Dates and Ordered Data

Extract Date Parts

Pull year and month from a sequence of date strings into separate lists. By hand, parse each string with datetime.date.fromisoformat and read .year and .month. With pandas, .dt.year and .dt.month extract all values from a datetime64 Series without looping.

By hand

With pandas

After pd.to_datetime, use s.dt.year.tolist() and s.dt.month.tolist() to extract all year and month values in a single vectorized step per component.

naive.py 
import datetime
date_strs = ['2024-01-15', '2024-03-22', '2024-07-04', '2024-11-11']
years = []
months = []
for s in date_strs:
    d = datetime.date.fromisoformat(s)
    years.append(d.year)
    months.append(d.month)
print('RESULT:', (years, months))
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

date_strs = ['2024-01-15', '2024-03-22', '2024-07-04', '2024-11-11']
df = pd.DataFrame({'date': date_strs})
s = pd.to_datetime(df['date'])
years = s.dt.year.tolist()
months = s.dt.month.tolist()
print('index:', s.index.tolist())
print('years:', years)
print('months:', months)
print('RESULT:', (years, months))

index: [0, 1, 2, 3]
years: [2024, 2024, 2024, 2024]
months: [1, 3, 7, 11]
RESULT: ([2024, 2024, 2024, 2024], [1, 3, 7, 11])

Implementation notes

  • The .dt accessor is available on any datetime64 Series. Common components: .dt.year, .dt.month, .dt.day, .dt.hour, .dt.dayofweek (Monday=0), .dt.day_name(), .dt.quarter.
  • tolist() converts int64 component Series to Python ints — matching stdlib .year/.month which are also plain ints, so the RESULT strings compare equal without any conversion.
  • Cross-reference: parse-date-column (this chapter) to produce the datetime64 Series this lesson starts from.