When a pandas operation has no direct Polars equivalent, translate what the code needs to do rather than searching for a method with the same name. First try a native Polars expression; if the logic genuinely needs Python, choose map_elements for individual values or map_batches for a Series or batch. Make the return type and null behavior explicit, then test the result on representative data.
Why a pandas operation may not translate directly
Polars is expression-oriented, and its data model and semantics differ from pandas. In particular, Polars does not have a pandas-style row index or MultiIndex, and its type system is stricter. A method with a familiar name is not necessarily a drop-in equivalent. Start by defining the behavior you need: which columns are inputs, whether the operation is per value, per row, per group, or per batch, how missing values are handled, what shape and dtype the result should have, and whether the logic depends on outside state. See Polars’ pandas migration guide for the conceptual differences.
Choose an approach based on the work the function does
| Approach | Function input | Best fit | Main tradeoff |
|---|---|---|---|
| Native Polars expression | Polars expression or column data | Logic supported by Polars’ expression API | You need to express the operation using Polars concepts. |
map_elements |
One value at a time | An unavoidable custom per-value function | Python callback overhead; Polars documents it as much slower than native expressions. |
map_batches |
A Series or batch of Series | Batch-oriented logic or integration with a third-party library | The function must match the expected batch and output semantics. |
| Plugin or external-library boundary | Depends on the plugin or library API | Custom expressions, I/O, or algorithms supplied elsewhere | Requires the relevant plugin or library and its integration contract. |
Look for a native expression first
Native expressions are generally the best first choice because they keep the work within Polars’ expression API; the official UDF guidance notes that this often removes the need for a custom Python function. Check the relevant expression namespace for the data you are working with, including list or struct operations when the values are nested. The map_elements API reference shows native expression alternatives for operations on ordinary values, list elements, and struct fields.
For example, if a pandas callback applies a supported arithmetic or string operation, express that operation with Polars expressions instead of wrapping it in Python. If the callback depends on a library or algorithm that Polars does not provide, move to the narrowest suitable UDF or integration boundary.
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Use map_elements for unavoidable per-value logic
Use map_elements when your function accepts one value at a time and the logic cannot reasonably be represented with native expressions. Set return_dtype when you know the output type, and decide deliberately what should happen to nulls. The stable API documentation warns: “This method is much slower than the native expressions API. Only use it if you cannot implement your logic otherwise.” That is Polars’ guidance, not a benchmark or a promised speed difference for your workload.
The function should be pure: Polars may call a UDF with arbitrary input data. Do not make its correctness depend on being called exactly once, in a particular order, or only with values seen in an earlier pass. Consult the current API reference for details such as null-skipping, return-type inference, and the threading strategy.
Use map_batches for Series-level work
Use map_batches when the algorithm needs a whole Series or batch of Series, rather than one value per call. This is often a better fit for a third-party function designed to process arrays or batches. Confirm that the function returns a result with the shape and type Polars expects, and declare the output type where the API permits or requires it. The Polars UDF guide explains the distinction between batch and elementwise functions.
Consider plugins or a conversion boundary
For reusable custom expressions or custom data sources, Polars recommends considering expression plugins or I/O plugins rather than defaulting to ordinary Python callbacks. If an external library requires array input, a conversion boundary may be appropriate: the migration guide notes Polars’ use of Apache Arrow memory format and support for conversion to NumPy with to_numpy. That does not make conversion the right choice for every operation; choose based on the library’s input contract, data size, and required output semantics.
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Check the Polars version before reusing examples
Polars 0.19 consolidated several custom-function names under map_*. Its 0.19 upgrade notes record these historical renames:
Series/Expr.apply→map_elementsSeries/Expr.rolling_apply→rolling_mapDataFrame.apply→map_rowsGroupBy.apply→map_groupsmap→map_batches
These are release-specific migration notes, not a guarantee that an older snippet matches your installed version. Check the documentation for the Polars version you actually use; the stable API reference may describe a different signature or set of options.
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Polars’ stricter type behavior makes it important to check edge cases that may have been silently coerced in pandas. Build checks around the operation’s actual contract, including:
- Null and missing values, including the chosen null behavior of the UDF.
- Empty input and any small or boundary-sized batches relevant to the function.
- Unexpected or mixed values that can occur in the source data.
- The result’s dtype, shape, and ordering assumptions.
- Whether the function remains correct when called on different input chunks or more than once.
Measure performance on the actual workload if it matters. Polars’ warning supports preferring native expressions where possible, but it does not establish a universal speed ratio or predict the result of a particular migration. Likewise, the API’s threading option is not a general promise of faster callbacks; its benefit depends on substantial per-element work and a function that releases the Python GIL.
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