I tried playing around with pypy 3.6. Big fat disclaimer: I did not run any of the xarray unit tests. Expect trouble if you do.

1. ```bash

!/bin/bash

set -o errexit set -o pipefail set -o nounset set -o xtrace

tar -xvjf Downloads/pypy3.6-v7.1.1-linux64.tar.bz2 cd pypy3.6-v7.1.1-linux64/bin ./pypy3 -m ensurepip ./pip3.6 install -U pip wheel ./pip list | awk 'NR > 2 {print $1}' | grep -v greenlet | xargs ./pip install -U

sudo apt-get install libopenblas-dev gfortran

./pip install numpy pandas xarray ` 2. to work around https://bitbucket.org/pypy/pypy/issues/3087/collectionsabc-__init_subclass__-failure, editxarray/core/common.pyand deleteAttrAccessMixin.init_subclass``

1. timeit is unreliable in pypy. I modified the benchmark as follows: ```python import time

import numpy as np import xarray as xr

shape = (10, 10, 10, 10) index = (0, 0, 0, 0) np_arr = np.ones(shape) arr = xr.DataArray(np_arr)

N = 10000

def bench_slice(obj): for _ in range(4): t0 = time.time() for _ in range(N): obj[index] t1 = time.time() t_ns = (t1 - t0) / N * 1e9 print(f"{t_ns:6.0f} ns {obj.class.name}")

bench_slice(arr) bench_slice(np_arr) ```

Benchmark outputs: CPython 3.7: 93496 ns DataArray 92732 ns DataArray 92560 ns DataArray 93427 ns DataArray 119 ns ndarray 121 ns ndarray 122 ns ndarray 119 ns ndarray PyPy 7.1 3.6: 113273 ns DataArray 38543 ns DataArray 34797 ns DataArray 39453 ns DataArray 386 ns ndarray 289 ns ndarray 329 ns ndarray 413 ns ndarray Big important reminder: all results are for a very small array. I would expect the gap between CPython and pypy to get narrower in % (both for numpy and xarray) as the array size gets larger and more time is spent in the pure C numpy code.