661.053102  387.247039
3   2020-07-16 16:27:01.221660  953.427100  873.895507  397.911431

Code:

df = df.set_index("DateTime")
df["S1"] = df["S1"].astype(float)
df["S2"] = df["S2"].astype(float)
df["S3"] = df["S3"].astype(float)
df_avg = df.resample('D').mean()

Output:

            S1          S2          S3
DateTime            
2020-07-15  802.904868  767.474304  392.579235
2020-07-16  802.904868  767.474304  392.579235

This gives daily averages of S1, S2 and S3.