import errno
import os
import signal
import time
class TimeoutError(Exception):
pass
class timeout:
def __init__(self, seconds=1, error_message='Timeout'):
self.seconds = seconds
self.error_message = error_message
def handle_timeout(self, signum, frame):
raise TimeoutError(self.error_message)
def __enter__(self):
signal.signal(signal.SIGALRM, self.handle_timeout)
signal.alarm(self.seconds)
def __exit__(self, type, value, traceback):
signal.alarm(0)
with timeout(seconds=3):
time.sleep(4)
In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of columnar storage , columnar compression and data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with Parquet datasets across the six major tools used to read and write from Parquet in the Python ecosystem: Pandas , PyArrow , fastparquet , AWS Data Wrangler , PySpark and Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
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