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.
See Roadmap evolution for proposing changes to this document.
pandas 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.
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
Currently, pandas handles missing data differently for different data types. We
use different types to indicate that a value is missing (np.nan for
floating-point data, np.nan or None for object-dtype data – typically
strings or booleans – with missing values, and pd.NaT for datetimelike
data). Integer data cannot store missing data or are cast to float. In addition,
pandas 1.0 introduced a new missing value sentinel, pd.NA, which is being
used for the experimental nullable integer, boolean, and string data types.
These different missing values have different behaviors in user-facing
operations. Specifically, we introduced different semantics for the nullable
data types for certain operations (e.g. propagating in comparison operations
instead of comparing as False).
Long term, we want to introduce consistent missing data handling for all data
types. This includes consistent behavior in all operations (indexing, arithmetic
operations, comparisons, etc.). We want to eventually make the new semantics the
This has been discussed at
github #28095 (and
linked issues), and described in more detail in this
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
We’d like to replace pandas current internal data structures (a collection of
1 or 2-D arrays) with a simpler collection of 1-D arrays.
pandas internal data model is quite complex. A DataFrame is made up of
one or more 2-dimensional “blocks”, with one or more blocks per dtype. This
collection of 2-D arrays is managed by the BlockManager.
The primary benefit of the BlockManager is improved performance on certain
operations (construction from a 2D array, binary operations, reductions across the columns),
especially for wide DataFrames. However, the BlockManager substantially increases the
complexity and maintenance burden of pandas.
By replacing the BlockManager we hope to achieve
Substantially simpler code
Easier extensibility with new logical types
Better user control over memory use and layout
Option to provide a C / Cython API to pandas’ internals
See these design documents
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. More details are discussed at
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.
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
Be more stable. Currently, they’re maintained on the nights and weekends when
a maintainer has free time.
Tune the system for benchmarks to improve stability, following
Build a GitHub bot to request ASV runs before a PR is merged. Currently, the
benchmarks are only run nightly.
pandas continues to evolve. The direction is primarily determined by community
interest. Everyone is welcome to review existing items on the roadmap and
to propose a new item.
Each item on the roadmap should be a short summary of a larger design proposal.
The proposal should include
Short summary of the changes, which would be appropriate for inclusion in
the roadmap if accepted.
Motivation for the changes.
An explanation of why the change is in scope for pandas.
Detailed design: Preferably with example-usage (even if not implemented yet)
and API documentation
API Change: Any API changes that may result from the proposal.
That proposal may then be submitted as a GitHub issue, where the pandas maintainers
can review and comment on the design. The pandas mailing list
should be notified of the proposal.
When there’s agreement that an implementation
would be welcome, the roadmap should be updated to include the summary and a
link to the discussion issue.
This section records now completed items from the pandas roadmap.
We improved the pandas documentation
The pandas community worked with others to build the pydata-sphinx-theme,
which is now used for https://pandas.pydata.org/docs/ (GH15556).
Getting started contains a number of resources intended for new
pandas users coming from a variety of backgrounds (GH26831).