I'm reading minute data from a sqlite db into a dataframe where the index is a datetime object:
open high low close volume trade_count vwap ticker
index
2022-09-13 04:26:00 00:00 163.50 163.50 163.50 163.50 298.0 12.0 163.503255 AAPL
2022-09-13 04:45:00 00:00 163.50 163.50 163.50 163.50 727.0 1.0 163.500000 AAPL
2022-09-13 05:16:00 00:00 163.43 163.43 163.43 163.43 202.0 4.0 163.430000 AAPL
2022-09-13 05:44:00 00:00 163.50 163.50 163.50 163.50 121.0 2.0 163.499587 AAPL
2022-09-13 05:45:00 00:00 163.46 163.46 163.46 163.46 200.0 2.0 163.460000 AAPL
... ... ... ... ... ... ... ... ...
2022-09-14 19:57:00 00:00 99.73 99.73 99.69 99.69 1273.0 18.0 99.693425 ZROZ
2022-09-14 19:58:00 00:00 99.69 99.69 99.66 99.69 1114.0 11.0 99.686965 ZROZ
2022-09-14 19:59:00 00:00 99.69 99.82 99.69 99.76 9764.0 76.0 99.736332 ZROZ
2022-09-14 20:00:00 00:00 99.76 99.76 99.76 99.76 2168.0 1.0 99.760000 ZROZ
2022-09-14 20:33:00 00:00 99.96 99.96 99.96 99.96 150.0 4.0 99.968667 ZROZ
[317028 rows x 8 columns] df
I want to split this massive dataframe into bits, grouped by the ticker, and the day. When I try the following method:
table = df.groupby(pd.Grouper(key='index', freq='1D'))
I get the error:
raise KeyError(f"The grouper name {key} is not found")
KeyError: 'The grouper name index is not found'
When I change the key to:
table = df.groupby(pd.Grouper(key=df.index, freq='1D'))
I get the error:
if getattr(self._gpr_index, "name", None) == key and isinstance(
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
How can I group by ticker and by day?
CodePudding user response:
Because key
parameter is for column name you can omit it:
table = df.groupby(pd.Grouper(freq='1D'))
Or use level
parameter:
table = df.groupby(pd.Grouper(level='index', freq='1D'))
Or convert index
to column (in my opinion overcomplicated):
table = df.reset_index().groupby(pd.Grouper(key='index', freq='1D'))