重新采样每日数据以获得每月数据框架？

Question

I have a daily dataframe which I am trying to resample to get the monthly Open High Low Close .

daily_df

            Open   High    Low   Last  Close

Date                                         

2010-01-04  55.15  57.55  54.55  57.50  57.30

2010-01-05  59.70  59.70  57.45  57.90  58.00

2010-01-06  60.30  60.30  57.10  57.55  57.50

2010-01-07  60.25  60.25  57.35  58.85  58.90

2010-01-08  59.40  59.95  56.90  57.30  57.65

2010-01-11  57.30  57.95  56.00  56.25  56.25

2010-01-12  56.25  56.80  53.80  54.25  54.10

2010-01-13  54.00  55.00  52.15  54.90  54.85

2010-01-14  55.45  55.70  54.15  54.30  54.35

2010-01-15  54.60  55.30  54.00  54.30  54.30

2010-01-18  53.90  55.20  53.85  54.35  54.40

2010-01-19  54.60  55.20  53.55  53.65  53.75

2010-01-20  54.40  54.40  53.45  53.60  53.70

2010-01-21  53.85  53.85  51.95  52.10  52.25

2010-01-22  51.80  52.85  50.30  51.85  52.00

2010-01-25  52.50  52.50  50.50  50.70  50.85

2010-01-27  51.25  51.25  47.80  47.90  48.20

2010-01-28  48.55  50.50  47.10  47.45  47.35

2010-01-29  47.45  52.15  45.60  51.80  51.70

2010-02-01  51.80  52.40  50.50  51.50  51.45

2010-02-02  53.25  54.10  51.40  51.80  51.80

2010-02-03  51.60  52.90  51.50  51.85  51.95

I have tried:

df2 = df_daily.resample('M',convention='end').asfreq()

This gives me a dataframe with only the closing values ie 30th values of open high low close if the date is exactly end of month otherwise NaN .

df2=df_daily.resample('M').mean()

This results in values which I assume are the average/mean of the Open High Low Close values in a particular month.

I am looking to get the Open of the month from the first day of the month where price is available, high to be the highest value during that month, low to be the lowest of the month, Close to the actual close.

I believe I can do this in pandas in a different way using min max but just wondering if resampling can be used to do this.

Expected df

         Open   High    Low   Close

Date                                         

2010-01-29  55.15  60.3  45.6  51.7

Thanks

Answer 1

resample by month considers last day of month irrespective of column dates.

df2 = df_daily.resample('M').agg({'Open':'first', 'High':'max', 
                                      'Low': 'min', 'Close':'last'})

Output:

            Open    High    Low    Close
Date                
2010-01-31  55.15   60.3    45.6    51.70
2010-02-28  51.80   54.1    50.5    51.95

You can change the index to last day present in the column:

df2 = df_daily.resample('M').agg({'Open':'first', 'High':'max', 
                                      'Low': 'min', 'Close':'last'})

idx = df_daily.reset_index().groupby(df_daily.index.to_period('M'))['Date'].idxmax()
df2.index = df_daily.iloc[idx].index
print(df2)

Output:

            Open    High    Low    Close
Date                
2010-01-29  55.15   60.3    45.6    51.70
2010-02-03  51.80   54.1    50.5    51.95

If you only want to groupby year and month use:

df3 = df_daily.groupby([df_daily.index.year,df_daily.index.month]).agg({'Open':'first',
                         'High':'max', 'Low': 'min', 'Close':'last'})

df3.index.names= ['Year', 'Month']
print(df3)

Output:

                Open    High    Low     Close
Year    Month               
2010      1     55.15   60.3    45.6    51.70
          2     51.80   54.1    50.5    51.95