【Spark】PySpark DataFrame

1 SparkSession 执行环境入口

from pyspark.sql import SparkSession, Row
from pyspark.sql.types import *

# 构建SparkSession执行环境入口
spark = SparkSession.builder.\
    appName("test").\
    master("local[*]").\
    config("spark.sql.shuffle.partitions", 2).\
    getOrCreate()

sc = spark.sparkContext

2 构建DataFrame

2.1 由rdd构建 (StructType、StructField)

rdd = sc.parallelize([(1001, "Tom", 12), (1002, "Jerry", 13), (1003, "John", 14)])
rdd = rdd.map(lambda x: Row(x[0], x[1], x[2]))

'''
StructType(列名, 列类型, 列是否允许为空) 描述df表结构
StructField 描述一列的信息
Row(1, 'a', 12) 记录一行数据
Column 记录一列数据并包含列信息(StructField)
'''
schema = StructType([StructField("id", LongType(), True)
                    ,StructField("name", StringType(), True)
                    ,StructField("age", IntegerType(), True)])
'''
也可以：
schema = StructType().add('id', LongType(), True)\
                     .add('name', StringType(), True)\
                     .add('age', IntegerType(), True)
'''

df = spark.createDataFrame(rdd, schema)
# 或 # df = spark.createDataFrame(rdd, schema=['id', 'name', 'age'])

'''
param1: 展示多少数据 默认20
param2: 默认True 进行截断，数据长度超过20，后续内容不显示
'''
df.show()

# 打印表结构
df.printSchema()
'''
    +----+-----+---+
    |  id| name|age|
    +----+-----+---+
    |1001|  Tom| 12|
    |1002|Jerry| 13|
    |1003| John| 14|
    +----+-----+---+
    
    root
     |-- id: long (nullable = true)
     |-- name: string (nullable = true)
     |-- age: integer (nullable = true)
'''

'''
数据类型需要在构建rdd时进行指定的转换
'''
df = rdd.toDF(['id', 'name', 'age'])
df.show()
df.printSchema()
'''
    +----+-----+---+
    |  id| name|age|
    +----+-----+---+
    |1001|  Tom| 12|
    |1002|Jerry| 13|
    |1003| John| 14|
    +----+-----+---+
    
    root
     |-- id: long (nullable = true)
     |-- name: string (nullable = true)
     |-- age: long (nullable = true)
'''

# 也可传入schema，指定类型
schema = StructType().add('id', LongType(), True)\
                     .add('name', StringType(), True)\
                     .add('age', IntegerType(), True)
df = rdd.toDF(schema=schema)
df.show()
df.printSchema()
'''
    +----+-----+---+
    |  id| name|age|
    +----+-----+---+
    |1001|  Tom| 12|
    |1002|Jerry| 13|
    |1003| John| 14|
    +----+-----+---+
    
    root
     |-- id: long (nullable = true)
     |-- name: string (nullable = true)
     |-- age: integer (nullable = true)
'''

2.2 由pandas.DataFrame构建

# pandas DataFrame >>> pyspark.sql DataFrame
import pandas as pd

pdf = pd.DataFrame(
    {
        'id': [1, 2, 3],
        'name': ['a', 'b', 'c'],
        'age': [12, 13, 14]
    }
)
df = spark.createDataFrame(pdf)
df.show()
df.printSchema()
'''
    +---+----+---+
    | id|name|age|
    +---+----+---+
    |  1|   a| 12|
    |  2|   b| 13|
    |  3|   c| 14|
    +---+----+---+
    
    root
     |-- id: long (nullable = true)
     |-- name: string (nullable = true)
     |-- age: long (nullable = true)
'''

2.3 由外部数据构建

2.3.1 text数据源

'''
text数据源读取数据时，将一整行的数据 作为一个列的一项数据，默认名称value，类型string
可以用schema指定列的名字和类型
'''
schema = StructType().add('data', StringType(), True)
df = spark.read.format('text').\
    schema(schema=schema).\
    load('/test.text')
'''
+---+---+
|   data|
+-------+
| 1 a 12|
| 2 b 13|
| 3 c 14|
+-------+
'''

2.3.2 json数据源

'''
JSON数据源自带Schema信息
{"name": 'c'}
{"id": 1, "name": 'a'}
{"id": 2, "name": 'b'}
'''
df = spark.read.format('json').load('/test.text')
'''
+----+-----+
| id | name|
+----+-----+
|null|  c  | 
| 1  |  a  |
| 2  |  b  |
+----------+
'''

2.3.3 csv数据源

'''
csv数据源
id,name,age
1,a,12
2,b,13
'''
df = spark.read.format('csv')\
    .option('sep', ',')\ # 指定分隔符
    .option('header', True)\ # csv文件中有表头(列名),True为不用
    .option('encoding', 'utf-8')\ 
    .schema('id INT, name STRING, age INT') # 指定列名和列数据类型
    .load('/test.text')
'''
+---+----+---+
| id|name|age|
+---+----+---+
|  1|   a| 12|
|  2|   b| 13|
+---+----+---+
'''

3 DataFrame 操作

3.1 SQL风格

# 构建临时视图表age，后续可用sql查询
df.createOrReplaceTempView("age")
# 构建全局表，可以跨SparkSession对象使用
# 在使用前 global_temp.
df.createGlobalTempView('global_age')

spark.sql(
    """  
    select * from age limit 2
    """
).show()
'''
    +----+----+---+
    |  id|name|age|
    +----+----+---+
    |1001| Tom| 12|
    |1003|John| 14|
    +----+----+---+
'''

spark.sql(
    """
    select * from global_temp.global_age
    """
).show()
'''
    +----+-----+---+
    |  id| name|age|
    +----+-----+---+
    |1001|  Tom| 12|
    |1002|Jerry| 13|
    |1003| John| 14|
    |1004|  Tom| 15|
    +----+-----+---+
'''

3.2 DSL风格

data = [(1001, "Tom", 12.0)
       ,(1002, "Jerry", 13.0)
       ,(1003, "John", 14.0)
       ,(1004, 'Tom', 15.0)
       ,(1003, "John", 17.353)]

rdd = sc.parallelize(data)
rdd = rdd.map(lambda x: Row(x[0], x[1], x[2]))

schema = StructType([StructField("id", LongType(), True)
                    ,StructField("name", StringType(), True)
                    ,StructField("age", FloatType(), True)])
df = spark.createDataFrame(rdd, schema)
df.show()
'''
    +----+-----+------+
    |  id| name|   age|
    +----+-----+------+
    |1001|  Tom|  12.0|
    |1002|Jerry|  13.0|
    |1003| John|  14.0|
    |1004|  Tom|  15.0|
    |1003| John|17.353|
    +----+-----+------+
'''

3.2.1 df.select() 指定查看某列

df.select('id', 'age').show()
'''
    +----+------+
    |  id|   age|
    +----+------+
    |1001|  12.0|
    |1002|  13.0|
    |1003|  14.0|
    |1004|  15.0|
    |1003|17.353|
    +----+------+
'''
df.select(df.col_name).show()

3.2.2 df.where/filter()

df.filter('age < 14').show()
# 同：
df.where('age < 14').show()
'''
    +----+-----+----+
    |  id| name| age|
    +----+-----+----+
    |1001|  Tom|12.0|
    |1002|Jerry|13.0|
    +----+-----+----+
'''

3.2.3 指定范围筛选数据

3.2.3.1 指定阈值

from pyspark.sql import functions as F

df[df['id'] > 1003].show()
'''
    +----+----+----+
    |  id|name| age|
    +----+----+----+
    |1004| Tom|15.0|
    +----+----+----+
'''

df.where(df['id'] > 1003).show()
'''
    +----+----+----+
    |  id|name| age|
    +----+----+----+
    |1004| Tom|15.0|
    +----+----+----+
'''

df.where((df['id'] < 1003) & (df['id'] > 1001)).show()
'''
    +----+-----+----+
    |  id| name| age|
    +----+-----+----+
    |1002|Jerry|13.0|
    +----+-----+----+
'''

# age大于平均值
df.where(df['age'] >  df.select(F.avg(df['age'])).first()['avg(age)']).show()
'''
    +----+----+------+
    |  id|name|   age|
    +----+----+------+
    |1004| Tom|  15.0|
    |1003|John|17.353|
    +----+----+------+
'''

3.2.3.2 统计null、nan、“”

查找某一列中的Null
df.where(df.col_name.isNull()).show() 返回所有列，且该列值为Null的行
df.where(df.col_name.isNull()).count()可统计该列null值数量
df.select(df.col_name.isNull()).show() 只返回该列，值为true或false
df.select(df.col_name.isNull()).count() 返回的仍是df的所有行数

查找空值(空字符串""被认为是空值)
df.where(df.col_name=="").count()

查找缺失值nan(not a number)
from pyspark.sql.functions import isnan
df.where(isna(df.col_name)).count()

3.2.4 df.orderBy() 排序

df.orderBy('age', ascending=False).show()
'''
    +----+-----+------+
    |  id| name|   age|
    +----+-----+------+
    |1003| John|17.353|
    |1004|  Tom|  15.0|
    |1003| John|  14.0|
    |1002|Jerry|  13.0|
    |1001|  Tom|  12.0|
    +----+-----+------+
'''

3.2.5 df.groupBy().agg() 分组操作

3.2.5.1 {col: func} 指定特征聚合

# groupBy
df.groupBy('name').agg({'id': 'max', 'age': 'max'}).show()
'''
    +-----+-------+--------+
    | name|max(id)|max(age)|
    +-----+-------+--------+
    |  Tom|   1004|    15.0|
    | John|   1003|  17.353|
    |Jerry|   1002|    13.0|
    +-----+-------+--------+
'''

• F 实现复杂聚合

from pyspark.sql import functions as F

df.groupBy('name').agg(
        # 取平均后保留2位小数
        # alias 对Columns改名
        F.round(F.avg('age'), 2).alias('avg_age'),
        F.max('age').alias('min_age'),
        F.count('id').alias('cnt_id')

    ).show()
'''
    +-----+-------+-------+------+
    | name|avg_age|min_age|cnt_id|
    +-----+-------+-------+------+
    |  Tom|   13.5|   15.0|     2|
    |Jerry|   13.0|   13.0|     1|
    | John|  15.68| 17.353|     2|
    +-----+-------+-------+------+
'''

3.2.5.2 所有/部分 特征统一聚合

• pandas 可以直接.mean() 或其他 pandas dataframe 分组聚合

cols = diff.columns
# .agg(*[F.mean(col) for col in cols])
diff_mean = diff.groupBy('pt').agg(*[F.round(F.mean(col), 5).alias(col) for col in cols[:2]])

diff_NoneRatio = diff.groupBy('pt').agg(*[F.round(F.mean(diff[col].isNull().cast('double')), 5).alias(col) for col in cols[:2]])

3.2.6 列操作

3.2.6.1 创建列 df.withcolumn()

# 创建新列
from pyspark.sql.functions import lit

df1 = df.withColumn("nnumberOne", lit(2))
df1.show()
'''
    +----+-----+------+----------+
    |  id| name|   age|nnumberOne|
    +----+-----+------+----------+
    |1001|  Tom|  12.0|         2|
    |1002|Jerry|  13.0|         2|
    |1003| John|  14.0|         2|
    |1004|  Tom|  15.0|         2|
    |1003| John|17.353|         2|
    +----+-----+------+----------+
'''

from pyspark.sql import functions as F

'''
withColumn
对已存在的列操作
如果新列名和原有列名相同则覆盖原列，否则在表后追加新列
'''
df.withColumn('class & No.', F.split(df['id'], '00')).show()
'''
    +----+-----+------+-----------+
    |  id| name|   age|class & No.|
    +----+-----+------+-----------+
    |1001|  Tom|  12.0|     [1, 1]|
    |1002|Jerry|  13.0|     [1, 2]|
    |1003| John|  14.0|     [1, 3]|
    |1004|  Tom|  15.0|     [1, 4]|
    |1003| John|17.353|     [1, 3]|
    +----+-----+------+-----------+
'''

3.2.6.2 删除列 df.drop()

# 删除列
df1.drop('numberOne').show()
'''
    +----+-----+------+----------+
    |  id| name|   age|nnumberOne|
    +----+-----+------+----------+
    |1001|  Tom|  12.0|         2|
    |1002|Jerry|  13.0|         2|
    |1003| John|  14.0|         2|
    |1004|  Tom|  15.0|         2|
    |1003| John|17.353|         2|
    +----+-----+------+----------+
'''

3.2.6.3 改列名 df.withColumnRenamed()

# 改名
df.withColumnRenamed('id', 'No.').show()
'''
    +----+-----+------+
    | No.| name|   age|
    +----+-----+------+
    |1001|  Tom|  12.0|
    |1002|Jerry|  13.0|
    |1003| John|  14.0|
    |1004|  Tom|  15.0|
    |1003| John|17.353|
    +----+-----+------+
'''

3.2.6.4 改列数据类型 df.withColumn(‘new_name’, col(‘name’).cast(‘type’))

from pyspark.sql.functions import col

# 转换列的类型
df.withColumn('age_int', col('age').cast('int')).show()
'''
    +----+-----+------+-------+
    |  id| name|   age|age_int|
    +----+-----+------+-------+
    |1001|  Tom|  12.0|     12|
    |1002|Jerry|  13.0|     13|
    |1003| John|  14.0|     14|
    |1004|  Tom|  15.0|     15|
    |1003| John|17.353|     17|
    +----+-----+------+-------+
'''

3.2.6.5 拼接列 concat、concat_ws

• 拼接后的新列尾string类型
• concat_ws 指定拼接符

from spark.sql.functions import concat, concat_ws


df_concat = df.withColumn("col_concat", \
						  concat(df['col0'], df['col1'], df['col2']))
						  
df_concat = df.withColumn("col_concat", \
					      concat_ws('_', df['col0'], df['col1'], df['col2']))

3.2.6.6 批量重命名列 df.toDF(*list)

另：rdd.toDF(list)

'''
整张表的字段重命名
'''

cols = ['new_name1', 'new_name2', 'name3']
df.toDF(*cols)

3.2.6.7 添加索引列

from pyspark.sql import Window
from pyspark.sql.functions import row_number, monotonically_increasing_id


window_spec = Window.orderBy(monotonically_increasing_id())
df = df.withColumn("index", row_number().over(window_spec) - 1)
'''
+------------------+--------------------+-----+
|            cfrnid|                0830|index|
+------------------+--------------------+-----+
|360287970218473313|[[0, 0, 0, 0, 0, ...|    0|
|360287970218338563|[[6, 47398, 0, 29...|    1|
|360287970207922506|[[11932, 1048, 0,...|    2|
|360287970208320398|[[171, 0, 0, 0, 0...|    3|
|360287970218289139|[[208, 1520, 0, 0...|    4|
|360287970218491829|[[0, 0, 0, 0, 0, ...|    5|
|360287970218325554|[[180354, 0, 0, 0...|    6|
'''

3.2.7 去重 df.dropDuplicates([‘name’]) / df.distinct()

'''
去重
无参数，只保留重复行的第一行；
指定列，只保留重复列值第一次出现所在的行
'''
df.dropDuplicates(['name']).show()

'''
选定列，去重
'''
df[['col_name']].distinct().show()

3.2.8 缺失值处理 df.dropna/fillna()

'''
去缺失值
无参数，有缺失值就删除对应行；
有参数，
'''
df.dropna().show()

df.fillna('loss') 填充缺失值为'loss'
df.fillna('loss', subset=['name']) 指定列填充缺失值
df.fillna({'id': 0, 'name': 'loss', 'age': -1}) 同时指定多个列的缺失值填充规则

3.2.9 表 join

data = [(1001, "Tom", 12.0)
       ,(1002, "Jerry", 13.0)
       ,(1003, "John", 14.0)
       ,(1004, 'Tom', 15.0)
       ,(1003, "John", 17.353)]

rdd = sc.parallelize(data)
rdd = rdd.map(lambda x: Row(x[0], x[1], x[2]))

df_l = spark.createDataFrame(rdd, schema=['id', 'name', 'age'])
df_l.show()
'''
    +----+-----+------+
    |  id| name|   age|
    +----+-----+------+
    |1001|  Tom|  12.0|
    |1002|Jerry|  13.0|
    |1003| John|  14.0|
    |1004|  Tom|  15.0|
    |1003| John|17.353|
    +----+-----+------+
'''

df_r = df_l.where(df_l['id'] > 1002).\
            withColumn('class', F.split(df_l['id'], '00')[0])

df_r.show()
'''
    +----+----+------+-----+
    |  id|name|   age|class|
    +----+----+------+-----+
    |1003|John|  14.0|    1|
    |1004| Tom|  15.0|    1|
    |1003|John|17.353|    1|
    +----+----+------+-----+
'''

df_l.join(df_r[['id', 'class']], how='inner', on='id').show()
'''
    +----+----+------+-----+
    |  id|name|   age|class|
    +----+----+------+-----+
    |1003|John|  14.0|    1|
    |1003|John|  14.0|    1|
    |1003|John|17.353|    1|
    |1003|John|17.353|    1|
    |1004| Tom|  15.0|    1|
    +----+----+------+-----+
'''

3.2.10 list/dict多元素展开 F.explode()

3.2.10.1 将一列的list中的元素展开成多行

rdd = sc.parallelize([(1001, 'a b c'), (1002, 'd e f')])
rdd = rdd.map(lambda x: (x[0], x[1].split()))
df = rdd.toDF(schema=['id', 'line'])
df.show()
'''
+----+---------+
|  id|     line|
+----+---------+
|1001|[a, b, c]|
|1002|[d, e, f]|
+----+---------+
'''

from pyspark.sql import functions as F

df.withColumn('explode_line', F.explode(df['line'])).show()
'''
+----+---------+------------+
|  id|     line|explode_line|
+----+---------+------------+
|1001|[a, b, c]|           a|
|1001|[a, b, c]|           b|
|1001|[a, b, c]|           c|
|1002|[d, e, f]|           d|
|1002|[d, e, f]|           e|
|1002|[d, e, f]|           f|
+----+---------+------------+
'''

3.2.10.2 将dict展开 只能展开为key、value两列

def udf():
    d = {'a': '1', 'b': '2'}
    return d

udf1 = spark.udf.register('udf1', udf, MapType(StringType(), StringType()))

df = df.withColumn('col', udf1())
'''
+------------------+---+---+----------------+
|            cfrnid|No1|No2|             col|
+------------------+---+---+----------------+
|360287970194088911|  1|  2|[a -> 1, b -> 2]|
|360287970192611432|  1|  2|[a -> 1, b -> 2]|
|360287970195509570|  1|  2|[a -> 1, b -> 2]|
|360287970195244387|  1|  2|[a -> 1, b -> 2]|
|360287970193184476|  1|  2|[a -> 1, b -> 2]|
+------------------+---+---+----------------+
'''

df.select(F.explode('col')).show()
'''
+---+-----+
|key|value|
+---+-----+
|  a|    1|
|  b|    2|
|  a|    1|
|  b|    2|
|  a|    1|
|  b|    2|
|  a|    1|
|  b|    2|
|  a|    1|
|  b|    2|
+---+-----+
'''

3.2.11 将一列的list中的元素展开成多列

'''
原字段 id, cr_stagesum(list)
现字段 id, cr_stagesum[0], cr_stagesum[1], cr_stagesum[2]

只分离出原list字段的索引为0，1，2的元素，作为新的3列
'''
col_nums = [0, 1, 2]
cols = ['id']
for i in col_nums:
    cols.append(df.cr_stagesum[i])
df = df.select(*cols)

3.2.12 查看个数/去重后个数 df.describe() / df.select(‘name’).distinct().count()

df.describe().show()
'''
+-------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+----------+
|summary|              cfrnid|             traceid|       report_times|              respon|              lender|              crtype|              ricomm|        pt|
+-------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+----------+
|  count|               38074|               38074|              38074|               35704|               35704|               35704|               35704|     38074|
|   mean|3.602879702074297E17|                null|               null|                null|                null|                null|                null|      null|
| stddev|   9750597.034539178|                null|               null|                null|                null|                null|                null|      null|
|    min|  360287970189650107|0a0f120e64ee09722...|2023-08-29 00:00:09|[[0, 0, 0, 0, 0, ...|[[0, 0, 0, 0, 0, ...|[[0, 0, 0, 0, 0, ...|[[0, 0, 0, 0, 0, ...|2023-08-30|
|    max|  360287970218654431|s570868e64ed9a4b1...|2023-08-29 23:59:55|[[99998, 0, 0, 0,...|[[9997, 0, 0, 646...|[[9993, 0, 0, 0, ...|[[0, 0, 0, 0, 0, ...|2023-08-30|
+-------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+----------+
'''

# 计算个数
df.select('respon').count()
# 计算不重复的个数
df.select('respon').distinct().count()

3.2.13 取前n个数据 df.take()

• df.take(50) 返回类型是list

3.2.14 按索引读取行数据，读取表格指定位置数据 df.take()[]

# 获取指定行的数据
'''
获取第3行, 索引为2的一行数据 必须后面加[idx]；
返回的是 Row类型
'''
df.take(3)[2] 
# 获取Row对象中具体哪个数据
df.take(3)[2]['cfrnid']

3.2.15 条件语句 F.when

import pyspark.sql.functions as F


df = df.withColumn('sample_set', 
                   F.when(df.index > t, F.lit('OOT'))\
                   .when(df.index <= t, F.lit('TRAIN')))

3.2.16 日期操作 F.date_sub

日期往前推几天

import pyspark.sql.functions as F

# pt - 1 == credit_date
d.where(d.credit_date == F.date_sub(d.pt, 1)).show()

3.2.17 将一列的值提取为list .rdd.flatMap()

接 3.2.10.2

keys = df.select('key').distinct()
'''
+---+
|key|
+---+
|  a|
|  b|
+---+
'''

keys.rdd.collect()
'''
[Row(key='a'), Row(key='b')]
'''

keys.rdd.flatMap(lambda x: x).collect()
'''
['a', 'b']
'''

3.2.18 按分区随机采样 df.sample(fraction=, seed=)

df = df.sample(fraction=0.02, seed=666)

4 UDF 用户定义(普通)函数

• 作用与单行数值
• UDF、UDAF、UDTF

from pyspark.sql import SparkSession,
from pyspark.sql import functions as F
from pyspark.sql.types import *

spark = SparkSession.builder.\
    appName('test').\
    master('local[*]').\
    config('spark.sql.shuffle.partitions', 2).\
    getOrCreate()
sc = spark.sparkContext

rdd = sc.parallelize([1, 2, 3]).\
    map(lambda x: [x])
df = rdd.toDF(['num'])
df.show()
'''
    +---+
    |num|
    +---+
    |  1|
    |  2|
    |  3|
    +---+
'''

4.1 sparksession.udf.register()

• 注册的udf可以用于DSL和SQL

# sparksession.udf.register()
def num_ride_10(num):
    return num * 10
'''
param1: 注册的udf名称，仅可用于SQL风格
param2: 数据处理方法
param3: UDF的返回类型
return: udf对象，仅可以用于DSL风格
'''
udf_dsl = spark.udf.register('udf_sql', num_ride_10, IntegerType())

df.createOrReplaceTempView("num")
spark.sql('select udf_sql(*) from num').show()
# df.selectExpr('udf_sql(num)').show()
'''
    +------------+
    |udf_sql(num)|
    +------------+
    |          10|
    |          20|
    |          30|
    +------------+
'''

# DSL 风格的udf只接受Column对象
# 注意：列名是udf_sql, 正常使用时返回对象和注册udf名一致
df.select(udf_dsl(df['num'])).show()
'''
    +------------+
    |udf_sql(num)|
    +------------+
    |          10|
    |          20|
    |          30|
    +------------+
'''

4.2 pyspark.sql.functions.udf() & 数据类型

• 仅用于DSL风格

4.2.1 IntegerType()

def num_ride_10(num):
    return num * 10

udf = F.udf(num_ride_10, IntegerType())
df.select(udf(df['num'])).show()
'''
    +----------------+
    |num_ride_10(num)|
    +----------------+
    |              10|
    |              20|
    |              30|
    +----------------+
'''

4.2.2 ArrayType(StringType())

rdd = sc.parallelize([['hello nice good a'], ['hello b'], ['nice c']])
df = rdd.toDF(['line'])
df.show()
'''
    +-----------------+
    |             line|
    +-----------------+
    |hello nice good a|
    |          hello b|
    |           nice c|
    +-----------------+
'''

# 返回ArrayType
def split_line(line):
    return line.split(' ')

# 规定返回数组类型中装string类型
udf_split = spark.udf.register('udf_split', split_line, ArrayType(StringType()))
# False 展示一行全部数据, 否则过长表示为'...'
df.select(udf_split(df['line'])).show(truncate=False)
'''
    +----------------------+
    |udf_split(line)       |
    +----------------------+
    |[hello, nice, good, a]|
    |[hello, b]            |
    |[nice, c]             |
    +----------------------+
'''

4.2.3 StructType()

import string
'''
返回字典类型
将字典想像成一张表格(json),用StructType()接收
'''

rdd = sc.parallelize([[1], [2], [3]])
df = rdd.toDF(['num'])

def get_letter(num):
    return {'num': num, 'letter': string.ascii_letters[num]}

udf= spark.udf.register('udf', 
                        get_letter, 
                        StructType().add('num', IntegerType(), True)\
                                    .add('letter', StringType(), True)
                       )

df.select(udf(df['num'])).show()
'''
    +--------+
    |udf(num)|
    +--------+
    |  [1, b]|
    |  [2, c]|
    |  [3, d]|
    +--------+
'''

5 UDAF 用户定义聚合函数

• User- Defined Aggregation Funcation
• 作用于多行数据， 相当于SQL中的count()、avg()

# 后期用rdd.mapPartitions()完成聚合, 必须用单分区
single_partition_rdd = df.rdd.repartition(1) 
single_partition_rdd.collect()
'''
    [Row(num=1), Row(num=2), Row(num=3)]
'''

def process(iter):
    sum = 0
    for row in iter:
        sum += row['num']
    # ！！！必须嵌套list
    return [sum]

single_partition_rdd.mapPartitions(process).collect()
'''
    [6]
'''

6 开窗函数

rdd = sc.parallelize([
    (1, 'a', 12),
    (2, 'b', 13),
    (3, 'c', 14),
    (1, 'd', 12),
    (2, 'e', 16)
])
schema = StructType([
    StructField('class', IntegerType(), True),
    StructField('name', StringType(), True),
    StructField('age', LongType(), True)
])

df = rdd.toDF(schema=schema)
df.createOrReplaceTempView('stu')

df.show()
'''
    +-----+----+---+
    |class|name|age|
    +-----+----+---+
    |    1|   a| 12|
    |    2|   b| 13|
    |    3|   c| 14|
    |    1|   d| 12|
    |    2|   e| 16|
    +-----+----+---+
'''

6.1 聚合窗口函数

'''
聚合窗口函数
聚合函数(列) over()

avg() 聚合函数 将多行变为一行
over() 窗口函数 将一行变为多行
'''

spark.sql("""
select *, avg(age) over() as avg_age from stu

""").show()
'''
    +-----+----+---+-------+
    |class|name|age|avg_age|
    +-----+----+---+-------+
    |    3|   c| 14|   13.4|
    |    1|   d| 12|   13.4|
    |    2|   e| 16|   13.4|
    |    1|   a| 12|   13.4|
    |    2|   b| 13|   13.4|
    +-----+----+---+-------+
'''

6.2 排序窗口函数

'''
排序窗口函数
row_number() over(order by age desc) 按照age全局进行降序排序，追加一列名次
dense_rank() over(partition by class order by age desc) 按class分区，区内按age排序，追加一列名次
rank() over(order by age) 按照age全局进行升序排序，追加一列名次
ntile(3) over(order by age desc) 按age均分成3份，每份中的数据都是一个排名

row_number 出现相同会依次排序，
rank 出现相同排序一样

'''

spark.sql("""
select *, 
       row_number() over(order by age desc) as row_number_rank,
       dense_rank() over(partition by class order by age desc) as dense_rank,
       rank() over(order by age) as rank
       
from   stu
""").show()
'''
    +-----+----+---+---------------+----------+----+
    |class|name|age|row_number_rank|dense_rank|rank|
    +-----+----+---+---------------+----------+----+
    |    1|   a| 12|              4|         1|   1|
    |    1|   d| 12|              5|         1|   1|
    |    2|   b| 13|              3|         2|   3|
    |    3|   c| 14|              2|         1|   4|
    |    2|   e| 16|              1|         1|   5|
    +-----+----+---+---------------+----------+----+
'''

spark.sql("""
select *, 
       ntile(3) over(order by age desc) as ntile_3
       
from   stu
""").show()
'''
    +-----+----+---+-------+
    |class|name|age|ntile_3|
    +-----+----+---+-------+
    |    2|   e| 16|      1|
    |    3|   c| 14|      1|
    |    2|   b| 13|      2|
    |    1|   a| 12|      2|
    |    1|   d| 12|      3|
    +-----+----+---+-------+
'''

7 DataFrame 缓存

【Spark】PySpark RDD - RDD 缓存

df.cache() # 优先缓存到内存，不够放硬盘

8 保存DataFrame 到hive

if spark.catalog._jcatalog.tableExists(table):
	# insertInto 不用partitionBy
	pre_fea_importance_df.write.format("hive").mode("overwrite").insertInto(table)
else:
	pre_fea_importance_df.write.format("hive").mode("overwrite").partitionBy('l_r').saveAsTable(table)