微服务-流量染色

文章目录

• • pyspark.sql.group.GroupedData 类型内置方法
  • agg 聚合
  • apply
  • applylnPandas
  • avg alias mean
  • count
  • max
  • min
  • sum
  • pivot

pyspark.sql.group.GroupedData 类型内置方法

agg 聚合

df = spark.createDataFrame(
     [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])
df.show()
+---+-----+
|age| name|
+---+-----+
|  2|Alice|
|  3|Alice|
|  5|  Bob|
| 10|  Bob|
+---+-----+
df.groupBy(df.name)
<pyspark.sql.group.GroupedData object at 0x7f74be2e4240>
df.groupBy(df.name).agg({'*':'count'}).show()
+-----+--------+
| name|count(1)|
+-----+--------+
|Alice|       2|
|  Bob|       2|
+-----+--------+

df.groupBy(df.name).agg({'age':'min'}).sort("name").show()
+-----+--------+
| name|min(age)|
+-----+--------+
|Alice|       2|
|  Bob|       5|
+-----+--------+

from pyspark.sql.functions import lit,col,min
df.groupBy(df.name).agg(min(df.age)).sort("name").show()
+-----+--------+
| name|min(age)|
+-----+--------+
|Alice|       2|
|  Bob|       5|
+-----+--------+


from pyspark.sql.pandas.functions import pandas_udf, PandasUDFType
@pandas_udf('int',PandasUDFType.GROUPED_AGG)
def min_udf(v):
    print(type(v))
    print(v)
    return v.min()
df.groupBy(df.name).agg(min_udf(df.age)).sort("name").show()  

<class 'pandas.core.series.Series'> 
0    2
1    3
                                                                                  +-----+------------+
| name|min_udf(age)|
+-----+------------+
|Alice|           2|
|  Bob|           5|
+-----+------------+

apply

参数：pandas_udf装饰的函数

pyspark.sql.functions.pandas_udf()

from pyspark.sql.pandas.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame(
    [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
    ("id", "v"))
    
df.show()
+---+----+
| id|   v|
+---+----+
|  1| 1.0|
|  1| 2.0|
|  2| 3.0|
|  2| 5.0|
|  2|10.0|
+---+----+

@pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)  
def normalize(pdf):
    print(pdf)
    v = pdf.v
    d1 = pdf.assign(v=(v - v.mean()) / v.std())
    print(d1,type(d1))
    return d1
df.groupby("id").apply(normalize).show()
   id    v
0   1  1.0
1   1  2.0
   id         v
0   1 -0.707107
1   1  0.707107 <class 'pandas.core.frame.DataFrame'>
   id     v
0   2   3.0
1   2   5.0
2   2  10.0
   id        v
0   2 -0.83205
1   2 -0.27735
2   2  1.10940 <class 'pandas.core.frame.DataFrame'>
+---+-------------------+
| id|                  v|
+---+-------------------+
|  1|-0.7071067811865475|
|  1| 0.7071067811865475|
|  2|-0.8320502943378437|
|  2|-0.2773500981126146|
|  2| 1.1094003924504583|
+---+-------------------+

applylnPandas

参数：普通函数，dataframe的scheam结构

import pandas as pd  
df = spark.createDataFrame(
    [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
    ("id", "v"))  
    
def normalize(pdf):
    v = pdf.v
    return pdf.assign(v=(v - v.mean()) / v.std())


df.groupby("id").applyInPandas(
    normalize, schema="id long, v double").show()  
+---+-------------------+
| id|                  v|
+---+-------------------+
|  1|-0.7071067811865475|
|  1| 0.7071067811865475|
|  2|-0.8320502943378437|
|  2|-0.2773500981126146|
|  2| 1.1094003924504583|
+---+-------------------+

def normalize(pdf):
    v = pdf.v
    return pd.DataFrame()
df.groupby("id").applyInPandas(
    normalize, schema="id long, v double").show()  
                                                                                +---+---+
| id|  v|
+---+---+
+---+---+

avg alias mean

df = spark.createDataFrame([
...     (2, "Alice", 80), (3, "Alice", 100),
...     (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
>>> df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
|  2|Alice|    80|
|  3|Alice|   100|
|  5|  Bob|   120|
| 10|  Bob|   140|
+---+-----+------+

# 直接使用avg方法
df.groupBy("name").avg('age').sort("name").show()
+-----+--------+
| name|avg(age)|
+-----+--------+
|Alice|     2.5|
|  Bob|     7.5|
+-----+--------+

# 使用agg 配合使用
df.groupBy("name").agg({"age":'avg'}).sort("name").show()
+-----+--------+
| name|avg(age)|
+-----+--------+
|Alice|     2.5|
|  Bob|     7.5|
+-----+--------+

count

df = spark.createDataFrame(
     [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])
df.show()
+---+-----+
|age| name|
+---+-----+
|  2|Alice|
|  3|Alice|
|  5|  Bob|
| 10|  Bob|
+---+-----+

df.groupBy(df.name).count().sort("name").show()
+-----+-----+
| name|count|
+-----+-----+
|Alice|    2|
|  Bob|    2|
+-----+-----+

df.groupBy("name").agg({"name":'count'}).sort("name").show()
+-----+-----------+
| name|count(name)|
+-----+-----------+
|Alice|          2|
|  Bob|          2|
+-----+-----------+

max

df = spark.createDataFrame([
    (2, "Alice", 80), (3, "Alice", 100),
    (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
|  2|Alice|    80|
|  3|Alice|   100|
|  5|  Bob|   120|
| 10|  Bob|   140|
+---+-----+------+

df.groupBy("name").max("age").sort("name").show()
+-----+--------+
| name|max(age)|
+-----+--------+
|Alice|       3|
|  Bob|      10|
+-----+--------+


df.groupBy("name").agg({"age":'max','height':"min"}).sort("name").show()
+-----+--------+-----------+
| name|max(age)|min(height)|
+-----+--------+-----------+
|Alice|       3|         80|
|  Bob|      10|        120|
+-----+--------+-----------+

min

df = spark.createDataFrame([
    (2, "Alice", 80), (3, "Alice", 100),
    (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
|  2|Alice|    80|
|  3|Alice|   100|
|  5|  Bob|   120|
| 10|  Bob|   140|
+---+-----+------+

df.groupBy("name").min("age").sort("name").show()
+-----+--------+
| name|min(age)|
+-----+--------+
|Alice|       2|
|  Bob|       5|
+-----+--------+


df.groupBy("name").agg({"age":'max','height':"min"}).sort("name").show()
+-----+--------+-----------+
| name|max(age)|min(height)|
+-----+--------+-----------+
|Alice|       3|         80|
|  Bob|      10|        120|
+-----+--------+-----------+

sum

df = spark.createDataFrame([
    (2, "Alice", 80), (3, "Alice", 100),
    (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
|  2|Alice|    80|
|  3|Alice|   100|
|  5|  Bob|   120|
| 10|  Bob|   140|
+---+-----+------+

df.groupBy("name").sum("age").sort("name").show()
+-----+--------+
| name|sum(age)|
+-----+--------+
|Alice|       5|
|  Bob|      15|
+-----+--------+

pivot

分组后，选中某列，把列中的指定值进行统计

from pyspark.sql import Row
... df1 = spark.createDataFrame([
...     Row(course="dotNET", year=2012, earnings=10000),
...     Row(course="Java", year=2012, earnings=20000),
...     Row(course="dotNET", year=2012, earnings=5000),
...     Row(course="dotNET", year=2013, earnings=48000),
...     Row(course="Java", year=2013, earnings=30000),
... ])
... df1.show()
+------+----+--------+
|course|year|earnings|
+------+----+--------+
|dotNET|2012|   10000|
|  Java|2012|   20000|
|dotNET|2012|    5000|
|dotNET|2013|   48000|
|  Java|2013|   30000|
+------+----+--------+

df1.groupBy("year").pivot("course", ["dotNET", "Java"]).max("earnings").show()
+----+------+-----+
|year|dotNET| Java|
+----+------+-----+
|2012| 10000|20000|
|2013| 48000|30000|
+----+------+-----+


df1.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").show()
+----+------+-----+
|year|dotNET| Java|
+----+------+-----+
|2012| 15000|20000|
|2013| 48000|30000|
+----+------+-----+