Avoids DST ambiguities and eases multi-region joins. Convert to local timezone only in presentation layer.

Time Series

Q: What is the difference between resample and groupby Grouper?

resample works on DatetimeIndex directly. Grouper attaches to column keys in groupby.

Q: How do I handle irregular events?

Resample to regular grid, then aggregate. Or use merge_asof for as-of joins.

Q: What does closed and label mean in resample?

closed="left" includes left bin edge. label sets which timestamp names the bucket.

Q: How do I export to Excel with dates?

Keep datetime dtype; avoid converting to strings before to_excel.

Time-indexed data needs calendar-aware grouping, rolling statistics, and explicit timezone rules - pandas DatetimeIndex provides the machinery.

Recipe

Quick-reference recipe card - copy-paste ready.

import pandas as pd
 
ts = df.set_index("ordered_at").sort_index()
daily = ts["revenue"].resample("D").sum()
roll = daily.rolling(7, min_periods=3).mean()
local = ts.index.tz_convert("America/New_York")

When to reach for this:

Sales or metrics indexed by event timestamps
Downsampling high-frequency logs to hourly/daily KPIs
Rolling anomaly detection on sensor streams
Aligning multi-region data to a reporting timezone

Working Example

import pandas as pd
 
idx = pd.date_range("2025-01-01", periods=48, freq="h", tz="UTC")
events = pd.DataFrame(
    {"revenue": range(48), "region": ["East"] * 24 + ["West"] * 24},
    index=idx,
)
events.index.name = "ts"
 
# Daily totals per region (wide)
daily = (
    events.groupby([pd.Grouper(freq="D"), "region"])["revenue"]
    .sum()
    .unstack(fill_value=0)
)
 
# 7-day rolling mean on East column
daily["East_roll7"] = daily["East"].rolling(7, min_periods=3).mean()
 
# Business-day reindex with forward fill for reporting gaps
biz = daily.asfreq("B").ffill()
 
# Display in US Eastern
eastern = events.index.tz_convert("America/New_York")
print(daily.head())
print("first Eastern hour:", eastern[0])

What this demonstrates:

UTC hourly index construction
Grouper(freq="D") for calendar daily buckets
Rolling mean with min_periods to avoid tiny windows
asfreq + ffill for business-day alignment

Deep Dive

How It Works

A DatetimeIndex stores int64 nanoseconds plus timezone metadata.
resample groups into regular periods (rule strings like D, ME, h).
rolling slides a fixed window; expanding grows from the start.
Timezone-naive vs aware comparisons raise in pandas 2.x - standardize at ingest.

Frequency Aliases

Rule	Meaning
`h`	Hourly
`D`	Calendar day
`B`	Business day
`ME`	Month end
`W-MON`	Weekly Monday

Python Notes

import pandas as pd
 
# Parse mixed formats, force UTC
pd.to_datetime(series, format="mixed", utc=True)
 
# Shift for lag features
df["revenue_lag1"] = df["revenue"].shift(1)

Gotchas

Unsorted index - resample and rolling assume sorted time. Fix: df.sort_index() first.
Timezone-naive local times - DST gaps and duplicates. Fix: ingest UTC; convert with tz_convert for display.
Month-end vs month-start - MS vs ME changes KPI boundaries. Fix: document reporting calendar with stakeholders.
Rolling on sparse data - default min_periods=window yields many NA. Fix: lower min_periods knowingly or impute gaps.
Mixing PeriodIndex and Timestamp - deprecated patterns break in pandas 2.x. Fix: stick to DatetimeIndex end-to-end.

Alternatives

Alternative	Use When	Don't Use When
Polars `group_by_dynamic`	Large lazy time buckets	Small pandas-only notebooks
statsmodels	ARIMA/seasonal decomposition	Simple resample KPIs
DuckDB `date_trunc`	SQL-native warehouse aggregates	Single-machine Series ops
`np.convolve`	Fixed FIR smoothing on ndarray	Irregular timestamps

FAQs

Why UTC at ingest?

Avoids DST ambiguities and eases multi-region joins.
Convert to local timezone only in presentation layer.

What is the difference between resample and groupby Grouper?

resample works on DatetimeIndex directly.
Grouper attaches to column keys in groupby.

How do I fill missing dates with zero?

ts.resample("D").sum().asfreq("D", fill_value=0)

How do I compute week-over-week change?

weekly = ts.resample("W").sum()
wow = weekly.pct_change()

Can rolling windows be time-based?

ts.rolling("7D").mean()

Requires sorted DatetimeIndex.

How do I handle irregular events?

Resample to regular grid, then aggregate.
Or use merge_asof for as-of joins.

What does closed and label mean in resample?

closed="left" includes left bin edge.
label sets which timestamp names the bucket.

How do I detect duplicate timestamps?

ts.index.duplicated().any()

Aggregate duplicates before resample.

How do I export to Excel with dates?

Keep datetime dtype; avoid converting to strings before to_excel.

Does Polars replace pandas time series?

Polars excels at scale; pandas still leads for interactive EDA.
Both use Arrow under the hood in modern stacks.

GroupBy & Aggregation - Grouper patterns
Cleaning & Transforming Data - parse dates at load
Joins & Merges - merge_asof
Performance & Memory - downsampling large series

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Recipe

Quick-reference recipe card - copy-paste ready.

import pandas as pd
 
ts = df.set_index("ordered_at").sort_index()
daily = ts["revenue"].resample("D").sum()
roll = daily.rolling(7, min_periods=3).mean()
local = ts.index.tz_convert("America/New_York")

When to reach for this:

Sales or metrics indexed by event timestamps
Downsampling high-frequency logs to hourly/daily KPIs
Rolling anomaly detection on sensor streams
Aligning multi-region data to a reporting timezone

Working Example

import pandas as pd
 
idx = pd.date_range("2025-01-01", periods=48, freq="h", tz="UTC")
events = pd.DataFrame(
    {"revenue": range(48), "region": ["East"] * 24 + ["West"] * 24},
    index=idx,
)
events.index.name = "ts"
 
# Daily totals per region (wide)
daily = (
    events.groupby([pd.Grouper(freq="D"), "region"])["revenue"]
    .sum()
    .unstack(fill_value=0)
)
 
# 7-day rolling mean on East column
daily["East_roll7"] = daily["East"].rolling(7, min_periods=3).mean()
 
# Business-day reindex with forward fill for reporting gaps
biz = daily.asfreq("B").ffill()
 
# Display in US Eastern
eastern = events.index.tz_convert("America/New_York")
print(daily.head())
print("first Eastern hour:", eastern[0])

What this demonstrates:

UTC hourly index construction
Grouper(freq="D") for calendar daily buckets
Rolling mean with min_periods to avoid tiny windows
asfreq + ffill for business-day alignment

Deep Dive

How It Works

A DatetimeIndex stores int64 nanoseconds plus timezone metadata.
resample groups into regular periods (rule strings like D, ME, h).
rolling slides a fixed window; expanding grows from the start.
Timezone-naive vs aware comparisons raise in pandas 2.x - standardize at ingest.

Frequency Aliases

Rule	Meaning
`h`	Hourly
`D`	Calendar day
`B`	Business day
`ME`	Month end
`W-MON`	Weekly Monday

Python Notes

import pandas as pd
 
# Parse mixed formats, force UTC
pd.to_datetime(series, format="mixed", utc=True)
 
# Shift for lag features
df["revenue_lag1"] = df["revenue"].shift(1)

Gotchas

Unsorted index - resample and rolling assume sorted time. Fix: df.sort_index() first.
Timezone-naive local times - DST gaps and duplicates. Fix: ingest UTC; convert with tz_convert for display.
Month-end vs month-start - MS vs ME changes KPI boundaries. Fix: document reporting calendar with stakeholders.
Rolling on sparse data - default min_periods=window yields many NA. Fix: lower min_periods knowingly or impute gaps.
Mixing PeriodIndex and Timestamp - deprecated patterns break in pandas 2.x. Fix: stick to DatetimeIndex end-to-end.

Alternatives

Alternative	Use When	Don't Use When
Polars `group_by_dynamic`	Large lazy time buckets	Small pandas-only notebooks
statsmodels	ARIMA/seasonal decomposition	Simple resample KPIs
DuckDB `date_trunc`	SQL-native warehouse aggregates	Single-machine Series ops
`np.convolve`	Fixed FIR smoothing on ndarray	Irregular timestamps

FAQs

Why UTC at ingest?

Avoids DST ambiguities and eases multi-region joins.
Convert to local timezone only in presentation layer.

What is the difference between resample and groupby Grouper?

resample works on DatetimeIndex directly.
Grouper attaches to column keys in groupby.

How do I fill missing dates with zero?

ts.resample("D").sum().asfreq("D", fill_value=0)

How do I compute week-over-week change?

weekly = ts.resample("W").sum()
wow = weekly.pct_change()

Can rolling windows be time-based?

ts.rolling("7D").mean()

Requires sorted DatetimeIndex.

How do I handle irregular events?

Resample to regular grid, then aggregate.
Or use merge_asof for as-of joins.

What does closed and label mean in resample?

closed="left" includes left bin edge.
label sets which timestamp names the bucket.

How do I detect duplicate timestamps?

ts.index.duplicated().any()

Aggregate duplicates before resample.

How do I export to Excel with dates?

Keep datetime dtype; avoid converting to strings before to_excel.

Does Polars replace pandas time series?

Polars excels at scale; pandas still leads for interactive EDA.
Both use Arrow under the hood in modern stacks.

GroupBy & Aggregation - Grouper patterns
Cleaning & Transforming Data - parse dates at load
Joins & Merges - merge_asof
Performance & Memory - downsampling large series

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Time Series

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Time Series