Manual: .chem.lead() Method for Creating Lead Variables

Overview

The .chem.lead() method is a custom pandas accessor that creates lead versions of one or more variables in a DataFrame. It works by shifting the specified columns backward in time (i.e., to future periods) and merging the result back onto the original DataFrame.

This is especially useful for panel or time-series data where each row represents an observation at a time point for a particular unit (e.g., experiment, company, individual).

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

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Accessor Registration

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

Access the method like this:

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Method Signature

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Parameters

Parameter	Type	Description
variables	str or list of str	Column(s) for which to create lead 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. Must be a positive integer.
replace	bool, default False	If True, overwrites existing lead columns. If False, raises an error if there’s a naming conflict.

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Returns

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

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Behavior

1. Validates parameter types and column existence.
2. Creates a shifted version of the selected variables by subtracting the shift from the time column.
3. Merges this lead DataFrame back into the original, using identifier and time as keys.
4. New columns are suffixed with:
   • _lead for shift=1
   • _leadN for shift=N (e.g., _lead3 for shift=3)
5. If replace=False and a target lead column already exists, a ValueError is raised.

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Notes

• The original DataFrame remains unchanged.
• Supports multiple variables and vectorized group-wise operations.
• Useful for forecasting models or previewing future values.
• For backward-looking operations, see .chem.lag().

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Example

This will produce a new column called mass_lead with the mass value from the next time step (grouped by id).

Or more concisely:

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Common Use Cases

• Creating future-looking predictors in modeling.
• Forecast validation (comparing current and future state).
• Detecting upcoming transitions or changes in sequence.

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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 a specified column does not exist.
• ValueError if a lead column already exists and replace=False.

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Internals

The method:

1. Copies the relevant subset of the DataFrame.
2. Shifts the time column backward (df[time] - shift) to align future values.
3. Applies suffixes such as _lead or _leadN.
4. Merges the lead values back into the original DataFrame using pd.merge(...).

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See Also

• .chem.lag() – for creating lagged (past) variables
• .chem.mutate() – for conditional column assignment
• pd.DataFrame.shift() – basic shifting

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