14. DateTime

df['date'] = pd.todatetime(df['date']) # parse strings

df['date'] = pd.to_datetime(df['date'])         # parse strings
pd.date_range('2024-01-01', periods=5, freq='D')  # daily range
pd.Timedelta(days=7)

# dt accessor
df['date'].dt.year
df['date'].dt.month
df['date'].dt.day_name()
df['date'].dt.dayofweek        # Mon=0
df['date'].dt.quarter

# set datetime index for resampling
df = df.set_index('date')
df['sales'].resample('M').sum()      # monthly totals
df['sales'].resample('W').mean()     # weekly average

# timezones
df['date'].dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata')

Tool	Purpose
`to_datetime`	parse strings/ints to datetime
`date_range`	generate regular date sequences
`Timedelta`	durations for date arithmetic
`.dt` accessor	extract parts (year, month, weekday)
`resample`	group time series by frequency

Common frequency strings: D day, W week, M month-end, MS month-start, Q quarter, Y year, H hour, T/min minute.

🚀 Best Practice: Set the datetime column as the index before resample or rolling. Always parse_dates at read time.

⭐ Interview Question: How to aggregate daily data to monthly? Set datetime index, then df.resample('M').sum() (or mean, last, etc.).