How do you parse, manipulate, and extract features from datetime columns in pandas?
Convert string columns to datetime with pd.to_datetime(), then use the .dt accessor to extract components like year, month, day, and day of week, compute time deltas, and perform resampling. Setting a DatetimeIndex unlocks time-series-specific operations like resample, rolling, and time-aware interpolation.
How to think about it
Date-time data is almost always messy — inconsistent string formats, mixed timezones, gaps in the series. A strong answer shows the four moves: pd.to_datetime() to parse, the .dt accessor to extract features, timedelta arithmetic for durations, and resample to aggregate over time windows.
The flow: pandas reads CSVs as strings, so step one is always df["order_ts"] = pd.to_datetime(df["order_ts"]) (or parse_dates=[...] at read time). Once a column is datetime64, .dt exposes every component, subtraction yields a Timedelta, and setting a DatetimeIndex unlocks resample — the pandas answer to SQL’s DATE_TRUNC + GROUP BY.
A worked example — parse, extract, resample
import pandas as pd
df = pd.DataFrame({
"order_ts": ["2024-03-15 08:30:00", "2024-03-16 14:00:00", "2024-03-22 09:15:00",
"2024-04-01 17:45:00", "2024-04-05 11:00:00"],
"ship_ts": ["2024-03-17 10:00:00", "2024-03-19 08:30:00", "2024-03-25 12:00:00",
"2024-04-04 09:00:00", "2024-04-06 14:30:00"],
"revenue": [120.0, 85.5, 200.0, 55.0, 310.0],
})
df["order_ts"] = pd.to_datetime(df["order_ts"])
df["ship_ts"] = pd.to_datetime(df["ship_ts"])
df["month"] = df["order_ts"].dt.month
df["day_of_week"] = df["order_ts"].dt.day_name()
df["is_weekend"] = df["order_ts"].dt.dayofweek >= 5
df["fulfillment_days"] = (df["ship_ts"] - df["order_ts"]).dt.days
print(df[["order_ts", "month", "day_of_week", "is_weekend", "fulfillment_days"]]) order_ts month day_of_week is_weekend fulfillment_days
0 2024-03-15 08:30:00 3 Friday False 2
1 2024-03-16 14:00:00 3 Saturday True 2
2 2024-03-22 09:15:00 3 Friday False 3
3 2024-04-01 17:45:00 4 Monday False 2
4 2024-04-05 11:00:00 4 Friday False 1Every feature is derived from the one parsed column: day_name() reads off “Saturday” for row 1, dayofweek >= 5 flags it as the only weekend, and subtracting the two datetimes then taking .dt.days gives whole-day fulfillment times. Set the timestamp as the index and resample("W") buckets revenue into calendar weeks:
ts = df.set_index("order_ts")
print(ts["revenue"].resample("W").sum())order_ts
2024-03-17 205.5
2024-03-24 200.0
2024-03-31 0.0
2024-04-07 365.0
Freq: W-SUN, Name: revenue, dtype: float64The two mid-March orders collapse into the week ending Sunday 03-17 (120 + 85.5 = 205.5), and — crucially — the empty week of 03-31 appears as 0.0 rather than vanishing, because resample fills the gap in the date range. That’s the behaviour that keeps time-series charts honest. (For ML, add cyclical encodings — np.sin/np.cos of dt.hour / 24 — so hour 23 stays close to hour 0.)