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All datetime operations in Pandas – Complete Guide for Data Science 2026

Pandas provides one of the most powerful and complete toolkits for working with datetime data. From parsing and loading to component extraction, timezone conversion, arithmetic, rounding, resampling, and rolling windows, the .dt accessor combined with pd.to_datetime, Timedelta, and resample() covers virtually every time-based operation you will encounter in real-world data science.

TL;DR — Core Pandas Datetime Operations

Parsing: parse_dates, pd.to_datetime(), date_format
Components: .dt.year, .dt.month, .dt.day_name(), etc.
Timezone: .dt.tz_localize(), .dt.tz_convert()
Arithmetic: + / - with Timedelta
Rounding: .dt.floor(), .dt.ceil(), .dt.round()
Resampling & Rolling: .resample(), .rolling()

1. Parsing and Loading Datetimes

import pandas as pd

df = pd.read_csv("sales_data.csv", 
                 parse_dates=["order_date", "delivery_date"],
                 date_format="%Y-%m-%d %H:%M:%S",
                 utc=True)

2. Component Extraction with .dt Accessor

df["year"] = df["order_date"].dt.year
df["month"] = df["order_date"].dt.month
df["day_name"] = df["order_date"].dt.day_name()
df["hour"] = df["order_date"].dt.hour
df["is_weekend"] = df["order_date"].dt.weekday.isin([5, 6])

3. Timezone Operations

df["order_date_utc"] = df["order_date"].dt.tz_localize("UTC")
df["order_date_ny"] = df["order_date_utc"].dt.tz_convert("America/New_York")

4. Arithmetic and Timedelta

now = pd.Timestamp.now(tz="UTC")
df["hours_since_order"] = (now - df["order_date_utc"]).dt.total_seconds() / 3600
df["projected_delivery"] = df["order_date_utc"] + pd.Timedelta(days=14)

5. Rounding, Flooring & Normalization

df["hour_start"] = df["order_date"].dt.floor("H")
df["day_start"] = df["order_date"].dt.normalize()
df["month_end"] = df["order_date"].dt.ceil("M")

6. Resampling and Rolling Windows

df = df.set_index("order_date_utc")
daily_sales = df.resample("D")["amount"].sum()
rolling_7d = df["amount"].rolling("7D").mean()

7. Best Practices in 2026

Parse datetimes as early as possible with parse_dates and date_format
Store all internal timestamps in UTC
Use the .dt accessor for all component extraction and transformations
Perform arithmetic with Timedelta on timezone-aware data
Use resample() and rolling() for time-series aggregation and windows

Conclusion

Pandas offers a complete and highly performant set of datetime operations that cover every stage of the data science workflow. From loading and parsing to component extraction, timezone handling, arithmetic, rounding, resampling, and rolling windows — the combination of .dt, Timedelta, resample(), and rolling() gives you everything you need to turn raw timestamps into powerful, actionable insights.

Next steps:

Review your current datasets and apply the full range of Pandas datetime operations to extract maximum value from your time-based data