This page provides an overview of the major themes in pandas' development. Each of these items requires a relatively large amount of effort to implement. These may be achieved more quickly with dedicated funding or interest from contributors.

An item being on the roadmap does not mean that it will necessarily happen, even with unlimited funding. During the implementation period we may discover issues preventing the adoption of the feature.

Additionally, an item not being on the roadmap does not exclude it from inclusion in pandas. The roadmap is intended for larger, fundamental changes to the project that are likely to take months or years of developer time. Smaller-scoped items will continue to be tracked on our issue tracker.

The roadmap is defined as a set of major enhancement proposals named PDEPs. For more information about PDEPs, and how to submit one, please refer to PEDP-1.


Under discussion




Roadmap points pending a PDEP


Pandas extending.extension-types allow for extending NumPy types with custom data types and array storage. Pandas uses extension types internally, and provides an interface for 3rd-party libraries to define their own custom data types.

Many parts of pandas still unintentionally convert data to a NumPy array. These problems are especially pronounced for nested data.

We'd like to improve the handling of extension arrays throughout the library, making their behavior more consistent with the handling of NumPy arrays. We'll do this by cleaning up pandas' internals and adding new methods to the extension array interface.

String data type

Currently, pandas stores text data in an object -dtype NumPy array. The current implementation has two primary drawbacks: First, object -dtype is not specific to strings: any Python object can be stored in an object -dtype array, not just strings. Second: this is not efficient. The NumPy memory model isn't especially well-suited to variable width text data.

To solve the first issue, we propose a new extension type for string data. This will initially be opt-in, with users explicitly requesting dtype="string". The array backing this string dtype may initially be the current implementation: an object -dtype NumPy array of Python strings.

To solve the second issue (performance), we'll explore alternative in-memory array libraries (for example, Apache Arrow). As part of the work, we may need to implement certain operations expected by pandas users (for example the algorithm used in, Series.str.upper). That work may be done outside of pandas.

Apache Arrow interoperability

Apache Arrow is a cross-language development platform for in-memory data. The Arrow logical types are closely aligned with typical pandas use cases.

We'd like to provide better-integrated support for Arrow memory and data types within pandas. This will let us take advantage of its I/O capabilities and provide for better interoperability with other languages and libraries using Arrow.

Decoupling of indexing and internals

The code for getting and setting values in pandas' data structures needs refactoring. In particular, we must clearly separate code that converts keys (e.g., the argument to DataFrame.loc) to positions from code that uses these positions to get or set values. This is related to the proposed BlockManager rewrite. Currently, the BlockManager sometimes uses label-based, rather than position-based, indexing. We propose that it should only work with positional indexing, and the translation of keys to positions should be entirely done at a higher level.

Indexing is a complicated API with many subtleties. This refactor will require care and attention. The following principles should inspire refactoring of indexing code and should result on cleaner, simpler, and more performant code.

  1. Label indexing must never involve looking in an axis twice for the same label(s). This implies that any validation step must either:

  2. limit validation to general features (e.g. dtype/structure of the key/index), or

  3. reuse the result for the actual indexing.

  4. Indexers must never rely on an explicit call to other indexers. For instance, it is OK to have some internal method of .loc call some internal method of __getitem__ (or of their common base class), but never in the code flow of .loc should the_obj[something] appear.

  5. Execution of positional indexing must never involve labels (as currently, sadly, happens). That is, the code flow of a getter call (or a setter call in which the right hand side is non-indexed) to .iloc should never involve the axes of the object in any way.

  6. Indexing must never involve accessing/modifying values (i.e., act on ._data or .values) more than once. The following steps must hence be clearly decoupled:

  7. find positions we need to access/modify on each axis

  8. (if we are accessing) derive the type of object we need to return (dimensionality)
  9. actually access/modify the values
  10. (if we are accessing) construct the return object

  11. As a corollary to the decoupling between 4.i and 4.iii, any code which deals on how data is stored (including any combination of handling multiple dtypes, and sparse storage, categoricals, third-party types) must be independent from code that deals with identifying affected rows/columns, and take place only once step 4.i is completed.

  12. In particular, such code should most probably not live in pandas/core/

  13. ... and must not depend in any way on the type(s) of axes (e.g. no MultiIndex special cases)

  14. As a corollary to point 1.i, Index (sub)classes must provide separate methods for any desired validity check of label(s) which does not involve actual lookup, on the one side, and for any required conversion/adaptation/lookup of label(s), on the other.

  15. Use of trial and error should be limited, and anyway restricted to catch only exceptions which are actually expected (typically KeyError).

  16. In particular, code should never (intentionally) raise new exceptions in the except portion of a try... exception

  17. Any code portion which is not specific to setters and getters must be shared, and when small differences in behavior are expected (e.g. getting with .loc raises for missing labels, setting still doesn't), they can be managed with a specific parameter.

Numba-accelerated operations

Numba is a JIT compiler for Python code. We'd like to provide ways for users to apply their own Numba-jitted functions where pandas accepts user-defined functions (for example, Series.apply, DataFrame.apply, DataFrame.applymap, and in groupby and window contexts). This will improve the performance of user-defined-functions in these operations by staying within compiled code.

Documentation improvements

We'd like to improve the content, structure, and presentation of the pandas documentation. Some specific goals include

Performance monitoring

Pandas uses airspeed velocity to monitor for performance regressions. ASV itself is a fabulous tool, but requires some additional work to be integrated into an open source project's workflow.

The asv-runner organization, currently made up of pandas maintainers, provides tools built on top of ASV. We have a physical machine for running a number of project's benchmarks, and tools managing the benchmark runs and reporting on results.

We'd like to fund improvements and maintenance of these tools to