• df['datetime_series'].to_period(freq) → Convert to Period (e.g., 'M' for monthly)
• df['period_series'].to_timestamp() → Convert Period back to Timestamp

• Basic Components:
• .dt.year → Extract year
• .dt.month → Extract month (1-12)
• .dt.day → Extract day (1-31)
• .dt.hour → Extract hour (0-23)
• .dt.minute → Extract minute (0-59)
• .dt.second → Extract second (0-59)
• Advanced Components:
• .dt.dayofweek → Day of week (0=Monday, alias: .weekday)
• .dt.dayofyear → Day of year (1-366)
• .dt.quarter → Quarter (1-4)
• .dt.is_month_end → Bool for month-end dates
• .dt.is_quarter_end → Bool for quarter-end dates
• .dt.is_year_end → Bool for year-end dates

• .dt.total_seconds() → Convert entire duration to seconds

Example Usage:

# Create datetime Series
dates = pd.Series(pd.date_range('2023-01-01', periods=3, freq='M'))

# Access components
dates.dt.month          # → [1, 2, 3]
dates.dt.is_quarter_end # → [False, False, True] (March end)

# Formatting
dates.dt.strftime("%Y-%m") # → ['2023-01', '2023-02', '2023-03']

# Rounding
pd.Series([pd.Timestamp('2023-01-01 14:30:45')]).dt.round('H')
# → 2023-01-01 15:00:00

1. The .dt accessor only works with datetime-like Series
2. For timezone handling, use:
• tz_localize() to assign timezones
• tz_convert() to convert timezones
3. Frequency strings (for rounding/periods):
• 'D' = day, 'M' = month, 'Q' = quarter
• 'H' = hour, 'T'/'min' = minute, 'S' = second

• Extracting month/year for grouping
• Flagging period-end dates
• Creating formatted date strings for reports
• Normalizing timestamps to compare dates without times