I want to convert a date time series to season, for example for months 3, 4, 5 I want to replace them with 2 (spring); for months 6, 7, 8 I want to replace them with 3 (summer) etc.
So, I have this series
id
1 2011-08-20
2 2011-08-23
3 2011-08-27
4 2011-09-01
5 2011-09-05
6 2011-09-06
7 2011-09-08
8 2011-09-09
Name: timestamp, dtype: datetime64[ns]
and this is the code I have been trying to use, but to no avail.
# Get seasons
spring = range(3, 5)
summer = range(6, 8)
fall = range(9, 11)
# winter = everything else
month = temp2.dt.month
season=[]
for _ in range(len(month)):
if any(x == spring for x in month):
season.append(2) # spring
elif any(x == summer for x in month):
season.append(3) # summer
elif any(x == fall for x in month):
season.append(4) # fall
else:
season.append(1) # winter
and
for _ in range(len(month)):
if month[_] == 3 or month[_] == 4 or month[_] == 5:
season.append(2) # spring
elif month[_] == 6 or month[_] == 7 or month[_] == 8:
season.append(3) # summer
elif month[_] == 9 or month[_] == 10 or month[_] == 11:
season.append(4) # fall
else:
season.append(1) # winter
Neither solution works, specifically in the first implementation I receive an error:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
While in the second is a large list with errors. Any ideas please? Thanks
You can use a simple mathematical formula to compress a month to a season, e.g.:
>>> [month%12 // 3 + 1 for month in range(1, 13)]
[1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1]
So for your use-case using vector operations (credit @DSM):
>>> temp2.dt.month%12 // 3 + 1
1 3
2 3
3 3
4 4
5 4
6 4
7 4
8 4
Name: id, dtype: int64
It's, also, possible to use dictionary mapping.
Create a dictionary that maps a month to a season:
In [27]: seasons = [1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1]
In [28]: month_to_season = dict(zip(range(1,13), seasons))
In [29]: month_to_season
Out[29]: {1: 1, 2: 1, 3: 2, 4: 2, 5: 2, 6: 3, 7: 3, 8: 3, 9: 4, 10: 4, 11: 4, 12: 1}
Use it to convert the months to seasons
In [30]: df.id.dt.month.map(month_to_season)
Out[30]:
1 3
2 3
3 3
4 4
5 4
6 4
7 4
8 4
Name: id, dtype: int64
Performance: This is fairly fast
In [35]: %timeit df.id.dt.month.map(month_to_season)
1000 loops, best of 3: 422 µs per loop
I think a more precise solution may be useful. If we have a month (1, ..., 12), we can convert it to season decreasing one and dividing by 3,
df = pd.Series(["2011-06-07",
"2011-08-23",
"2011-08-27",
"2011-09-01",
"2011-09-05",
"2011-09-06",
"2011-09-08",
"2011-12-25"])
df = pd.to_datetime(df)
season = (df.dt.month - 1) // 3
Therefore we will be mapping 1,2,3 to 0 (winter), 4,5,6 to 1 (spring), 7,8,9 to 2 (summer), and 10,11,12 to 3 (fall). However, we know the months 3,6,9, and 12 divide two seasons each. I propose the following approach:
If the month is 3 and the day is greater or equal 20, the season is spring, and we need to sum 1. If the month is 6 and the day is greater or equal 21, the season is summer, and we need to sum 1. If the month is 9 and the day is greater or equal 23, the season is fall, and we need to sum 1. If the month is 3 and the day is greater or equal 20, the season is winter, and we need to decrease 3 (or sum +1 in modulus 4). Then we have
season += (df.dt.month == 3)&(df.dt.day>=20)
season += (df.dt.month == 6)&(df.dt.day>=21)
season += (df.dt.month == 9)&(df.dt.day>=23)
season -= 3*((df.dt.month == 12)&(df.dt.day>=21)).astype(int)
The solution for this series will be [1,2,2,2,2,2,2,0].
I think this would work.
while True:
date=int(input("Date?"))
season=""
if date<4:
season=1
elif date<7:
season=2
elif date<10:
season=3
elif date<13:
season=4
else:
print("This would not work.")
print(season)
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