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Python Spark DataFrame 基础

df = spark.read.parquet('/sql/users.parquet')

df.show()

+------+--------------+----------------+

| name|favorite_color|favorite_numbers|

+------+--------------+----------------+

|Alyssa| null| [3, 9, 15, 20]|

| Ben| red| []|

+------+--------------+----------------+

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Python Spark DataFrame 聚合统计

CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)

0001,Male,19,15,39

0002,Male,21,15,81

0003,Female,20,16,6

0004,Female,23,16,77

0005,Female,31,17,40

0006,Female,22,17,76

df = spark.read.csv('/sql/customers.csv',header=True)

df.printSchema()

df.show()

root

|-- CustomerID: string (nullable = true)

|-- Genre: string (nullable = true)

|-- Age: string (nullable = true)

|-- Annual Income (k$): string (nullable = true)

|-- Spending Score (1-100): string (nullable = true)

+----------+------+---+------------------+----------------------+

|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|

+----------+------+---+------------------+----------------------+

| 0001| Male| 19| 15| 39|

| 0002| Male| 21| 15| 81|

| 0003|Female| 20| 16| 6|

| 0004|Female| 23| 16| 77|

| 0005|Female| 31| 17| 40|

| 0006|Female| 22| 17| 76|

| 0007|Female| 35| 18| 6|

| 0008|Female| 23| 18| 94|

| 0009| Male| 64| 19| 3|

| 0010|Female| 30| 19| 72|

| 0011| Male| 67| 19| 14|

| 0012|Female| 35| 19| 99|

| 0013|Female| 58| 20| 15|

| 0014|Female| 24| 20| 77|

| 0015| Male| 37| 20| 13|

| 0016| Male| 22| 20| 79|

| 0017|Female| 35| 21| 35|

| 0018| Male| 20| 21| 66|

| 0019| Male| 52| 23| 29|

| 0020|Female| 35| 23| 98|

+----------+------+---+------------------+----------------------+

df.agg({"Age": "max","Annual Income (k$)":"mean","Spending Score (1-100)":"mean"}).show()

+---------------------------+-----------------------+--------+

|avg(Spending Score (1-100))|avg(Annual Income (k$))|max(Age)|

+---------------------------+-----------------------+--------+

| 50.2| 60.56| 70|

+---------------------------+-----------------------+--------+

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alias(alias)为DataFrame定义一个别名,稍后再函数中就可以利用这个别名来做相关的运 算，例如说自关联Join:

df1 = df.alias('cus1')

type(df1)

df2 = df.alias('cus2')

df3 = df1.join(df2,col('cus1.CustomerId')==col('cus2.CustomerId'),'inner')

df3.count()

200

+----------+------+---+------------------+----------------------+----------+------+---+------------------+----------------------+

|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|

+----------+------+---+------------------+----------------------+----------+------+---+------------------+----------------------+

| 0001| Male| 19| 15| 39| 0001| Male| 19| 15| 39|

| 0002| Male| 21| 15| 81| 0002| Male| 21| 15| 81|

| 0003|Female| 20| 16| 6| 0003|Female| 20| 16| 6|

| 0004|Female| 23| 16| 77| 0004|Female| 23| 16| 77|

| 0005|Female| 31| 17| 40| 0005|Female| 31| 17| 40|

| 0006|Female| 22| 17| 76| 0006|Female| 22| 17| 76|

| 0007|Female| 35| 18| 6| 0007|Female| 35| 18| 6|

| 0008|Female| 23| 18| 94| 0008|Female| 23| 18| 94|

| 0009| Male| 64| 19| 3| 0009| Male| 64| 19| 3|

| 0010|Female| 30| 19| 72| 0010|Female| 30| 19| 72|

+----------+------+---+------------------+----------------------+----------+------+---+------------------+----------------------+

only showing top 10 rows

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cache()，将DataFrame缓存到StorageLevel对应的缓存级别中，默认是 MEMORY_AND_DISK

df = spark.read.csv('/sql/customers.csv',header=True)

a = df.cache()

a.show()

+----------+------+---+------------------+----------------------+

|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|

+----------+------+---+------------------+----------------------+

| 0001| Male| 19| 15| 39|

| 0002| Male| 21| 15| 81|

| 0003|Female| 20| 16| 6|

| 0004|Female| 23| 16| 77|

| 0005|Female| 31| 17| 40|

| 0006|Female| 22| 17| 76|

| 0007|Female| 35| 18| 6|

| 0008|Female| 23| 18| 94|

| 0009| Male| 64| 19| 3|

| 0010|Female| 30| 19| 72|

| 0011| Male| 67| 19| 14|

| 0012|Female| 35| 19| 99|

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checkpoint(eager=True) 对DataFrame设置断点，这个方法是Spark2.1引入的方法，这个方法的调用会斩断在这个 DataFrame上的逻辑执行计划，将前后的依赖关系持久化到checkpoint文件中去。

sc

sc.setCheckpointDir('/datas/checkpoint')

a.checkpoint()

a.show()

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coalesce(numPartitions) 重分区算法，传入的参数是DataFrame的分区数量。

注意通过read方法读取文件，创建的DataFrame默认的分区数为文件的个数，即一个文件对

应一个分区，在分区数少于coalesce指定的分区数的时候，调用coalesce是不起作用的

df = spark.read.csv('/sql/customers.csv',header=True)

df.rdd.getNumPartitions()

1

spark.read.csv('/sql/customers.csv',header=True).coalesce(3).rdd.getNumPartitions()

1

df = spark.range(0,20,2,3)

df.rdd.getNumPartitions()

df.coalesce(2).rdd.getNumPartitions()

2

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repartition(numPartitions, *cols)这个方法和coalesce(numPartitions) 方法一样，都是 对DataFrame进行重新的分区，但是repartition这个方法会使用hash算法，在整个集群中进 行shuffle，效率较低。repartition方法不仅可以指定分区数，还可以指定按照哪些列来做分 区。

df = spark.read.csv('/sql/customers.csv',header=True)

df.rdd.getNumPartitions()

1

df2 = df.repartition(3)

df2.rdd.getNumPartitions()

3

df2.columns

df3 = df2.repartition(6,'Genre')

df3.show(20)

+----------+------+---+------------------+----------------------+

|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|

+----------+------+---+------------------+----------------------+

| 0003|Female| 20| 16| 6|

| 0004|Female| 23| 16| 77|

| 0005|Female| 31| 17| 40|

| 0006|Female| 22| 17| 76|

| 0007|Female| 35| 18| 6|

| 0008|Female| 23| 18| 94|

| 0010|Female| 30| 19| 72|

| 0012|Female| 35| 19| 99|

| 0013|Female| 58| 20| 15|

df3.rdd.getNumPartitions()

6

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colRegex(colName)用正则表达式的方式返回我们想要的列。

df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "a"])

df.select(df.colRegex("`(Col1)?+.+`")).show()

+---+

| a|

+---+

| 1|

| 2|

| 3|

+---+

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collect()，返回DataFrame中的所有数据，注意数据量大了容易造成Driver节点内存溢 出！

df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "a"])

df.collect()

[Row(Col1='a', a=1), Row(Col1='b', a=2), Row(Col1='c', a=3)]

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columns,以列表的形式返回DataFrame的所有列名

df = spark.read.csv('/sql/customers.csv',header=True)

df.columns

df = spark.read.csv('/sql/customers.csv',header=True)

df.columns

['CustomerID', 'Genre', 'Age', 'Annual Income (k$)', 'Spending Score (1-100)']

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SparkSQL DataFrame 转换为 PandasDataFrame

df = spark.read.csv('/sql/customers.csv',header=True)

pdf = df.toPandas()

pdf.info()

RangeIndex: 200 entries, 0 to 199

Data columns (total 5 columns):

CustomerID 200 non-null object

Genre 200 non-null object

Age 200 non-null object

Annual Income (k$) 200 non-null object

Spending Score (1-100) 200 non-null object

dtypes: object(5)

memory usage: 7.9+ KB

pdf['Age'] = pdf['Age'].astype('int')

pdf["Annual Income (k$)"]=pdf["Annual Income (k$)"].astype('int')

pdf["Spending Score (1-100)"]=pdf["Spending Score (1-100)"].astype('int')

pdf.info()

RangeIndex: 200 entries, 0 to 199

Data columns (total 5 columns):

CustomerID 200 non-null object

Genre 200 non-null object

Age 200 non-null int64

Annual Income (k$) 200 non-null int64

Spending Score (1-100) 200 non-null int64

dtypes: int64(3), object(2)

memory usage: 7.9+ KB

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PandasDataFrame 转换为 SparkSQL DataFrame

df1 = spark.createDataFrame(pdf)

df1.corr("Age","Annual Income (k$)")

df1.corr("Spending Score (1-100)","Annual Income (k$)")

0.009902848094037492

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count()返回DataFrame中Row的数量

df = spark.read.csv('/sql/customers.csv',header=True)

df.count()

200

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createGlobalTempView(name)使用DataFrame创建一个全局的临时表，其生命周期 和启动的app的周期一致，即启动的spark应用存在则这个临时的表就一直能访问。直到 sparkcontext的stop方法的调用退出应用为止。创建的临时表保存在global_temp这个库 中