Translating a coded date Series into datetime in pandas

Question

How is a DataFrame of encoded dates converted to a DataFrame of datetime in pandas?

Starting dataFrame:

| year | month | day |
| ---- | ----- | ---
| 2022 | 'F'   |   9 |
| 2022 | 'J'   |   1 |
| 2022 | 'K'   |   5 |

code for month: 
   'F' = January, 
   'J' = April, 
   'K' = May

Desired dataFrame:

| datetime   |
| ---------- | 
| 2022-01-09 | 
| 2022-04-01 | 
| 2022-05-05 | 

What's the right way to do this?

This function translates a single code; how do I apply this function to the DataFrame?

def datefromcode(df):
    match month:
        case 'F': mo = 1
        case 'J': mo = 4
        case 'K': mo = 5

    return datetime.date(year, mo, day)

Answer 1

You can use a dictionary to map the month numbers and feed it to pandas.to_datetime that accepts a DataFrame as input with the year/month/day column names:

codes = {'F': 1, 'J': 4, 'K': 5}
pd.to_datetime(df.assign(month=df['month'].map(codes)))

output:

0   2022-01-09
1   2022-04-01
2   2022-05-05
dtype: datetime64[ns]

Or using string concatenation:

months = {'F': 'January', 'J': 'April', 'K': 'May'}
pd.to_datetime(df['year'].astype(str)+df['month'].map(months)+df['day'].astype(str), format='%Y%B%d')

Answer 2

Here's one way:

pd.to_datetime(df.assign(month=df["month"].map({
    "F": 1,
    "J": 2,
    "K": 3
})))

Answer 3

If you use a dictionary for the codes, you can map them first.

Then, using datetime, you can transform these to dates.

import pandas as pd
import datetime as dt

df = pd.DataFrame({'year': [2022]*3,
                   'month': list('FJK'),
                   'day': [9,1,5]})

months = {'F': 'January', 'J': 'April', 'K': 'May'}

df["month"] = df["month"].map(months)

df.apply(lambda x: dt.datetime.strptime(str(x.year) + "-" + x.month + "-" + str(x.day),"%Y-%B-%d"), axis=1)
#Out: 
#0   2022-01-09
#1   2022-04-01
#2   2022-05-05
#dtype: datetime64[ns]