31 lines
1.8 KiB
ReStructuredText
31 lines
1.8 KiB
ReStructuredText
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.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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=================================
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Upgrading from PySpark 2.2 to 2.3
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=================================
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* In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as ``toPandas``, ``createDataFrame`` from Pandas DataFrame, etc.
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* In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration ``spark.sql.execution.pandas.respectSessionTimeZone`` to False. See `SPARK-22395 <https://issues.apache.org/jira/browse/SPARK-22395>`_ for details.
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* In PySpark, ``na.fill()`` or ``fillna`` also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame.
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* In PySpark, ``df.replace`` does not allow to omit value when ``to_replace`` is not a dictionary. Previously, value could be omitted in the other cases and had None by default, which is counterintuitive and error-prone.
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