zhengruifeng commented on code in PR #43897: URL: https://github.com/apache/spark/pull/43897#discussion_r1400159072
########## python/docs/source/user_guide/dataframe_creation.rst: ########## @@ -0,0 +1,179 @@ +.. 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. + +================== +DataFrame creation +================== + +.. currentmodule:: pyspark.sql + +Basic data structures +--------------------- + +Pyspark provides an important class for handling data: + +1. :class:`DataFrame`: a distributed collection of data grouped into named columns. + +Creating through `createDataFrame` +---------------------------------- + +A PySpark :class:`DataFrame` can be created via :meth:`SparkSession.createDataFrame` typically by passing +a list of lists, tuples, dictionaries and :class:`Row`, a pandas :class:`pandas.DataFrame` +and an :class:`pyspark.RDD` consisting of such a list. +:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the DataFrame. +When it is omitted, PySpark infers the corresponding schema by taking a sample from the data. + +Creating a PySpark :class:`DataFrame` from a list of lists +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + df = spark.createDataFrame([['Alice', 1], ['Bob', 5]]) + df + +DataFrame[_1: string, _2: bigint] + + +Creating a PySpark :class:`DataFrame` from a list of tuples +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + df = spark.createDataFrame([('Alice', 1), ('Bob', 5)]) + df + +DataFrame[_1: string, _2: bigint] + + +Creating a PySpark :class:`DataFrame` from a list of dictionaries +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + df = spark.createDataFrame([{'name': 'Alice', 'age': 1}]) + df + +DataFrame[age: bigint, name: string] + + +Creating a PySpark :class:`DataFrame` from a list of :class:`Row` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from pyspark.sql import Row + Person = Row('name', 'age') + df = spark.createDataFrame([Person("Alice", 1), Person("Bob", 5)]) + df + +DataFrame[name: string, age: bigint] + + +Creating a PySpark :class:`DataFrame` from a :class:`pandas.DataFrame` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import pandas as pd + df = spark.createDataFrame(pd.DataFrame([[1, 2]])) + df + +DataFrame[0: bigint, 1: bigint] + + +Creating a PySpark :class:`DataFrame` from a :class:`numpy.ndarray` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import numpy as np + import pandas as pd + df = spark.createDataFrame(pd.DataFrame(data=np.array([[1, 2], [3, 4]]), columns=['a', 'b'])) + df + +DataFrame[a: bigint, b: bigint] + + +Creating a PySpark :class:`DataFrame` from an :class:`pyspark.RDD` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python Review Comment: I think we'd better not mention RDD since it is not supported on connect ########## python/docs/source/user_guide/dataframe_creation.rst: ########## @@ -0,0 +1,179 @@ +.. 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. + +================== +DataFrame creation +================== + +.. currentmodule:: pyspark.sql + +Basic data structures +--------------------- + +Pyspark provides an important class for handling data: + +1. :class:`DataFrame`: a distributed collection of data grouped into named columns. + +Creating through `createDataFrame` +---------------------------------- + +A PySpark :class:`DataFrame` can be created via :meth:`SparkSession.createDataFrame` typically by passing +a list of lists, tuples, dictionaries and :class:`Row`, a pandas :class:`pandas.DataFrame` +and an :class:`pyspark.RDD` consisting of such a list. +:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the DataFrame. +When it is omitted, PySpark infers the corresponding schema by taking a sample from the data. + +Creating a PySpark :class:`DataFrame` from a list of lists Review Comment: shall we also add examples for `schema` in `createDataFrame`? -- This is an automated message from the Apache Git Service. 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