Python大数据处理利器，PySpark的入门实战
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PySpark极速入门
一Pyspark简介与安装
什么是Pyspark
PySpark是Spark的Python语言接口通过它可以使用Python API编写Spark应用程序目前支持绝大多数Spark功能。目前Spark官方在其支持的所有语言中将Python置于首位。
如何安装
在终端输入
pip intsall pyspark
或者使用pycharm在GUI界面安装
二编程实践
加载、转换数据
# 导入pyspark
# 导入pandas, 稍后与pyspark中的数据结构做对比
import pyspark
import pandas as pd
在编写spark程序前我们要创建一个SparkSession对象
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Spark极速入门").getOrCreate()
可以看到会话的一些信息使用的Spark版本、运行模式、应用程序名字
演示环境用的是local本地模式 * 代表的是使用全部线程 如果想用集群模式的话可以去查看集群搭建的相关教程 届时pyspark程序作为spark的客户端设置连接集群就是真正的分布式计算了 目前只是本地模式用多线程去模拟分布式计算。
spark
看看我们将用到的test1数据吧
使用read方法用option设置是否读取csv的头再指定路径就可以读取数据了
df_spark = spark.read.option("header", "true").csv("./data/test1.csv")
看看是什么类型
type(df_spark)
pyspark.sql.dataframe.DataFrame再看看用pandas读取是什么类型
type(pd.read_csv("./data/test1.csv"))
pandas.core.frame.DataFrame可以发现Spark读取这种结构化数据时用的也是和pandas类似的dataframe结构 这也是Spark应用最广泛的数据结构
使用show方法打印数据
df_spark.show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
+---------+---+----------+------+使用printSchema方法打印元数据信息发现明明是数值类型的它却读取为了字符串类型
df_spark.printSchema()
root
|-- Name: string (nullable = true)
|-- age: string (nullable = true)
|-- Experience: string (nullable = true)
|-- Salary: string (nullable = true)在读取时加上类型推断,发现此时已经能正确读取了
df_spark = spark.read.option("header", "true").csv("./data/test1.csv",inferSchema=True)
df_spark.printSchema()
root
|-- Name: string (nullable = true)
|-- age: integer (nullable = true)
|-- Experience: integer (nullable = true)
|-- Salary: integer (nullable = true)选择某些列, 可以发现不管选多列还是选单列返回的都是dataframe 返回的也同样可以printSchema、show等dataframe使用的方法做到了结构的统一
df_spark.select(["Name", "age"])
DataFrame[Name: string, age: int]df_spark.select("Name")
DataFrame[Name: string]df_spark.select(["Name", "age", "Salary"]).printSchema()
root
|-- Name: string (nullable = true)
|-- age: integer (nullable = true)
|-- Salary: integer (nullable = true)不用select而用[]直接选取就有点类似与pandas的series了
df_spark["Name"]
Column<'Name'>column就不能直接show了
df_spark["age"].show()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [15], in <cell line: 1>()
----> 1 df_spark["age"].show()
TypeError: 'Column' object is not callable用describe方法可以对dataframe做一些简单的统计
df_spark.describe().show()
+-------+------+------------------+-----------------+------------------+
|summary| Name| age| Experience| Salary|
+-------+------+------------------+-----------------+------------------+
| count| 6| 6| 6| 6|
| mean| null|26.333333333333332|4.666666666666667|21333.333333333332|
| stddev| null| 4.179314138308661|3.559026084010437| 5354.126134736337|
| min|Harsha| 21| 1| 15000|
| max| Sunny| 31| 10| 30000|
+-------+------+------------------+-----------------+------------------+用withColumn方法给dataframe加上一列
df_spark = df_spark.withColumn("Experience After 3 year", df_spark["Experience"] + 3)
df_spark.show()
+---------+---+----------+------+-----------------------+
| Name|age|Experience|Salary|Experience After 3 year|
+---------+---+----------+------+-----------------------+
| Krish| 31| 10| 30000| 13|
|Sudhanshu| 30| 8| 25000| 11|
| Sunny| 29| 4| 20000| 7|
| Paul| 24| 3| 20000| 6|
| Harsha| 21| 1| 15000| 4|
| Shubham| 23| 2| 18000| 5|
+---------+---+----------+------+-----------------------+用drop方法删除列
df_spark = df_spark.drop("Experience After 3 year")
df_spark.show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
+---------+---+----------+------+用withColumnRename方法重命名列
df_spark.withColumnRenamed("Name", "New Name").show()
+---------+---+----------+------+
| New Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
+---------+---+----------+------+处理缺失值
看看接下来要带缺失值的test2数据吧
CSeoe.png
df_spark = spark.read.csv("./data/test2.csv", header=True, inferSchema=True)
df_spark.show()
+---------+----+----------+------+
| Name| age|Experience|Salary|
+---------+----+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
| Mahesh|null| null| 40000|
| null| 34| 10| 38000|
| null| 36| null| null|
+---------+----+----------+------+用na.drop删除缺失值 how参数设置策略any意思是只要一行里有缺失值那就删了 any也是how的默认参数
df_spark.na.drop(how="any").show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
+---------+---+----------+------+可以通过thresh参数设置阈值代表超过一行中缺失值的数量超过这个值才会被删除
df_spark.na.drop(how="any", thresh=2).show()
+---------+----+----------+------+
| Name| age|Experience|Salary|
+---------+----+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
| Mahesh|null| null| 40000|
| null| 34| 10| 38000|
+---------+----+----------+------+也可以用subset参数设置关注的列 下面代码意思是在Experience列中只要有缺失值就删掉
df_spark.na.drop(how="any", subset=["Experience"]).show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
| null| 34| 10| 38000|
+---------+---+----------+------+用fillna填充缺失值, 可以用字典对各列的填充值进行设置
df_spark.fillna({'Name': 'unknown', 'age': 18, 'Experience': 0, 'Salary': 0}).show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
| Mahesh| 18| 0| 40000|
| unknown| 34| 10| 38000|
| unknown| 36| 0| 0|
+---------+---+----------+------+还可以调用机器学习模块的相关方法 通过设置策略可以用平均数、众数等方式填充
from pyspark.ml.feature import Imputer
imputer = Imputer(
inputCols = ['age', 'Experience', 'Salary'],
outputCols = [f"{c}_imputed" for c in ['age', 'Experience', 'Salary']]
).setStrategy("mean")
imputer.fit(df_spark).transform(df_spark).show()
+---------+----+----------+------+-----------+------------------+--------------+
| Name| age|Experience|Salary|age_imputed|Experience_imputed|Salary_imputed|
+---------+----+----------+------+-----------+------------------+--------------+
| Krish| 31| 10| 30000| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000| 30| 8| 25000|
| Sunny| 29| 4| 20000| 29| 4| 20000|
| Paul| 24| 3| 20000| 24| 3| 20000|
| Harsha| 21| 1| 15000| 21| 1| 15000|
| Shubham| 23| 2| 18000| 23| 2| 18000|
| Mahesh|null| null| 40000| 28| 5| 40000|
| null| 34| 10| 38000| 34| 10| 38000|
| null| 36| null| null| 36| 5| 25750|
+---------+----+----------+------+-----------+------------------+--------------+过滤操作
还是切换到test1数据
df_spark = spark.read.csv("./data/test1.csv", header=True, inferSchema=True)
df_spark.show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
+---------+---+----------+------+可以使用filter方法对数据进行过滤操作类似于SQL中的where 可以使用字符串的方式也可以利用column方式去传递条件
df_spark.filter("Salary <= 20000").show()
+-------+---+----------+------+
| Name|age|Experience|Salary|
+-------+---+----------+------+
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
|Shubham| 23| 2| 18000|
+-------+---+----------+------+df_spark.filter(df_spark["Salary"]<=20000).show()
+-------+---+----------+------+
| Name|age|Experience|Salary|
+-------+---+----------+------+
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
|Shubham| 23| 2| 18000|
+-------+---+----------+------+如果是字符串用 and 表示同时满足多个条件 如果是用column用( & ) 连接多个条件
df_spark.filter("Salary <= 20000 and age <= 24").show()
+-------+---+----------+------+
| Name|age|Experience|Salary|
+-------+---+----------+------+
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
|Shubham| 23| 2| 18000|
+-------+---+----------+------+df_spark.filter(
(df_spark["Salary"]<=20000)
& (df_spark["age"]<=24)
).show()
+-------+---+----------+------+
| Name|age|Experience|Salary|
+-------+---+----------+------+
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
|Shubham| 23| 2| 18000|
+-------+---+----------+------+column中用|表示或 ~表示取反
df_spark.filter(
(df_spark["Salary"]<=20000)
| (df_spark["age"]<=24)
).show()
+-------+---+----------+------+
| Name|age|Experience|Salary|
+-------+---+----------+------+
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
|Shubham| 23| 2| 18000|
+-------+---+----------+------+df_spark.filter(
(df_spark["Salary"]<=20000)
| ~(df_spark["age"]<=24)
).show()
+---------+---+----------+------+
| Name|age|Experience|Salary|
+---------+---+----------+------+
| Krish| 31| 10| 30000|
|Sudhanshu| 30| 8| 25000|
| Sunny| 29| 4| 20000|
| Paul| 24| 3| 20000|
| Harsha| 21| 1| 15000|
| Shubham| 23| 2| 18000|
+---------+---+----------+------+分组聚合
换一个数据集test3
df_spark = spark.read.csv("./data/test3.csv", header=True, inferSchema=True)
df_spark.show()
+---------+------------+------+
| Name| Departments|salary|
+---------+------------+------+
| Krish|Data Science| 10000|
| Krish| IOT| 5000|
| Mahesh| Big Data| 4000|
| Krish| Big Data| 4000|
| Mahesh|Data Science| 3000|
|Sudhanshu|Data Science| 20000|
|Sudhanshu| IOT| 10000|
|Sudhanshu| Big Data| 5000|
| Sunny|Data Science| 10000|
| Sunny| Big Data| 2000|
+---------+------------+------+使用groupby方法对dataframe某些列进行分组
df_spark.groupBy("Name")
<pyspark.sql.group.GroupedData at 0x227454d4be0>可以看到分组的结果是GroupedData对象它不能使用show等方法打印 GroupedData对象需要进行聚合操作才能重新转换为dataframe 聚合函数有sum、count、avg、max、min等
df_spark.groupBy("Departments").sum().show()
+------------+-----------+
| Departments|sum(salary)|
+------------+-----------+
| IOT| 15000|
| Big Data| 15000|
|Data Science| 43000|
+------------+-----------+三总结
Pandas的dataframe与PySpark的dataframe有许多相似之处熟悉Pandas的同学可以很快适应它的API。目前可以粗浅地把PySpark理解为”分布式的Pandas“不过PySpark还有分布式机器学习的功能——Spark MLlib可以理解为分布式的Sklearn、TensorFlow等后续会给大家介绍。在集群中它的dataframe可以分布在不同的机器上以此处理海量数据。有兴趣的小伙伴可以通过虚拟机搭建一个Spark集群进一步学习Spark。
Apache Spark™ - 用于大规模数据分析的统一引擎