I have tried a lot to found a method that allow me to obtain all the groups of a resampling or of a group by without any aggregation for example starting from:
import pandas as pd
import datetime
import numpy as np
fir_date=pd.to_datetime('1991/12/25')
days = pd.date_range(fir_date, fir_date + datetime.timedelta(100), freq='30T')
data=[]
for i in range(3):
np.random.seed(seed=i)
data.append(np.random.randint(1, high=100, size=len(days)))
df = pd.DataFrame({'Datetime': days, 'feature1': data[0],'feature2': data[1],'feature3': data[2]})
df = df.set_index('Datetime')
df.index = df.index.map(lambda x: x.replace(second=0,microsecond=0))
I am able to obtain every group of a certain resample with:
df.resample('D').groups
> {Timestamp('1991-12-25 00:00:00', freq='D'): 48,
> Timestamp('1991-12-26 00:00:00', freq='D'): 96,
> Timestamp('1991-12-27 00:00:00', freq='D'): 144,
> Timestamp('1991-12-28 00:00:00', freq='D'): 192,
> Timestamp('1991-12-29 00:00:00', freq='D'): 240, ...}
The output is a dict an so i can access to a specific element with:
df.resample('D').get_group('1991-12-25 00:00:00')
But this seems not so smart.
There is a better way to, for example, get 1 DataFrame for every group of the resample??
I know is possible to cycle on the resample as:
for i in df.resample('D'):
print(i)
break
But this not allow me to comprare not consecutive group, or at least not easily.
Any good tips to handle this problem?
If need list of DataFrames use list comprehension:
dfs = [x for i, x in df.resample('D')]
If need dictionary is possible convert resample object to tuples and dict:
d = dict(tuple(df.resample('D')))