Pandas Time Series Cheat Sheet

Covers datetime indexing, resampling, rolling windows, and shifting operations for analyzing and transforming time series data in pandas.

2 PagesIntermediateMar 5, 2026

Datetime Parsing & Indexing

Turn a date column into a queryable DatetimeIndex.

python

import pandas as pddf["date"] = pd.to_datetime(df["date"])df = df.set_index("date").sort_index()# Date range generationdates = pd.date_range(start="2024-01-01", end="2024-12-31", freq="D")# Extract componentsdf["year"] = df.index.yeardf["month"] = df.index.monthdf["day_of_week"] = df.index.dayofweek# Slice by date rangedf.loc["2024-03"]                # all of March 2024df.loc["2024-01-01":"2024-06-30"]

Resampling

Aggregate a time series into fixed-frequency bins.

python

# Downsample daily -> monthly totalsmonthly = df["sales"].resample("ME").sum()   # 'ME' = month end# Downsample to weekly averageweekly = df["sales"].resample("W").mean()# Upsample and forward-fillhourly = df["sales"].resample("h").ffill()# groupby + resample togetherdf.groupby("store").resample("ME")["sales"].sum()

Rolling Windows & Shifting

Compute moving averages, lags, and period-over-period change.

python

df["rolling_7d_avg"] = df["sales"].rolling(window=7).mean()df["rolling_7d_std"] = df["sales"].rolling(window=7).std()df["expanding_sum"] = df["sales"].expanding().sum()df["sales_lag1"] = df["sales"].shift(1)          # previous perioddf["sales_pct_change"] = df["sales"].pct_change()df["sales_ewm"] = df["sales"].ewm(span=7).mean()  # exponential weighted moving avg

Key Concepts

Core building blocks of time series work in pandas.

DatetimeIndex- Index type enabling label-based date slicing, resampling, and time-aware alignment
resample()- Groups time series data into fixed frequency bins (e.g. 'D', 'W', 'ME') and aggregates
asfreq()- Converts to a specified frequency without aggregation, inserting NaN for missing periods
rolling()- Applies a function over a sliding window (e.g. a 7-day moving average)
shift()- Shifts values forward/backward by n periods, used for lag features and period-over-period change
Timezone handling- Use tz_localize() to assign a timezone and tz_convert() to convert between timezones
Freq aliases- Common: 'D' day, 'W' week, 'ME' month end, 'QE' quarter end, 'YE' year end, 'h' hour

Pro Tip

When resampling irregular timestamped event data, resample() to a fixed frequency first before applying rolling() - rolling() operates on row count by default, not elapsed time, unless you pass a time-based window like rolling('7D').

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