cudf-cu12

library26.6.0pypypiunverified

cuDF is a GPU-accelerated Python DataFrame library that mirrors the pandas API, enabling data scientists to perform data manipulation and analytics tasks entirely on NVIDIA GPUs. It is a core component of the RAPIDS suite of open-source libraries, designed to significantly speed up data processing for large datasets by leveraging GPU parallelism and memory bandwidth. cuDF is actively developed with frequent releases, typically aligned with the RAPIDS project's release cycle.

data ai-ml database

pip install cudf-cu12 rmm-cu12 --extra-index-url https://pypi.nvidia.com

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · CUDF-CU12

cudf-cu12

datapythonv26.6.0

Install

53.5s avg

Import

4233ms

Disk

2478MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v26.2.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

build_error

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 53.5s · import 3.386s · 2560MB

2478MB installed

● package 2478MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

cudf

✓ import cudf

Standard import for direct cuDF DataFrame operations.

cudf.pandas

✓ import cudf.pandas; cudf.pandas.install() # before import pandas

✗ import pandas as pd; import cudf.pandas; cudf.pandas.install()

To enable the pandas accelerator mode, `cudf.pandas.install()` must be called *before* pandas is imported or used. For Jupyter/IPython, use `%load_ext cudf.pandas` as the first command. Direct `import cudf` is generally incompatible with `cudf.pandas` acceleration.

This quickstart demonstrates creating a cuDF DataFrame and performing a basic groupby aggregation, similar to pandas. It also includes a comment illustrating how to enable `cudf.pandas` for zero-code-change GPU acceleration of existing pandas workflows, emphasizing the importance of activating it before `pandas` is imported.

import cudf # Create a cuDF DataFrame from a dictionary data = {'col1': [1, 2, 3, 4], 'col2': [10.0, 20.0, 15.0, 25.0], 'col3': ['A', 'B', 'C', 'A']} df = cudf.DataFrame(data) print("Original DataFrame:") print(df) # Perform a groupby aggregation grouped_df = df.groupby('col3').agg({'col1': 'sum', 'col2': 'mean'}) print("\nGrouped DataFrame:") print(grouped_df) # Using the cudf.pandas accelerator (restart kernel if pandas was already imported) # %load_ext cudf.pandas # For Jupyter/IPython # import cudf.pandas; cudf.pandas.install() # For scripts, before import pandas # import pandas as pd # pdf = pd.DataFrame(data) # This would be accelerated by cuDF.pandas

Debug

Known issues

breakingStarting with v26.04.00, cuDF requires PyArrow 19 or newer. Older PyArrow versions will lead to installation failures or runtime errors due to ABI incompatibilities.

fix

Ensure your environment has `pyarrow>=19` installed. If using conda, update `pyarrow` and `cudf` together.

affects: >=26.04.00

breakingThe `DataFrame.apply_rows`, `DataFrame.apply_chunks`, and `Groupby.apply_grouped` APIs have been deprecated in v25.10.00 and fully removed in v25.12.00.

fix

Refactor code to use alternative cuDF operations, such as element-wise operations, or more efficient `apply` patterns if available. Consider using `map_partitions` with Dask-cuDF for custom row-wise logic across partitions.

affects: >=25.10.00

breakingcuDF v25.08.00 and later officially drop support for CUDA 11, requiring CUDA 12.x or newer. Users on older CUDA versions will experience build or runtime failures.

fix

Upgrade your NVIDIA CUDA Toolkit installation to version 12.x. Ensure your `cudf-cuXX` package matches your CUDA runtime version.

affects: >=25.08.00

deprecatedThe `nvtext::byte_pair_encoding` APIs were deprecated in v26.04.00.

fix

Consult `nvtext` documentation for recommended replacement APIs for byte pair encoding, or consider alternative text processing methods.

affects: >=26.04.00

gotchaWhen using `cudf.pandas`, avoid mixing it with direct `import cudf` statements in the same session/script. `cudf.pandas` operates as a pandas accelerator and manages data movement, while direct `cudf` imports expect GPU DataFrames.

fix

Choose either `cudf.pandas` (by installing it before importing `pandas`) for pandas-like workflows or `import cudf` for explicit cuDF usage. Do not use both in the same execution context unless explicitly managing data conversions between `cudf.DataFrame` and `pandas.DataFrame`.

affects: All

gotchaThe default behavior for filling missing values in `Series/Index.values` changed to `np.nan` for numeric types in v25.10.00. This might alter downstream computations if `None` or another sentinel value was implicitly expected.

fix

Explicitly handle missing values using `.fillna()` or `.replace()` if a specific fill value other than `np.nan` is required.

affects: >=25.10.00

Upgrade

Version history

26.6.0latest on PyPI · released Jun 9, 2026

Audit

Dependencies

pythonrequiredRequires Python >=3.11.

pyarrowrequiredEssential for I/O operations (e.g., Parquet, Feather) and data interoperability; strict version requirements often apply.

rmmrequiredRAPIDS Memory Manager, a dependency for efficient GPU memory allocation.

cuda-toolkitrequiredRequires a compatible NVIDIA CUDA Toolkit installation on the system.

Agent activity

18 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/rapidsai/cudf ↗

packagehttps://pypi.org/project/cudf-cu12/ ↗

homepagehttps://rapids.ai ↗

docshttps://docs.rapids.ai/api/cudf/stable/ ↗