Source code for dtale.cli.loaders.parquet_loader

import pandas as pd

from dtale.app import show
from dtale.cli.clickutils import get_loader_options, loader_prop_keys

"""
  IMPORTANT!!! These global variables are required for building any customized CLI loader.
  When build_loaders runs startup it will search for any modules containing the global variable LOADER_KEY.
"""
LOADER_KEY = "parquet"
LOADER_PROPS = [
    dict(name="path", help="path to parquet file or URL to parquet endpoint"),
    dict(name="engine", help="parquet library to use"),
]


# IMPORTANT!!! This function is required if you would like to be able to use this loader from the back-end.
[docs]def show_loader(**kwargs): return show(data_loader=lambda: loader_func(**kwargs), **kwargs)
[docs]def loader_func(**kwargs): try: import pyarrow # noqa: F401 except ImportError: try: import fastparquet # noqa: F401 except ImportError: raise ImportError( "In order to use the parquet loader you must install either pyarrow or fastparquet!" ) path = kwargs.pop("path") return pd.read_parquet( path, **{k: v for k, v in kwargs.items() if k in loader_prop_keys(LOADER_PROPS)} )
# IMPORTANT!!! This function is required for building any customized CLI loader.
[docs]def find_loader(kwargs): """ JSON implementation of data loader which will return a function if any of the `click` options based on LOADER_KEY & LOADER_PROPS have been used, otherwise return None :param kwargs: Optional keyword arguments to be passed from `click` :return: data loader function for Parquet implementation """ parquet_opts = get_loader_options(LOADER_KEY, kwargs) if len([f for f in parquet_opts.values() if f]): def _json_loader(): parquet_arg_parsers = {} # TODO: add additional arg parsers kwargs = { k: parquet_arg_parsers.get(k, lambda v: v)(v) for k, v in parquet_opts.items() } return loader_func(**kwargs) return _json_loader return None