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Posted to reviews@spark.apache.org by "panbingkun (via GitHub)" <gi...@apache.org> on 2023/11/20 03:09:14 UTC
[PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
panbingkun opened a new pull request, #43897:
URL: https://github.com/apache/spark/pull/43897
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403831809
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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
Review Comment:
This isn't really useful here as it includes one class alone. Should probably just remove them
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403831456
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating Through `read.format(...).load(...)`
+---------------------------------------------
+
+Creating a PySpark :class:`DataFrame` by reading existing **json** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **csv** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").
+ ... load("python/test_support/sql/people.csv")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **parquet** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("parquet").load("python/test_support/sql/people.parquet")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+Creating a PySpark :class:`DataFrame` by reading existing **orc** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("parquet").load("python/test_support/sql/people.orc")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading data from other databases using **JDBC**
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
Review Comment:
I think we should really have a self-contained example, e.g. https://sparkbyexamples.com/pyspark/pyspark-read-and-write-mysql-database-table/. We could have a separate page for this, and link it here. Could be done separately in another PR.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403832135
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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
Review Comment:
Or, include the methods being used here. e.g., `` :meth:`SparkSession.createDataFrame ``, `` :meth:`DataFrameReader.load` ``, etc.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1398626446
##########
python/docs/source/user_guide/10min.rst:
##########
@@ -0,0 +1,188 @@
+.. 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.
+
+=====================
+10 minutes to PySpark
Review Comment:
We actually already have 10 minutes (https://spark.apache.org/docs/latest/api/python/getting_started/quickstart_df.html). Let's rename it better like DataFrame creation.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1410106899
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
Review Comment:
I haven't found any relevant documents yet, or we can add one later and reference it in this place.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "itholic (via GitHub)" <gi...@apache.org>.
itholic commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1400324847
##########
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:
Review Comment:
Pyspark -> PySpark
##########
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.
Review Comment:
DataFrame -> :class:\`DataFrame\`
##########
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
+1, or at least we should add a note.
##########
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.
Review Comment:
Maybe do we want to mention :class:\`numpy.ndarray\` here as well? e.g.
```suggestion
A PySpark :class:`DataFrame` can be created via :meth:`SparkSession.createDataFrame` typically by passing a list of lists, tuples, dictionaries, :class:`Row`, a pandas :class:`pandas.DataFrame`, a NumPy :class:\`numpy.ndarray\` and an :class:`pyspark.RDD`.
```
##########
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
Review Comment:
Maybe shall we capitalizing the first word of the title/sub-title for consistency with other documents?
e.g. DataFrame Creation / Basic Data Structures ...
##########
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
Review Comment:
Maybe shall we capitalizing the first word of the title/sub-title for consistency with other documents?
e.g. DataFrame Creation / Basic Data Structures ...
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "nchammas (via GitHub)" <gi...@apache.org>.
nchammas commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1426075998
##########
python/docs/source/user_guide/sql/creating_dataframes.rst:
##########
@@ -0,0 +1,223 @@
+.. 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.
+
+
+===================
+Creating DataFrames
+===================
+
+.. currentmodule:: pyspark.sql
+
+PySpark allows you to create :class:`DataFrame`\s in several ways. Let's explore these methods with simple examples.
+
+Creating a :class:`DataFrame` from Lists
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+ :emphasize-lines: 5
+
+ >>> # when the schema is not provided, the resulting DataFrame has _1 and _2 as the schema
+ >>> df = spark.createDataFrame([['Alice', 1], ['Bob', 5]])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` with a Specified Schema
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Define a schema and use it to create a :class:`DataFrame`. A schema describes the column names and types.
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import StructType, StructField, StringType, IntegerType
+ >>> schema = StructType([
+ ... StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+Use DDL-formatted string schema to create a :class:`DataFrame`.
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` from Dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Dictionaries with keys as column names can also be used.
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+Creating a :class:`DataFrame` from :class:`Row`\s
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Use the :class:`Row` type to define rows of a :class:`DataFrame`.
+
+.. code-block:: python
+
+ >>> from pyspark.sql import Row
+ >>> Person = Row('name', 'age')
+ >>> df = spark.createDataFrame([Person("Alice", 1), Person("Bob", 5)])
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` from a :class:`pandas.DataFrame` or a :class:`numpy.ndarray`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> import pandas as pd
+ >>> df = spark.createDataFrame(pd.DataFrame([[1, 2]]))
+ >>> df.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+.. 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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Reading Data from Files
+-----------------------
+
+Example with **JSON**
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Example with **CSV**
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").load(
+ ... "python/test_support/sql/people.csv")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Example with **PARQUET**
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> # Write a Parquet file to the temporary directory, and read it back
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Overwrite the path with a new Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": None, "name": "Michael"}, {"age": 30, "name": "Andy"}]
+ ... ).write.mode("overwrite").format("parquet").save(d)
+ ... # Read the Parquet file as a DataFrame
+ ... df = spark.read.format("parquet").load(d)
+ ... df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ | 30| Andy|
+ |NULL|Michael|
+ +----+-------+
+
+Example with **ORC**
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> # Write a Orc file to the temporary directory, and read it back
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Overwrite the path with a new Orc file
+ ... spark.createDataFrame(
+ ... [{"age": None, "name": "Michael"}, {"age": 30, "name": "Andy"}]
+ ... ).write.mode("overwrite").format("orc").save(d)
+ ... # Read the Orc file as a DataFrame
Review Comment:
```suggestion
... # Read the ORC file as a DataFrame
```
##########
python/docs/source/user_guide/sql/creating_dataframes.rst:
##########
@@ -0,0 +1,223 @@
+.. 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.
+
+
+===================
+Creating DataFrames
+===================
+
+.. currentmodule:: pyspark.sql
+
+PySpark allows you to create :class:`DataFrame`\s in several ways. Let's explore these methods with simple examples.
+
+Creating a :class:`DataFrame` from Lists
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+ :emphasize-lines: 5
+
+ >>> # when the schema is not provided, the resulting DataFrame has _1 and _2 as the schema
+ >>> df = spark.createDataFrame([['Alice', 1], ['Bob', 5]])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` with a Specified Schema
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Define a schema and use it to create a :class:`DataFrame`. A schema describes the column names and types.
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import StructType, StructField, StringType, IntegerType
+ >>> schema = StructType([
+ ... StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+Use DDL-formatted string schema to create a :class:`DataFrame`.
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` from Dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Dictionaries with keys as column names can also be used.
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+Creating a :class:`DataFrame` from :class:`Row`\s
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Use the :class:`Row` type to define rows of a :class:`DataFrame`.
+
+.. code-block:: python
+
+ >>> from pyspark.sql import Row
+ >>> Person = Row('name', 'age')
+ >>> df = spark.createDataFrame([Person("Alice", 1), Person("Bob", 5)])
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` from a :class:`pandas.DataFrame` or a :class:`numpy.ndarray`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> import pandas as pd
+ >>> df = spark.createDataFrame(pd.DataFrame([[1, 2]]))
+ >>> df.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+.. 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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Reading Data from Files
+-----------------------
+
+Example with **JSON**
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Example with **CSV**
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").load(
+ ... "python/test_support/sql/people.csv")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Example with **PARQUET**
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> # Write a Parquet file to the temporary directory, and read it back
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Overwrite the path with a new Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": None, "name": "Michael"}, {"age": 30, "name": "Andy"}]
+ ... ).write.mode("overwrite").format("parquet").save(d)
+ ... # Read the Parquet file as a DataFrame
+ ... df = spark.read.format("parquet").load(d)
+ ... df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ | 30| Andy|
+ |NULL|Michael|
+ +----+-------+
+
+Example with **ORC**
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> # Write a Orc file to the temporary directory, and read it back
Review Comment:
Nit: It's [Apache ORC](https://orc.apache.org) (upper case).
```suggestion
>>> # Write an ORC file to a temporary directory and read it back.
```
##########
python/docs/source/user_guide/sql/creating_dataframes.rst:
##########
@@ -0,0 +1,223 @@
+.. 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.
+
+
+===================
+Creating DataFrames
+===================
+
+.. currentmodule:: pyspark.sql
+
+PySpark allows you to create :class:`DataFrame`\s in several ways. Let's explore these methods with simple examples.
+
+Creating a :class:`DataFrame` from Lists
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+ :emphasize-lines: 5
+
+ >>> # when the schema is not provided, the resulting DataFrame has _1 and _2 as the schema
+ >>> df = spark.createDataFrame([['Alice', 1], ['Bob', 5]])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` with a Specified Schema
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Define a schema and use it to create a :class:`DataFrame`. A schema describes the column names and types.
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import StructType, StructField, StringType, IntegerType
+ >>> schema = StructType([
+ ... StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+Use DDL-formatted string schema to create a :class:`DataFrame`.
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` from Dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Dictionaries with keys as column names can also be used.
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+Creating a :class:`DataFrame` from :class:`Row`\s
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Use the :class:`Row` type to define rows of a :class:`DataFrame`.
+
+.. code-block:: python
+
+ >>> from pyspark.sql import Row
+ >>> Person = Row('name', 'age')
+ >>> df = spark.createDataFrame([Person("Alice", 1), Person("Bob", 5)])
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a :class:`DataFrame` from a :class:`pandas.DataFrame` or a :class:`numpy.ndarray`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> import pandas as pd
+ >>> df = spark.createDataFrame(pd.DataFrame([[1, 2]]))
+ >>> df.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+.. 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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Reading Data from Files
+-----------------------
+
+Example with **JSON**
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Example with **CSV**
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").load(
+ ... "python/test_support/sql/people.csv")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Example with **PARQUET**
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> # Write a Parquet file to the temporary directory, and read it back
Review Comment:
Nit: No need for a comma here.
```suggestion
>>> # Write a Parquet file to a temporary directory and read it back.
```
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
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`?
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1398627880
##########
python/docs/source/user_guide/10min.rst:
##########
@@ -0,0 +1,188 @@
+.. 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.
+
+=====================
+10 minutes to PySpark
Review Comment:
Okay
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403831650
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating Through `read.format(...).load(...)`
+---------------------------------------------
+
+Creating a PySpark :class:`DataFrame` by reading existing **json** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **csv** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").
+ ... load("python/test_support/sql/people.csv")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **parquet** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("parquet").load("python/test_support/sql/people.parquet")
Review Comment:
I think we should make the example self-contained with including `spark.range(10).write("parquet")` with properly adding comments.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403829936
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating Through `read.format(...).load(...)`
+---------------------------------------------
+
+Creating a PySpark :class:`DataFrame` by reading existing **json** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **csv** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").
+ ... load("python/test_support/sql/people.csv")
Review Comment:
This won't work because of the indentation.
```suggestion
>>> df = spark.read.format("csv").option("header", "true").load(
... "python/test_support/sql/people.csv")
```
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403830598
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating Through `read.format(...).load(...)`
Review Comment:
Preposition starts with a lower case.
```suggestion
Creating through `read.format(...).load(...)`
```
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1409849131
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
Review Comment:
```suggestion
Creating DataFrames in PySpark
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
Review Comment:
Do we need to show this example? Which one is better? From lists or tuples? We should provide opinionated ways to create DataFrames.
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`DataFrame`.
+When it is omitted, PySpark infers the corresponding schema by taking a sample from the data.
Review Comment:
Personally, I think we don't need this section. We can directly dive into different ways to create data frames and add some explanations there.
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+==================
+
Review Comment:
```suggestion
PySpark allows you to create DataFrames in several ways. Let's explore these methods with simple examples.
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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:
```suggestion
Creating a :class:`DataFrame` from Lists
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
Review Comment:
```suggestion
schema = StructType([
StructField("name", StringType(), True),
StructField("age", IntegerType(), True)
])
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
Review Comment:
Let's combine this with the previous section
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
Review Comment:
```suggestion
Define a schema and use it to create a DataFrame. A schema describes the column names and types.
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
Review Comment:
```suggestion
Creating a :class:`DataFrame` with a Specified Schema
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
Review Comment:
Maybe we should highlight that when the schema is not provided, the resulting data frame has `_1` and `_2` as the schema (this differs from pandas for example)
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+Creating a PySpark :class:`DataFrame` from a list of :class:`Row`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
Review Comment:
```suggestion
Use the Row type to define rows of a DataFrame.
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+Creating a PySpark :class:`DataFrame` from a list of :class:`Row`
Review Comment:
```suggestion
Creating a :class:`DataFrame` from :class:`Row`s
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a :class:`pandas.DataFrame`
Review Comment:
```suggestion
Creating a :class:`DataFrame` from a :class:`pandas.DataFrame` or a :class:`numpy.ndarray`
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating through `read.format(...).load(...)`
+---------------------------------------------
+
+Creating a PySpark :class:`DataFrame` by reading existing **json** format file data
Review Comment:
Here we can combine all sections to show examples:
```
- Example with JSON
<code block>
- Example with CSV
<code block>
- Example with Parquet
<code block>
- Example with JDBC
<code block>
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+Creating a PySpark :class:`DataFrame` from a :class:`numpy.ndarray`
Review Comment:
We can combine this with the previous section.
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating through `read.format(...).load(...)`
Review Comment:
Reading Data from Files
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
Review Comment:
```suggestion
Dictionaries with keys as column names can also be used.
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
Review Comment:
Let's not use `import *`
```
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
```
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
Review Comment:
"name string, age int" Just curious, do we have any documentation on this DDL string format? How to translate a pyspark type into this DDL string format?
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit DDL-formatted string schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema = "name string, age int")
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
Review Comment:
```suggestion
Creating a :class:`DataFrame` from Dictionaries
```
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1831003144
cc @allanf-db FYI
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1855078032
> Will the code in this guide be tested by our Python doc tests, by the way?
No, but I have manually tested it.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1402872980
##########
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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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
Review Comment:
Okay
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1820718220
Done
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1825064564
Looks pretty good otherwise.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403879127
##########
python/docs/source/user_guide/index.rst:
##########
@@ -26,6 +26,7 @@ PySpark specific user guides are available here:
:maxdepth: 2
python_packaging
+ dataframe_creation
Review Comment:
@HyukjinKwon Should we do similar operations? Move it under `sql/index`?
<img width="916" alt="image" src="https://github.com/apache/spark/assets/15246973/c38ee451-836f-4d53-85a9-d586ca7309b8">
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-2016641997
We're closing this PR because it hasn't been updated in a while. This isn't a judgement on the merit of the PR in any way. It's just a way of keeping the PR queue manageable.
If you'd like to revive this PR, please reopen it and ask a committer to remove the Stale tag!
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1400175341
##########
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:
Okay
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1404041842
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
+ >>> df.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of dictionaries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
+ >>> df.show()
+ +---+-----+
+ |age| name|
+ +---+-----+
+ | 1|Alice|
+ +---+-----+
+
+
+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.show()
+ +-----+---+
+ | name|age|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+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.show()
+ +---+---+
+ | 0| 1|
+ +---+---+
+ | 1| 2|
+ +---+---+
+
+
+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.show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+
+Creating Through `read.format(...).load(...)`
+---------------------------------------------
+
+Creating a PySpark :class:`DataFrame` by reading existing **json** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("json").load("python/test_support/sql/people.json")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **csv** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("csv").option("header", "true").
+ ... load("python/test_support/sql/people.csv")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading existing **parquet** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("parquet").load("python/test_support/sql/people.parquet")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+Creating a PySpark :class:`DataFrame` by reading existing **orc** format file data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.read.format("parquet").load("python/test_support/sql/people.orc")
+ >>> df.show()
+ +----+-------+
+ | age| name|
+ +----+-------+
+ |NULL|Michael|
+ | 30| Andy|
+ | 19| Justin|
+ +----+-------+
+
+
+Creating a PySpark :class:`DataFrame` by reading data from other databases using **JDBC**
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
Review Comment:
Okay, Let me temporarily remove this section from the document.
I have already created a new jira, and when I start this jira https://issues.apache.org/jira/browse/SPARK-46088,
I will add this section back and link it.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "allanf-db (via GitHub)" <gi...@apache.org>.
allanf-db commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1409861283
##########
python/docs/source/user_guide/sql/index.rst:
##########
@@ -16,13 +16,14 @@
under the License.
-=========
-Spark SQL
-=========
+=================
+DataFrame and SQL
Review Comment:
I think we should be consistent with the main PySpark doc page terminology now that we are changing this:
https://spark.apache.org/docs/latest/api/python/index.html
On the page, we say "Spark SQL and DataFrames"
I suggest we do the same here.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "nchammas (via GitHub)" <gi...@apache.org>.
nchammas commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1854991274
Will the code in this guide be tested by our Python doc tests, by the way?
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] closed pull request #43897: [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation
URL: https://github.com/apache/spark/pull/43897
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1402869871
##########
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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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
Review Comment:
what about using `df.show()` in the examples instead to show the data?
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1403830830
##########
python/docs/source/user_guide/dataframe_creation.rst:
##########
@@ -0,0 +1,233 @@
+.. 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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)])
+ >>> df.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` with the explicit schema specified
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+ >>> from pyspark.sql.types import *
+ >>> schema = StructType([StructField("name", StringType(), True),
+ ... StructField("age", IntegerType(), True)])
+ >>> df = spark.createDataFrame([('Alice', 1), ('Bob', 5)], schema)
Review Comment:
We should probably show an example with a string DDL schema schema
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1820363945
Cc @itholic @allisonwang-db @zhengruifeng mind reviewing this please when you find some time?
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1400364744
##########
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
Review Comment:
Okay
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1818163072
After:
<img width="1183" alt="image" src="https://github.com/apache/spark/assets/15246973/8d930743-76f8-4d72-963c-35f3afe6406e">
<img width="1389" alt="image" src="https://github.com/apache/spark/assets/15246973/ad54dfc1-19d3-4359-8d77-b6eb24682157">
<img width="1095" alt="image" src="https://github.com/apache/spark/assets/15246973/bb0e49df-34b4-4ccf-80b8-46cb97b6744c">
<img width="1102" alt="image" src="https://github.com/apache/spark/assets/15246973/ee79e187-e518-4527-b8e2-a87b9940f78a">
<img width="1109" alt="image" src="https://github.com/apache/spark/assets/15246973/04afd931-72a2-4ba7-8bc8-2e89cc589999">
<img width="1116" alt="image" src="https://github.com/apache/spark/assets/15246973/37f64ec8-268a-42ff-aba1-b8a404b1ebca">
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "allanf-db (via GitHub)" <gi...@apache.org>.
allanf-db commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1409864256
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
Review Comment:
I prefer "Creating DataFrames" to use an active verb, implying action and engagement.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1410102871
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
Review Comment:
Let's add some comments and highlight it.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on PR #43897:
URL: https://github.com/apache/spark/pull/43897#issuecomment-1833456493
All done.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1410057838
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`DataFrame`.
+When it is omitted, PySpark infers the corresponding schema by taking a sample from the data.
Review Comment:
Okay, Let's delete it.
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Re: [PR] [SPARK-45861][PYTHON][DOCS] Add user guide for dataframe creation [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #43897:
URL: https://github.com/apache/spark/pull/43897#discussion_r1410058936
##########
python/docs/source/user_guide/sql/dataframe_creation.rst:
##########
@@ -0,0 +1,239 @@
+.. 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
+
+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`,
+a NumPy :class:`numpy.ndarray` and an :class:`pyspark.RDD`.
+:meth:`SparkSession.createDataFrame` takes the `schema` argument to specify the schema of the :class:`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.show()
+ +-----+---+
+ | _1| _2|
+ +-----+---+
+ |Alice| 1|
+ | Bob| 5|
+ +-----+---+
+
+
+Creating a PySpark :class:`DataFrame` from a list of tuples
Review Comment:
Delete it.
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