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Numba extension for compiling Pandas data frames, Intel® Scalable Dataframe Compiler

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We need to make sure that all processor have the same permutation index.

I managed to install `hpat` and tried to run https://intellabs.github.io/hpat/_build/html/source/supported.html#numpy-dot-parallelization I got ``` Traceback (most recent call last): File "/Users/uweschmitt/miniconda3/envs/HPAT/lib/python3.6/site-packages/numba/targets/options.py", line 17, in from_dict ctor = self.OPTIONS[k] KeyError: 'user_pipeline_funcs' ```...

I have some basic questions about how HPAT does I/O with HDF5 files... For example, in this program: ``` @hpat.jit def example_1D(n): f = h5py.File("data.h5", "r") A = f['A'][:] return...

**Issue Description:** Hello. I have discovered a performance degradation in the `read_csv` function of pandas version 1.3.4 when handling CSV files with a large number of columns. This problem significantly...

Bumps [pyarrow](https://github.com/apache/arrow) from 4.0.1 to 14.0.1. Commits ba53748 MINOR: [Release] Update versions for 14.0.1 529f376 MINOR: [Release] Update .deb/.rpm changelogs for 14.0.1 b84bbca MINOR: [Release] Update CHANGELOG.md for 14.0.1 f141709...

dependencies

I noticed that there is a good amount of code pointing to a read_parquet implementation available with the Pandas API, but I am seeming to have some trouble with it....