Install & Compatibility
Where this runs
tested against v0.37.1 · 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
798MB installed
● package 798MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
pandas
✓ import modin.pandas as pd
✗ import pandas as pd
To leverage Modin's accelerated functionality, replace the standard pandas import with `modin.pandas`.
This quickstart demonstrates how to use Modin by simply changing the pandas import statement. Modin automatically parallelizes DataFrame operations across available cores/nodes using the installed backend (Ray, Dask, or Unidist). For small datasets, the performance difference might be negligible or even slightly worse due to overhead, but it offers significant speedups for large data.
import modin.pandas as pd
# Create a Modin DataFrame (this uses your configured backend, e.g., Ray or Dask)
data = {'col1': [1, 2, 3, 4], 'col2': [5, 6, 7, 8]}
df = pd.DataFrame(data)
print("Original Modin DataFrame:")
print(df)
# Perform a common operation, like adding a new column
df['col3'] = df['col1'] + df['col2']
print("DataFrame after operation:")
print(df)
# Note: For larger datasets, Modin's performance benefits become more apparent.
# Example with a larger dataset (requires a CSV file, e.g., 'nyc_taxi_data.csv')
# Uncomment and replace 'path/to/your/data.csv' with an actual path to test with large data.
# try:
# large_df = pd.read_csv('path/to/your/data.csv')
# print(f"Loaded large DataFrame with {len(large_df)} rows.")
# print(large_df.head())
# except FileNotFoundError:
# print("Skipping large dataset example: 'path/to/your/data.csv' not found.")
Debug
Known issues
gotchaNot all pandas operations are fully implemented in Modin. When an unimplemented method is called, Modin may silently fall back to the single-threaded pandas implementation, incurring communication overhead and potentially being slower than native pandas. A `UserWarning` might be issued.fixCheck Modin's documentation for supported operations. If an operation consistently defaults to pandas and performance is critical, consider refactoring or selectively using native pandas for that specific part of the workflow. You can enable `modin.config.LogDefaultToPandas.put(True)` to get a more verbose warning message.
affects: All versions
gotchaMixing pandas and Modin DataFrames directly in the same workflow is not recommended. Passing a pandas DataFrame to a Modin method or vice versa can lead to performance degradation (due to conversion overhead) or undefined behavior, as pandas identifies Modin objects as simple iterables.fixEnsure all DataFrames in a computational flow are consistently either Modin DataFrames or native pandas DataFrames. Convert explicitly using `df.to_pandas()` or `modin.pandas.DataFrame(pandas_df)` when switching between them is necessary.
affects: All versions
gotchaFor very small datasets (MBs), the overhead introduced by Modin's distributed computing engine (Ray, Dask, etc.) can sometimes make operations slower than plain pandas. Modin is optimized for medium to large datasets (GBs to TBs).fixBenchmarking is recommended. If working primarily with small datasets, consider sticking with native pandas. Modin's benefits become significant as data size increases.
affects: All versions
breakingSince Modin 0.30.0, the default Ray installation uses `ray-core` instead of `ray-default`. This means the Ray dashboard and cluster launcher are no longer installed by default.fixIf you require the Ray dashboard or cluster launcher, explicitly install `ray[default]` alongside your `modin[ray]` installation: `pip install "modin[ray]" "ray[default]"`.
affects: >=0.30.0
breakingAs of Modin 0.31.0, the HDK engine and Cudf storage format have been removed, as they were unmaintained.fixUsers previously relying on the HDK engine or Cudf storage format must switch to a supported backend like Ray, Dask, or Unidist.
affects: >=0.31.0
gotchaModin's `read_csv` function might not handle certain edge cases or exceptions as gracefully as native pandas. This can lead to errors (e.g., `TypeError` for missing columns) in situations where pandas would succeed.fixImplement robust error handling around `pd.read_csv` or fall back to native pandas for complex or error-prone CSV parsing. Consider pre-processing files or explicitly specifying `dtype` to handle heterogeneous data.
affects: All versions
gotchaErrors like `ArrowIOError: Broken Pipe` or Modin 'hanging on import' can occur if the underlying Ray (or Dask) backend fails to start correctly or unexpectedly shuts down (e.g., due to `KeyboardInterrupt`, system sleep, or rapid restarts of notebooks).fixRestart your Python interpreter or notebook kernel. Avoid using `KeyboardInterrupt` during Modin operations and ensure the system doesn't go to sleep during computation. Avoid starting multiple Modin notebooks/interpreters in quick succession.
affects: All versions (especially with Ray)
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'modin.pandas'
The Modin library or its pandas interface has not been correctly installed or is not accessible in the current Python environment.
ImportError: Please `pip install modin[ray]` or `modin[dask]` to install an engine
Modin requires a compute engine (like Ray or Dask) to run, and the necessary backend dependencies were not installed with Modin.
fixpip install 'modin[ray]' --upgrade
Hanging on import modin.pandas as pd
The underlying compute engine (most commonly Ray) failed to start correctly, causing the import statement to appear to hang indefinitely.
fixRestart your Python interpreter or notebook kernel; avoid starting multiple Modin notebooks or interpreters in quick succession.
UserWarning: `DataFrame.asfreq` defaulting to pandas implementation.
The specific pandas API method being called has not yet been implemented or optimized within Modin, causing Modin to fall back to the single-threaded pandas implementation.
fixThis is a warning, not an error. To avoid potential performance overhead, check Modin's documentation for supported methods or consider using alternative Modin-optimized operations; if performance is critical for this specific operation, consider using plain pandas for it.
Upgrade
Version history
0.37.1latest on PyPI · released Oct 2, 2025
Audit
Dependencies
pandasrequiredCore API compatibility
rayoptionalOptional execution backend for distributed computing
daskoptionalOptional execution backend for distributed computing
distributedoptionalRequired for Dask backend
unidistoptionalOptional execution backend for MPI distributed computing
mpi4pyoptionalRequired for Unidist/MPI backend (requires prior MPI installation)
pyarrowoptionalData serialization and I/O optimization
typing_extensionsrequiredTyping support (added in 0.37.0)