Manual: .chem.lag() Method for Creating Lagged Variables

Overview

The .chem.lag() method is a custom pandas accessor that creates lagged versions of one or more variables in a DataFrame. It operates by shifting the specified columns by a given number of time periods and merging the result back onto the original DataFrame.

This is especially useful for time-series panel data where each row belongs to a unique unit (e.g., company, experiment, patient) over time.

For a concrete example of usage, refer to https://pychemist.com/creating-lag-and-lead-variables/.

Accessor Registration

This method is registered with pandas under the accessor name .chem:

Access the method like this:

1	df.chem.lag(...)

Method Signature

1	df.chem.lag(variables, identifier, time, shift=1, *, replace=False)

Parameters

Parameter	Type	Description
variables	`str` or `list of str`	Column(s) for which to create lagged versions.
identifier	`str`	The name of the column identifying individual units (e.g., subject ID or group).
time	`str`	The name of the time column. Used to shift values within groups.
shift	`int`, default `1`	Number of time periods to shift. Positive values create lags. Negative values are not allowed.
replace	`bool`, default `False`	If `True`, overwrites existing lagged columns. If `False`, raises an error if there’s a naming conflict.

Returns

A modified copy of the original pd.DataFrame, with lagged versions of the specified variables added as new columns.

Behavior

Verifies input types and column existence.
Constructs a lagged version of the selected variables by shifting the time column forward by the given shift amount.
Merges this lagged DataFrame back onto the original, based on the identifier and time.
New columns are suffixed with:
- _lag for shift=1
- _lagN for shift=N (e.g., _lag3 for shift=3)
If replace=False and any of the output columns already exist, the method raises a ValueError.

Notes

The original DataFrame remains unchanged.
Supports multiple variables and vectorized operations.
Designed for panel or longitudinal data.
For negative shift (lead variables) refert to .chem.lead()

Example

df = pd.DataFrame({

"id": [1, 1, 1, 2, 2, 2],

"time": [1, 2, 3, 1, 2, 3],

"mass": [10, 15, 20, 5, 10, 15]

})

df_lagged = df.chem.lag(variables="mass", identifier="id", time="time", shift=1)

This will produce a new column called mass_lag with the mass value from the previous time step (by id).

Or more concisely:

1	df_lagged = df.chem.lag("mass", "id", "time")

Common Use Cases

Creating lagged predictors in time-series regression.
Modeling delayed effects in experiments.
Comparing values across sequential periods.

Error Handling

TypeError if variables is not a string or list of strings.
TypeError if replace is not a boolean.
TypeError if shift is not a positive integer.
ValueError if any specified variable is missing from the DataFrame.
ValueError if a lagged column already exists and replace=False.

Internals

The method:

Creates a shifted copy of the target columns using df[time] + shift.
Applies suffixes like _lag, _lag2, etc.
Merges the lagged columns back onto the original DataFrame using pd.merge(...).