Pandas Lambda Function Format Month and Day - pandas

Question

I have a DF "ltyc" that looks like this:
month day wind_speed
0 1 1 11.263604
1 1 2 11.971495
2 1 3 11.989080
3 1 4 12.558736
4 1 5 11.850899
And, i apply a lambda function:
ltyc['date'] = pd.to_datetime(ltyc["month"], format='%m').apply(lambda dt: dt.replace(year=2020))
To get it to look like this:
month day wind_speed date
0 1 1 11.263604 2020-01-01
1 1 2 11.971495 2020-01-01
2 1 3 11.989080 2020-01-01
3 1 4 12.558736 2020-01-01
4 1 5 11.850899 2020-01-01
Except, I need it to look like this so that the days change also...but I cannot figure out how to format the lambda statement to do this instead as this is what I need.
month day wind_speed date
0 1 1 11.263604 2020-01-01
1 1 2 11.971495 2020-01-02
2 1 3 11.989080 2020-01-03
3 1 4 12.558736 2020-01-04
4 1 5 11.850899 2020-01-05
I have tried this:
ltyc['date'] = pd.to_datetime(ltyc["month"], format='%m%d').apply(lambda dt: dt.replace(year=2020))
and i get this error:
ValueError: time data '1' does not match format '%m%d' (match)
Thank you for help since i'm trying to figure out the lambda functions.

Answer 1

create a series with value 2020 and name year. Concat it to ['month', 'day'] and passing to pd.to_datetime. As long as, you passing a dataframe with columns names in this order year, month, date, pd.to_datetime will convert it to the appropriate datetime series.
#Allolz suggestion:
ltyc['date'] = pd.to_datetime(ltyc[['day', 'month']].assign(year=2020))
Out[367]:
month day wind_speed date
0 1 1 11.263604 2020-01-01
1 1 2 11.971495 2020-01-02
2 1 3 11.989080 2020-01-03
3 1 4 12.558736 2020-01-04
4 1 5 11.850899 2020-01-05
Or you may use reindex to create the sub-dataframe to pass to pd.to_datetime
ltyc['date'] = pd.to_datetime(ltyc.reindex(['year','month','day'],
axis=1, fill_value=2020))
Original:
s = pd.Series([2020]*len(ltyc), name='year')
ltyc['date'] = pd.to_datetime(pd.concat([s, ltyc[['month','day']]], axis=1))

Answer 2

This is similar to a previous answer, but does not persist the 'helper' column with the year. In brief, we pass a data frame with three columns (year, month, day) to the to_datetime() function.
ltyc['date'] = pd.to_datetime(ltyc
.assign(year=2020)
.filter(['year', 'month', 'day'])
)

Answer 3

You could also use your method and add month and day together with .astype(str) and then add %d to the format. The problem with your lambda is that you only considered month, so this is how you would consider month and day.
ltyc['date'] = (pd.to_datetime(ltyc["month"].astype(str) + '-' + ltyc["day"].astype(str),
format='%m-%d')
.apply(lambda dt: dt.replace(year=2020)))
output:
month day wind_speed date
0 1 1 11.263604 2020-01-01
1 1 2 11.971495 2020-01-02
2 1 3 11.989080 2020-01-03
3 1 4 12.558736 2020-01-04
4 1 5 11.850899 2020-01-05