Python 连接两个 PySpark 数据帧
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Concatenate two PySpark dataframes
提问by Ivan
I'm trying to concatenate two PySpark dataframes with some columns that are only on each of them:
我正在尝试将两个 PySpark 数据帧与一些仅位于其中的列连接起来：
from pyspark.sql.functions import randn, rand
df_1 = sqlContext.range(0, 10)
+--+
|id|
+--+
| 0|
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
| 7|
| 8|
| 9|
+--+
df_2 = sqlContext.range(11, 20)
+--+
|id|
+--+
| 10|
| 11|
| 12|
| 13|
| 14|
| 15|
| 16|
| 17|
| 18|
| 19|
+--+
df_1 = df_1.select("id", rand(seed=10).alias("uniform"), randn(seed=27).alias("normal"))
df_2 = df_2.select("id", rand(seed=10).alias("uniform"), randn(seed=27).alias("normal_2"))
and now I want to generate a third dataframe. I would like something like pandas concat:
现在我想生成第三个数据框。我想要像熊猫这样的东西concat：
df_1.show()
+---+--------------------+--------------------+
| id| uniform| normal|
+---+--------------------+--------------------+
| 0| 0.8122802274304282| 1.2423430583597714|
| 1| 0.8642043127063618| 0.3900018344856156|
| 2| 0.8292577771850476| 1.8077401259195247|
| 3| 0.198558705368724| -0.4270585782850261|
| 4|0.012661361966674889| 0.702634599720141|
| 5| 0.8535692890157796|-0.42355804115129153|
| 6| 0.3723296190171911| 1.3789648582622995|
| 7| 0.9529794127670571| 0.16238718777444605|
| 8| 0.9746632635918108| 0.02448061333761742|
| 9| 0.513622008243935| 0.7626741803250845|
+---+--------------------+--------------------+
df_2.show()
+---+--------------------+--------------------+
| id| uniform| normal_2|
+---+--------------------+--------------------+
| 11| 0.3221262660507942| 1.0269298899109824|
| 12| 0.4030672316912547| 1.285648175568798|
| 13| 0.9690555459609131|-0.22986601831364423|
| 14|0.011913836266515876| -0.678915153834693|
| 15| 0.9359607054250594|-0.16557488664743034|
| 16| 0.45680471157575453| -0.3885563551710555|
| 17| 0.6411908952297819| 0.9161177183227823|
| 18| 0.5669232696934479| 0.7270125277020573|
| 19| 0.513622008243935| 0.7626741803250845|
+---+--------------------+--------------------+
#do some concatenation here, how?
df_concat.show()
| id| uniform| normal| normal_2 |
+---+--------------------+--------------------+------------+
| 0| 0.8122802274304282| 1.2423430583597714| None |
| 1| 0.8642043127063618| 0.3900018344856156| None |
| 2| 0.8292577771850476| 1.8077401259195247| None |
| 3| 0.198558705368724| -0.4270585782850261| None |
| 4|0.012661361966674889| 0.702634599720141| None |
| 5| 0.8535692890157796|-0.42355804115129153| None |
| 6| 0.3723296190171911| 1.3789648582622995| None |
| 7| 0.9529794127670571| 0.16238718777444605| None |
| 8| 0.9746632635918108| 0.02448061333761742| None |
| 9| 0.513622008243935| 0.7626741803250845| None |
| 11| 0.3221262660507942| None | 0.123 |
| 12| 0.4030672316912547| None |0.12323 |
| 13| 0.9690555459609131| None |0.123 |
| 14|0.011913836266515876| None |0.18923 |
| 15| 0.9359607054250594| None |0.99123 |
| 16| 0.45680471157575453| None |0.123 |
| 17| 0.6411908952297819| None |1.123 |
| 18| 0.5669232696934479| None |0.10023 |
| 19| 0.513622008243935| None |0.916332123 |
+---+--------------------+--------------------+------------+
Is that possible?
那可能吗？
回答by Daniel de Paula
Maybe you can try creating the unexisting columns and calling union(unionAllfor Spark 1.6 or lower):
也许您可以尝试创建不存在的列并调用union（unionAll对于 Spark 1.6 或更低版本）：
cols = ['id', 'uniform', 'normal', 'normal_2']
df_1_new = df_1.withColumn("normal_2", lit(None)).select(cols)
df_2_new = df_2.withColumn("normal", lit(None)).select(cols)
result = df_1_new.union(df_2_new)
回答by David
df_concat = df_1.union(df_2)
The dataframes may need to have identical columns, in which case you can use withColumn()to create normal_1and normal_2
数据框可能需要具有相同的列，在这种情况下，您可以使用withColumn()创建normal_1和normal_2
回答by Shadowtrooper
You can use unionByName to make this:
您可以使用 unionByName 来实现：
df = df_1.unionByName(df_2)
unionByName is available since Spark 2.3.0.
unionByName 从 Spark 2.3.0 开始可用。
回答by user020314
Here is one way to do it, in case it is still useful: I ran this in pyspark shell, Python version 2.7.12 and my Spark install was version 2.0.1.
这是一种方法，以防它仍然有用：我在 pyspark shell 中运行它，Python 版本为 2.7.12，我的 Spark 安装版本为 2.0.1。
PS: I guess you meant to use different seeds for the df_1 df_2 and the code below reflects that.
PS：我猜你打算为 df_1 df_2 使用不同的种子，下面的代码反映了这一点。
from pyspark.sql.types import FloatType
from pyspark.sql.functions import randn, rand
import pyspark.sql.functions as F
df_1 = sqlContext.range(0, 10)
df_2 = sqlContext.range(11, 20)
df_1 = df_1.select("id", rand(seed=10).alias("uniform"), randn(seed=27).alias("normal"))
df_2 = df_2.select("id", rand(seed=11).alias("uniform"), randn(seed=28).alias("normal_2"))
def get_uniform(df1_uniform, df2_uniform):
if df1_uniform:
return df1_uniform
if df2_uniform:
return df2_uniform
u_get_uniform = F.udf(get_uniform, FloatType())
df_3 = df_1.join(df_2, on = "id", how = 'outer').select("id", u_get_uniform(df_1["uniform"], df_2["uniform"]).alias("uniform"), "normal", "normal_2").orderBy(F.col("id"))
Here are the outputs I get:
这是我得到的输出：
df_1.show()
+---+-------------------+--------------------+
| id| uniform| normal|
+---+-------------------+--------------------+
| 0|0.41371264720975787| 0.5888539012978773|
| 1| 0.7311719281896606| 0.8645537008427937|
| 2| 0.1982919638208397| 0.06157382353970104|
| 3|0.12714181165849525| 0.3623040918178586|
| 4| 0.7604318153406678|-0.49575204523675975|
| 5|0.12030715258495939| 1.0854146699817222|
| 6|0.12131363910425985| -0.5284523629183004|
| 7|0.44292918521277047| -0.4798519469521663|
| 8| 0.8898784253886249| -0.8820294772950535|
| 9|0.03650707717266999| -2.1591956435415334|
+---+-------------------+--------------------+
df_2.show()
+---+-------------------+--------------------+
| id| uniform| normal_2|
+---+-------------------+--------------------+
| 11| 0.1982919638208397| 0.06157382353970104|
| 12|0.12714181165849525| 0.3623040918178586|
| 13|0.12030715258495939| 1.0854146699817222|
| 14|0.12131363910425985| -0.5284523629183004|
| 15|0.44292918521277047| -0.4798519469521663|
| 16| 0.8898784253886249| -0.8820294772950535|
| 17| 0.2731073068483362|-0.15116027592854422|
| 18| 0.7784518091224375| -0.3785563841011868|
| 19|0.43776394586845413| 0.47700719174464357|
+---+-------------------+--------------------+
df_3.show()
+---+-----------+--------------------+--------------------+
| id| uniform| normal| normal_2|
+---+-----------+--------------------+--------------------+
| 0| 0.41371265| 0.5888539012978773| null|
| 1| 0.7311719| 0.8645537008427937| null|
| 2| 0.19829196| 0.06157382353970104| null|
| 3| 0.12714182| 0.3623040918178586| null|
| 4| 0.7604318|-0.49575204523675975| null|
| 5|0.120307155| 1.0854146699817222| null|
| 6| 0.12131364| -0.5284523629183004| null|
| 7| 0.44292918| -0.4798519469521663| null|
| 8| 0.88987845| -0.8820294772950535| null|
| 9|0.036507078| -2.1591956435415334| null|
| 11| 0.19829196| null| 0.06157382353970104|
| 12| 0.12714182| null| 0.3623040918178586|
| 13|0.120307155| null| 1.0854146699817222|
| 14| 0.12131364| null| -0.5284523629183004|
| 15| 0.44292918| null| -0.4798519469521663|
| 16| 0.88987845| null| -0.8820294772950535|
| 17| 0.27310732| null|-0.15116027592854422|
| 18| 0.7784518| null| -0.3785563841011868|
| 19| 0.43776396| null| 0.47700719174464357|
+---+-----------+--------------------+--------------------+
回答by Yuchen Zhong
To make it more generic of keeping both columns in df1and df2:
为了更通用地将两列都保留在df1和 中df2：
import pyspark.sql.functions as F
# Keep all columns in either df1 or df2
def outter_union(df1, df2):
# Add missing columns to df1
left_df = df1
for column in set(df2.columns) - set(df1.columns):
left_df = left_df.withColumn(column, F.lit(None))
# Add missing columns to df2
right_df = df2
for column in set(df1.columns) - set(df2.columns):
right_df = right_df.withColumn(column, F.lit(None))
# Make sure columns are ordered the same
return left_df.union(right_df.select(left_df.columns))
回答by furianpandit
Above answers are very elegant. I have written this function long back where i was also struggling to concatenate two dataframe with distinct columns.
上面的答案非常优雅。我很久以前就写过这个函数，我也在努力将两个数据框与不同的列连接起来。
Suppose you have dataframe sdf1 and sdf2
假设你有数据帧 sdf1 和 sdf2
from pyspark.sql import functions as F
from pyspark.sql.types import *
def unequal_union_sdf(sdf1, sdf2):
s_df1_schema = set((x.name, x.dataType) for x in sdf1.schema)
s_df2_schema = set((x.name, x.dataType) for x in sdf2.schema)
for i,j in s_df2_schema.difference(s_df1_schema):
sdf1 = sdf1.withColumn(i,F.lit(None).cast(j))
for i,j in s_df1_schema.difference(s_df2_schema):
sdf2 = sdf2.withColumn(i,F.lit(None).cast(j))
common_schema_colnames = sdf1.columns
sdk = \
sdf1.select(common_schema_colnames).union(sdf2.select(common_schema_colnames))
return sdk
sdf_concat = unequal_union_sdf(sdf1, sdf2)
回答by Bobby John
im a dwh turned pyspark developer. Below is what I would do:
我是 dwh 转为 pyspark 开发人员。下面是我会做的：
from pyspark.sql import SparkSession
df_1.createOrReplaceTempView("tab_1")
df_2.createOrReplaceTempView("tab_2")
df_concat=spark.sql("select tab_1.id,tab_1.uniform,tab_1.normal,tab_2.normal_2 from tab_1 tab_1 left join tab_2 tab_2 on tab_1.uniform=tab_2.uniform\
union\
select tab_2.id,tab_2.uniform,tab_1.normal,tab_2.normal_2 from tab_2 tab_2 left join tab_1 tab_1 on tab_1.uniform=tab_2.uniform")
df_concat.show()
--pls let me know if this worked for you or was your need.
--请让我知道这是否适合您或您需要。
回答by Hans
This should do it for you ...
这应该为你做...
from pyspark.sql.types import FloatType
from pyspark.sql.functions import randn, rand, lit, coalesce, col
import pyspark.sql.functions as F
df_1 = sqlContext.range(0, 6)
df_2 = sqlContext.range(3, 10)
df_1 = df_1.select("id", lit("old").alias("source"))
df_2 = df_2.select("id")
df_1.show()
df_2.show()
df_3 = df_1.alias("df_1").join(df_2.alias("df_2"), df_1.id == df_2.id, "outer")\
.select(\
[coalesce(df_1.id, df_2.id).alias("id")] +\
[col("df_1." + c) for c in df_1.columns if c != "id"])\
.sort("id")
df_3.show()
回答by Mangnier Lo?c
Maybe, you want to concatenate more of two Dataframes. I found a issue which use pandas Dataframe conversion.
也许，您想连接更多的两个数据帧。我发现了一个使用 Pandas Dataframe 转换的问题。
Suppose you have 3 spark Dataframe who want to concatenate.
假设您有 3 个想要连接的 spark Dataframe。
The code is the following:
代码如下：
list_dfs = []
list_dfs_ = []
df = spark.read.json('path_to_your_jsonfile.json',multiLine = True)
df2 = spark.read.json('path_to_your_jsonfile2.json',multiLine = True)
df3 = spark.read.json('path_to_your_jsonfile3.json',multiLine = True)
list_dfs.extend([df,df2,df3])
for df in list_dfs :
df = df.select([column for column in df.columns]).toPandas()
list_dfs_.append(df)
list_dfs.clear()
df_ = sqlContext.createDataFrame(pd.concat(list_dfs_))
