HyukjinKwon commented on a change in pull request #29491:
URL: https://github.com/apache/spark/pull/29491#discussion_r474536418
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File path: python/docs/source/getting_started/quickstart.ipynb
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@@ -0,0 +1,1177 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quickstart\n",
+ "\n",
+ "This is a short introduction and quickstart for the PySpark DataFrame
API. PySpark DataFrames are lazily evaluated. They are implemented on top of
[RDD](https://spark.apache.org/docs/latest/rdd-programming-guide.html#overview)s.
When Spark
[transforms](https://spark.apache.org/docs/latest/rdd-programming-guide.html#transformations)
data, it does not immediately compute the transformation but plans how to
compute later. When
[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",
+ "`pyspark.sql.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.\n",
+ "\n",
+ "The example below creates a PySpark DataFrame from a list of rows"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import datetime\n",
+ "import pandas as pd\n",
+ "from pyspark.sql import Row\n",
+ "\n",
+ "df = spark.createDataFrame([\n",
+ " Row(a=1, b=2., c='string1', d=datetime.date(2000, 1, 1),
e=datetime.datetime(2000, 1, 1, 12, 0)),\n",
+ " Row(a=2, b=3., c='string2', d=datetime.date(2000, 2, 1),
e=datetime.datetime(2000, 1, 2, 12, 0)),\n",
+ " Row(a=4, b=5., c='string3', d=datetime.date(2000, 3, 1),
e=datetime.datetime(2000, 1, 3, 12, 0))\n",
+ "])\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a PySpark DataFrame with the schema."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = spark.createDataFrame([\n",
+ " (1, 2., 'string1', datetime.date(2000, 1, 1), datetime.datetime(2000,
1, 1, 12, 0)),\n",
+ " (2, 3., 'string2', datetime.date(2000, 2, 1), datetime.datetime(2000,
1, 2, 12, 0)),\n",
+ " (3, 4., 'string3', datetime.date(2000, 3, 1), datetime.datetime(2000,
1, 3, 12, 0))\n",
+ "], schema='a long, b double, c string, d date, e timestamp')\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a PySpark DataFrame from a pandas DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pandas_df = pd.DataFrame({\n",
+ " 'a': [1, 2, 3],\n",
+ " 'b': [2., 3., 4.],\n",
+ " 'c': ['string1', 'string2', 'string3'],\n",
+ " 'd': [datetime.date(2000, 1, 1), datetime.date(2000, 2, 1),
datetime.date(2000, 3, 1)],\n",
+ " 'e': [datetime.datetime(2000, 1, 1, 12, 0), datetime.datetime(2000,
1, 2, 12, 0), datetime.datetime(2000, 1, 3, 12, 0)]\n",
+ "})\n",
+ "df = spark.createDataFrame(pandas_df)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a PySpark DataFrame from an RDD consisting of a list of tuples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rdd = spark.sparkContext.parallelize([\n",
+ " (1, 2., 'string1', datetime.date(2000, 1, 1), datetime.datetime(2000,
1, 1, 12, 0)),\n",
+ " (2, 3., 'string2', datetime.date(2000, 2, 1), datetime.datetime(2000,
1, 2, 12, 0)),\n",
+ " (3, 4., 'string3', datetime.date(2000, 3, 1), datetime.datetime(2000,
1, 3, 12, 0))\n",
+ "])\n",
+ "df = spark.createDataFrame(rdd, schema=['a', 'b', 'c', 'd', 'e'])\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The DataFrames created above all have the same results and schema."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+---+-------+----------+-------------------+\n",
+ "| a| b| c| d| e|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|\n",
+ "| 2|3.0|string2|2000-02-01|2000-01-02 12:00:00|\n",
+ "| 3|4.0|string3|2000-03-01|2000-01-03 12:00:00|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "\n",
+ "root\n",
+ " |-- a: long (nullable = true)\n",
+ " |-- b: double (nullable = true)\n",
+ " |-- c: string (nullable = true)\n",
+ " |-- d: date (nullable = true)\n",
+ " |-- e: timestamp (nullable = true)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# All DataFrames above result same.\n",
+ "df.show()\n",
+ "df.printSchema()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Viewing Data\n",
+ "\n",
+ "The top rows of a DataFrame can be displayed using `DataFrame.show()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+---+-------+----------+-------------------+\n",
+ "| a| b| c| d| e|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "only showing top 1 row\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternatively, you can enable `spark.sql.repl.eagerEval.enabled`
configuration for the eager evaluation of PySpark DataFrame in notebooks such
as Jupyter. The number of rows to show can be controled via
`spark.sql.repl.eagerEval.maxNumRows` configuration."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table border='1'>\n",
+ "<tr><th>a</th><th>b</th><th>c</th><th>d</th><th>e</th></tr>\n",
+
"<tr><td>1</td><td>2.0</td><td>string1</td><td>2000-01-01</td><td>2000-01-01
12:00:00</td></tr>\n",
+
"<tr><td>2</td><td>3.0</td><td>string2</td><td>2000-02-01</td><td>2000-01-02
12:00:00</td></tr>\n",
+
"<tr><td>3</td><td>4.0</td><td>string3</td><td>2000-03-01</td><td>2000-01-03
12:00:00</td></tr>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "spark.conf.set('spark.sql.repl.eagerEval.enabled', True)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The rows can also be shown vertically. This is useful when rows are too
long to show horizontally."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-RECORD 0------------------\n",
+ " a | 1 \n",
+ " b | 2.0 \n",
+ " c | string1 \n",
+ " d | 2000-01-01 \n",
+ " e | 2000-01-01 12:00:00 \n",
+ "only showing top 1 row\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show(1, vertical=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see the DataFrame's schema and column names as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['a', 'b', 'c', 'd', 'e']"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "root\n",
+ " |-- a: long (nullable = true)\n",
+ " |-- b: double (nullable = true)\n",
+ " |-- c: string (nullable = true)\n",
+ " |-- d: date (nullable = true)\n",
+ " |-- e: timestamp (nullable = true)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.printSchema()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Show the summary of the DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+---+---+-------+\n",
+ "|summary| a| b| c|\n",
+ "+-------+---+---+-------+\n",
+ "| count| 3| 3| 3|\n",
+ "| mean|2.0|3.0| null|\n",
+ "| stddev|1.0|1.0| null|\n",
+ "| min| 1|2.0|string1|\n",
+ "| max| 3|4.0|string3|\n",
+ "+-------+---+---+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.describe().show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`DataFrame.collect()` collects the distributed data to the driver side as
the local data in Python. Note that this can throw an out-of-memory error when
the dataset is too larget to fit in the driver side because it collects all the
data from executors to the driver side."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Row(a=1, b=2.0, c='string1', d=datetime.date(2000, 1, 1),
e=datetime.datetime(2000, 1, 1, 12, 0)),\n",
+ " Row(a=2, b=3.0, c='string2', d=datetime.date(2000, 2, 1),
e=datetime.datetime(2000, 1, 2, 12, 0)),\n",
+ " Row(a=3, b=4.0, c='string3', d=datetime.date(2000, 3, 1),
e=datetime.datetime(2000, 1, 3, 12, 0))]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.collect()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to avoid throwing an out-of-memory exception, use
`DataFrame.take()` or `DataFrame.tail()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Row(a=1, b=2.0, c='string1', d=datetime.date(2000, 1, 1),
e=datetime.datetime(2000, 1, 1, 12, 0))]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.take(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "PySpark DataFrame also provides the conversion back to a [pandas
DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
in order to leverage pandas APIs. Note that `toPandas` also collects all data
into the driver side that can easily cause an out-of-memory-error when the data
is too large to fit into the driver side."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>a</th>\n",
+ " <th>b</th>\n",
+ " <th>c</th>\n",
+ " <th>d</th>\n",
+ " <th>e</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>2.0</td>\n",
+ " <td>string1</td>\n",
+ " <td>2000-01-01</td>\n",
+ " <td>2000-01-01 12:00:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>3.0</td>\n",
+ " <td>string2</td>\n",
+ " <td>2000-02-01</td>\n",
+ " <td>2000-01-02 12:00:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>4.0</td>\n",
+ " <td>string3</td>\n",
+ " <td>2000-03-01</td>\n",
+ " <td>2000-01-03 12:00:00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " a b c d e\n",
+ "0 1 2.0 string1 2000-01-01 2000-01-01 12:00:00\n",
+ "1 2 3.0 string2 2000-02-01 2000-01-02 12:00:00\n",
+ "2 3 4.0 string3 2000-03-01 2000-01-03 12:00:00"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.toPandas()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Selecting and Accessing Data\n",
+ "\n",
+ "PySpark DataFrame is lazily evaluated and simply selecting a column does
not trigger the computation but it returns a `Column` instance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Column<b'a'>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.a"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In fact, most of column-wise operations return `Column`s."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyspark.sql import Column\n",
+ "from pyspark.sql.functions import upper\n",
+ "\n",
+ "type(df.c) == type(upper(df.c)) == type(df.c.isNull())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These `Column`s can be used to select the columns from a DataFrame. For
example, `DataFrame.select()` takes the `Column` instances that returns another
DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+\n",
+ "| c|\n",
+ "+-------+\n",
+ "|string1|\n",
+ "|string2|\n",
+ "|string3|\n",
+ "+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select(df.c).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Assign new `Column` instance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+---+-------+----------+-------------------+-------+\n",
+ "| a| b| c| d| e|upper_c|\n",
+ "+---+---+-------+----------+-------------------+-------+\n",
+ "| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|STRING1|\n",
+ "| 2|3.0|string2|2000-02-01|2000-01-02 12:00:00|STRING2|\n",
+ "| 3|4.0|string3|2000-03-01|2000-01-03 12:00:00|STRING3|\n",
+ "+---+---+-------+----------+-------------------+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.withColumn('upper_c', upper(df.c)).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to select rows instead of columns, use `DataFrame.filter()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+---+-------+----------+-------------------+\n",
+ "| a| b| c| d| e|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter(df.a == 1).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Applying a Function\n",
+ "\n",
+ "PySpark supports various UDFs and APIs to allow users to execute Python
native functions. See also the latest [Pandas
UDFs](https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-udfs-aka-vectorized-udfs)
and [Pandas Function
APIs](https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-function-apis).
For instance, the example below allows users to directly use the APIs in [a
pandas
Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html)
within Python native function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------------+\n",
+ "|pandas_plus_one(a)|\n",
+ "+------------------+\n",
+ "| 2|\n",
+ "| 3|\n",
+ "| 4|\n",
+ "+------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas\n",
+ "from pyspark.sql.functions import pandas_udf\n",
+ "\n",
+ "@pandas_udf('long')\n",
+ "def pandas_plus_one(series: pd.Series) -> pd.Series:\n",
+ " # Simply plus one by using pandas Series.\n",
+ " return series + 1\n",
+ "\n",
+ "df.select(pandas_plus_one(df.a)).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another example is `DataFrame.mapInPandas` which allows users directly
use the APIs in a [pandas
DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
without any restrictions such as the result length."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+---+-------+----------+-------------------+\n",
+ "| a| b| c| d| e|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|\n",
+ "+---+---+-------+----------+-------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "def pandas_filter_func(iterator):\n",
+ " for pandas_df in iterator:\n",
+ " yield pandas_df[pandas_df.a == 1]\n",
+ "\n",
+ "df.mapInPandas(pandas_filter_func, schema=df.schema).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Grouping Data\n",
+ "\n",
+ "PySpark DataFrame also provides a way of handling grouped data by using
the common approach, split-apply-combine strategy.\n",
+ "It groups the data by a certain condition, applies a function to each
group, and then combines them back to the DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----+------+---+---+\n",
+ "|color| fruit| v1| v2|\n",
+ "+-----+------+---+---+\n",
+ "| red|banana| 1| 10|\n",
+ "| blue|banana| 2| 20|\n",
+ "| red|carrot| 3| 30|\n",
+ "| blue| grape| 4| 40|\n",
+ "| red|carrot| 5| 50|\n",
+ "|black|carrot| 6| 60|\n",
+ "| red|banana| 7| 70|\n",
+ "| red| grape| 8| 80|\n",
+ "+-----+------+---+---+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = spark.createDataFrame([\n",
+ " ['red', 'banana', 1, 10], ['blue', 'banana', 2, 20], ['red',
'carrot', 3, 30],\n",
+ " ['blue', 'grape', 4, 40], ['red', 'carrot', 5, 50], ['black',
'carrot', 6, 60],\n",
+ " ['red', 'banana', 7, 70], ['red', 'grape', 8, 80]], schema=['color',
'fruit', 'v1', 'v2'])\n",
+ "df.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Grouping and then applying the avg() function to the resulting groups."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----+-------+-------+\n",
+ "|color|avg(v1)|avg(v2)|\n",
+ "+-----+-------+-------+\n",
+ "| red| 4.8| 48.0|\n",
+ "|black| 6.0| 60.0|\n",
+ "| blue| 3.0| 30.0|\n",
+ "+-----+-------+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.groupby('color').avg().show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also apply a Python native function against each group by using
pandas APIs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----+------+---+---+\n",
+ "|color| fruit| v1| v2|\n",
+ "+-----+------+---+---+\n",
+ "| red|banana| -3| 10|\n",
+ "| red|carrot| -1| 30|\n",
+ "| red|carrot| 0| 50|\n",
+ "| red|banana| 2| 70|\n",
+ "| red| grape| 3| 80|\n",
+ "|black|carrot| 0| 60|\n",
+ "| blue|banana| -1| 20|\n",
+ "| blue| grape| 1| 40|\n",
+ "+-----+------+---+---+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "def plus_mean(pandas_df):\n",
+ " return pandas_df.assign(v1=pandas_df.v1 - pandas_df.v1.mean())\n",
+ "\n",
+ "df.groupby('color').applyInPandas(plus_mean, schema=df.schema).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Co-grouping and applying a function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+--------+---+---+---+\n",
+ "| time| id| v1| v2|\n",
+ "+--------+---+---+---+\n",
+ "|20000101| 1|1.0| x|\n",
+ "|20000102| 1|3.0| x|\n",
+ "|20000101| 2|2.0| y|\n",
+ "|20000102| 2|4.0| y|\n",
+ "+--------+---+---+---+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df1 = spark.createDataFrame(\n",
+ " [(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0),
(20000102, 2, 4.0)],\n",
+ " ('time', 'id', 'v1'))\n",
+ "\n",
+ "df2 = spark.createDataFrame(\n",
+ " [(20000101, 1, 'x'), (20000101, 2, 'y')],\n",
+ " ('time', 'id', 'v2'))\n",
+ "\n",
+ "def asof_join(l, r):\n",
+ " return pd.merge_asof(l, r, on='time', by='id')\n",
+ "\n",
+ "df1.groupby('id').cogroup(df2.groupby('id')).applyInPandas(\n",
+ " asof_join, schema='time int, id int, v1 double, v2 string').show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Data in/out\n",
+ "\n",
+ "CSV is straightforward and easy to use. Parquet and ORC are efficient and
compact file formats to read and write faster.\n",
+ "\n",
+ "There are many other data sources available in PySpark such as JDBC,
text, binaryFile, Avro, etc. See also the latest [Spark SQL, DataFrames and
Datasets
Guide](https://spark.apache.org/docs/latest/sql-programming-guide.html) in
Apache Spark documentation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### CSV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----+------+---+---+\n",
+ "|color| fruit| v1| v2|\n",
+ "+-----+------+---+---+\n",
+ "| red|banana| 1| 10|\n",
+ "| blue|banana| 2| 20|\n",
+ "| red|carrot| 3| 30|\n",
+ "| blue| grape| 4| 40|\n",
+ "| red|carrot| 5| 50|\n",
+ "|black|carrot| 6| 60|\n",
+ "| red|banana| 7| 70|\n",
+ "| red| grape| 8| 80|\n",
+ "+-----+------+---+---+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.write.csv('foo.csv', header=True)\n",
+ "spark.read.csv('foo.csv', header=True).show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Parquet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----+------+---+---+\n",
+ "|color| fruit| v1| v2|\n",
+ "+-----+------+---+---+\n",
+ "| red|banana| 1| 10|\n",
+ "| blue|banana| 2| 20|\n",
+ "| red|carrot| 3| 30|\n",
+ "| blue| grape| 4| 40|\n",
+ "| red|carrot| 5| 50|\n",
+ "|black|carrot| 6| 60|\n",
+ "| red|banana| 7| 70|\n",
+ "| red| grape| 8| 80|\n",
+ "+-----+------+---+---+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.write.parquet('bar.parquet')\n",
+ "spark.read.parquet('bar.parquet').show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ORC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----+------+---+---+\n",
+ "|color| fruit| v1| v2|\n",
+ "+-----+------+---+---+\n",
+ "| red|banana| 1| 10|\n",
+ "| blue|banana| 2| 20|\n",
+ "| red|carrot| 3| 30|\n",
+ "| blue| grape| 4| 40|\n",
+ "| red|carrot| 5| 50|\n",
+ "|black|carrot| 6| 60|\n",
+ "| red|banana| 7| 70|\n",
+ "| red| grape| 8| 80|\n",
+ "+-----+------+---+---+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.write.orc('zoo.orc')\n",
+ "spark.read.orc('zoo.orc').show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Working with SQL\n",
Review comment:
I put a couple of more items in "Working with SQL" here
https://hyukjin-spark.readthedocs.io/en/stable/getting_started/quickstart.html#Working-with-SQL
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