nchammas commented on a change in pull request #29491:
URL: https://github.com/apache/spark/pull/29491#discussion_r474834499
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File path: python/docs/source/getting_started/quickstart.ipynb
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@@ -0,0 +1,1091 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quickstart\n",
+ "\n",
+ "This is a short introduction and quickstart for PySpark DataFrame.
PySpark DataFrame is lazily evaludated and implemented on thetop of
[RDD](https://spark.apache.org/docs/latest/rdd-programming-guide.html#overview).
When the data is
[transformed](https://spark.apache.org/docs/latest/rdd-programming-guide.html#transformations),
it does not actually compute but plans how to compute later. When the
[actions](https://spark.apache.org/docs/latest/rdd-programming-guide.html#actions)
such as `collect()` are explicitly called, the computation starts.\n",
+ "This notebook shows the basic usages of the DataFrame, geared mainly for
new users. You can run the latest version of these examples by yourself on a
live notebook
[here](https://mybinder.org/v2/gh/databricks/apache/master?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart.ipynb).\n",
+ "\n",
+ "There are also other useful information in Apache Spark documentation
site, see the latest version of [Spark SQL and
DataFrames](https://spark.apache.org/docs/latest/sql-programming-guide.html),
[RDD Programming
Guide](https://spark.apache.org/docs/latest/rdd-programming-guide.html),
[Structured Streaming Programming
Guide](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html),
[Spark Streaming Programming
Guide](https://spark.apache.org/docs/latest/streaming-programming-guide.html)
and [Machine Learning Library (MLlib)
Guide](https://spark.apache.org/docs/latest/ml-guide.html).\n",
+ "\n",
+ "Usually PySaprk applications start with initializing `SparkSession` which
is the entry point of PySpark as below. In case of running it in PySpark shell
via <code>pyspark</code> executable, the shell automatically creates the
session in the variable <code>spark</code> for users."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.sql import SparkSession\n",
+ "\n",
+ "spark = SparkSession.builder.getOrCreate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## DataFrame Creation\n",
+ "\n",
+ "A PySpark DataFrame can be created via
`pyspark.sql.SparkSession.createDataFrame` typically by passing a list of
lists, tuples, dictionaries and `pyspark.sql.Row`s, a [pandas
DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
and an RDD consisting of such a list.\n",
Review comment:
~Sure, will do.~
Done: https://github.com/apache/spark/pull/29510
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