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I have this pandas dataframe including a column for months whose type is int64, and I want to change every value in the column to be the corresponding season of the year.

For example if x is any of 12, 1, or 2, change the value of x to 'winter', etc.

I have tried some for loops but no use. I think there is a one liner that can do this using lambda x.

I supposed that every month has only 1 season so for example winter is in months 12, 1, and 2, and so on.

For example (0, 1, and 2 are indices):

input:

0  1
1  3
2  6

output:

0  winter
1  spring
2  summer

My problem is with month 12 or else I could have used bins.

Any ideas?

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4 Answers 4

Reset to default
1

One fast and flexible way is to map a dict from current column values to new column values:

d = dict(zip(range(1,13), ["winter"]*2 + ["spring"]*3 + ["summer"]*3 + ["fall"]*3 + ["winter"]))    
df = pd.DataFrame(dict(month=[3,4,6,1,5,6,2,4,5]))

df = df.month.map(d)

output:

0    spring
1    spring
2    summer
3    winter
4    spring
5    summer
6    winter
7    spring
8    spring
Name: month, dtype: object
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1 Comment

Ok thank you for this nice solution
1

map accepts a dict or a Series. (Values that are not found in the dict are converted to NaN.)

month2season = {1:'winter', 2:'winter', 3:'spring', ...}
s.map(month2season)  # s.apply(month2season) does the same thing
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1 Comment

that;'s very helpful actually thanks
0

suppose you have this dataframe (named df)

enter image description here

then u can easily write this function

def map_fn(x: int):
    if x in range(3, 6):
        return "spring"
    if x in range(6, 9):
        return "summer"
    if x in range(9, 12):
        return "autumn"
    return "winter"

and then apply it to your column like this

df.seasons.apply(map_fn)

to have this

enter image description here

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3 Comments

Please don't post pictures of text. Instead, copy the text, edit it into your post, and use the formatting tools like code formatting.
Not a big deal, but the second two ifs should be elifs.
NaNs will be mapped to 'winter', which is probably not what you want. You could add another check: elif x in range(1, 3) or x == 12: return "winter", although NaNs will be mapped to None. hmm... I'm not sure how to fix that.
0

I solved it using pandas replace and cut methods:

df['month']=df['month'].replace(12,0)

bin_edges = [-1,2,5,8,11]

bin_labels= ["winter",'spring','summer','fall']

df['month']=pd.cut(df['month'], bin_edges, labels=bin_labels)

Thanks all.

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