As a followup of Pandas groupby: group by semester I need to collapse a Series' MultiIndex to a DateTimeIndex.
I already gave a look at Collapse Pandas MultiIndex to Single Index but at no avail. I cannot make it work.
Series ser
is:
dtime dtime
2016 1 78.0
7 79.0
2017 1 73.0
7 79.0
2018 1 79.0
7 71.0
Name: values, dtype: float64
How to collapse dtime
to a single DateTimeIndex?
dtime
2016-01-01 78.0
2016-07-01 79.0
2017-01-01 73.0
2017-07-01 79.0
2018-01-01 79.0
2018-07-01 71.0
Name: values, dtype: float64
This is the code producing my demo Series ser
:
from datetime import *
import pandas as pd
import numpy as np
np.random.seed(seed=1111)
days = pd.date_range(start="2016-02-15",
end="2018-09-12",
freq="2W")
df = pd.DataFrame({"dtime":days, "values":np.random.randint(50, high=80, size=len(days))}).set_index("dtime")
# group by semester
year = df.index.year.astype(int)
month = (df.index.month.astype(int) - 1) // 6 * 6 + 1
grouped = df.groupby([year, month])
ser = grouped.describe()[("values", "max")].rename("values")
print(ser)
You need join levels of MultiIndex
or Series
together and convert to datetimes
:
idx = ser.index.get_level_values(0).astype(str) + ser.index.get_level_values(1).astype(str)
ser.index = pd.to_datetime(idx, format='%Y%m')
print(ser)
2016-01-01 78.0
2016-07-01 79.0
2017-01-01 73.0
2017-07-01 79.0
2018-01-01 79.0
2018-07-01 71.0
Name: values, dtype: float64
Or:
dates = pd.to_datetime(year.astype(str) + month.astype(str), format='%Y%m')
grouped = df.groupby(dates)
ser = grouped.describe()[("values", "max")].rename("values")
print (ser)
2016-01-01 78.0
2016-07-01 79.0
2017-01-01 73.0
2017-07-01 79.0
2018-01-01 79.0
2018-07-01 71.0
Name: values, dtype: float64
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