python/pyspark/pandas/missing/common.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#


def memory_usage(f):
    return f(
        "memory_usage",
        reason="Unlike pandas, most DataFrames are not materialized in memory in Spark "
        "(and pandas-on-Spark), and as a result memory_usage() does not do what you intend it "
        "to do. Use Spark's web UI to monitor disk and memory usage of your application.",
    )


def array(f):
    return f(
        "array",
        reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.",
    )


def to_pickle(f):
    return f(
        "to_pickle",
        reason="For storage, we encourage you to use Delta or Parquet, instead of Python pickle "
        "format.",
    )


def to_xarray(f):
    return f(
        "to_xarray",
        reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.",
    )


def to_list(f):
    return f(
        "to_list",
        reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.",
    )


def tolist(f):
    return f(
        "tolist",
        reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.",
    )


def __iter__(f):
    return f(
        "__iter__",
        reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.",
    )


def duplicated(f):
    return f(
        "duplicated",
        reason="'duplicated' API returns np.ndarray and the data size is too large."
        "You can just use DataFrame.deduplicated instead",
    )