I have a Spark DataFrame, say df, to which I need to apply a GroupBy col1, aggregate by maximum value of col2 and pass the corresponding value of col3 (which has nothing to do with the groupBy or the aggregation). It is best to illustrate it with an example.
df.show()
+-----+-----+-----+
| col1| col2| col3|
+-----+-----+-----+
| 1| 500| 10 |
| 1| 600| 11 |
| 1| 700| 12 |
| 2| 600| 14 |
| 2| 800| 15 |
| 2| 650| 17 |
+-----+-----+-----+
I can easily perform the groupBy and the aggregation to obtain the maximum value of each group in col2, using
import pyspark.sql.functions as F
df1 = df.groupBy("col1").agg(
F.max("col2").alias('Max_col2')).show()
+-----+---------+
| col1| Max_col2|
+-----+---------+
| 1| 700|
| 2| 800|
+-----+---------+
However, what I am struggling with and what I would like to do is to, additionally, pass the corresponding value of col3, thus obtaining the following table:
+-----+---------+-----+
| col1| Max_col2| col3|
+-----+---------+-----+
| 1| 700| 12 |
| 2| 800| 15 |
+-----+---------+-----+
Does anyone know how this can be done?
Many thanks in advance,
Marioanzas
You can aggregate the maximum of a struct, and then expand the struct:
import pyspark.sql.functions as F
df2 = df.groupBy('col1').agg(
F.max(F.struct('col2', 'col3')).alias('col')
).select('col1', 'col.*')
df2.show()
+----+----+----+
|col1|col2|col3|
+----+----+----+
| 1| 700| 12|
| 2| 800| 15|
+----+----+----+
df1:
+---+------+
| id| code|
+---+------+
| 1|[A, F]|
| 2| [G]|
| 3| [A]|
+---+------+
df2:
+--------+----+
| col1|col2|
+--------+----+
| Apple| A|
| Google| G|
|Facebook| F|
+--------+----+
I want the df3 should be like this by using the df1, and df2 columns :
+---+------+-----------------+
| id| code| changed|
+---+------+-----------------+
| 1|[A, F]|[Apple, Facebook]|
| 2| [G]| [Google]|
| 3| [A]| [Apple]|
+---+------+-----------------+
I know this can be archived if the code column is NOT an ARRAY. I don't know how to iterate the code array for this purpose.
Try:
from pyspark.sql.functions import *
import pyspark.sql.functions as f
res=(df1
.select(f.col("id"), f.explode(f.col("code")).alias("code"))
.join(df2, f.col("code")==df2.col2)
.groupBy("id")
.agg(f.collect_list(f.col("code")).alias("code"), f.collect_list(f.col("col1")).alias("changed"))
)
I have a pyspark dataframe like this,
+----------+--------+
|id_ | p |
+----------+--------+
| 1 | A |
| 1 | B |
| 1 | B |
| 1 | A |
| 1 | A |
| 1 | B |
| 2 | C |
| 2 | C |
| 2 | C |
| 2 | A |
| 2 | A |
| 2 | C |
---------------------
I want to create another column for each group of id_. Column is made using pandas now with the code,
sample.groupby(by=['id_'], group_keys=False).apply(lambda grp : grp['p'].ne(grp['p'].shift()).cumsum())
How can I do this in pyspark dataframe.?
Currently I am doing this with a help of a pandas UDF, which runs very slow.
What are the alternatives.?
Expected column will be like this,
1
2
2
3
3
4
1
1
1
2
2
3
You can combination of udf and window functions to achieve your results:
# required imports
from pyspark.sql.window import Window
import pyspark.sql.functions as F
from pyspark.sql.types import IntegerType
# define a window, which we will use to calculate lag values
w = Window().partitionBy().orderBy(F.col('id_'))
# define user defined function (udf) to perform calculation on each row
def f(lag_val, current_val):
if lag_val != current_val:
return 1
return 0
# register udf so we can use with our dataframe
func_udf = F.udf(f, IntegerType())
# read csv file
df = spark.read.csv('/path/to/file.csv', header=True)
# create new column with lag on window we created earlier, apply udf on lagged
# and current value and then apply window function again to calculate cumsum
df.withColumn("new_column", func_udf(F.lag("p").over(w), df['p'])).withColumn('cumsum', F.sum('new_column').over(w.partitionBy(F.col('id_')).rowsBetween(Window.unboundedPreceding, 0))).show()
+---+---+----------+------+
|id_| p|new_column|cumsum|
+---+---+----------+------+
| 1| A| 1| 1|
| 1| B| 1| 2|
| 1| B| 0| 2|
| 1| A| 1| 3|
| 1| A| 0| 3|
| 1| B| 1| 4|
| 2| C| 1| 1|
| 2| C| 0| 1|
| 2| C| 0| 1|
| 2| A| 1| 2|
| 2| A| 0| 2|
| 2| C| 1| 3|
+---+---+----------+------+
# where:
# w.partitionBy : to partition by id_ column
# w.rowsBetween : to specify frame boundaries
# ref https://spark.apache.org/docs/2.2.1/api/java/org/apache/spark/sql/expressions/Window.html#rowsBetween-long-long-
I am trying to calculate the mean of a list (cost) within a PySpark Dataframe column, the values that are less than the mean get the value 1 and above the mean a 0.
This is the current dataframe:
+----------+--------------------+--------------------+
| id| collect_list(p_id)|collect_list(cost) |
+----------+--------------------+--------------------+
| 7|[10, 987, 872] |[12.0, 124.6, 197.0]|
| 6|[11, 858, 299] |[15.0, 167.16, 50.0]|
| 17| [2]| [65.4785]|
| 1|[34359738369, 343...|[16.023384, 104.9...|
| 3|[17179869185, 0, ...|[48.3255, 132.025...|
+----------+--------------------+--------------------+
This is the desired output:
+----------+--------------------+--------------------+-----------+
| id| p_id |cost | result |
+----------+--------------------+--------------------+-----------+
| 7|10 |12.0 | 1 |
| 7|987 |124.6 | 0 |
| 7|872 |197.0 | 0 |
| 6|11 |15.0 | 1 |
| 6|858 |167.16 | 0 |
| 6|299 |50.0 | 1 |
| 17|2 |65.4785 | 1 |
+----------+--------------------+--------------------+-----------+
from pyspark.sql.functions import col, mean
#sample data
df = sc.parallelize([(7,[10, 987, 872],[12.0, 124.6, 197.0]),
(6,[11, 858, 299],[15.0, 167.16, 50.0]),
(17,[2],[65.4785])]).toDF(["id", "collect_list(p_id)","collect_list(cost)"])
#unpack collect_list in desired output format
df = df.rdd.flatMap(lambda row: [(row[0], x, y) for x,y in zip(row[1],row[2])]).toDF(["id", "p_id","cost"])
df1 = df.\
join(df.groupBy("id").agg(mean("cost").alias("mean_cost")), "id", 'left').\
withColumn("result",(col("cost") <= col("mean_cost")).cast("int")).\
drop("mean_cost")
df1.show()
Output is :
+---+----+-------+------+
| id|p_id| cost|result|
+---+----+-------+------+
| 7| 10| 12.0| 1|
| 7| 987| 124.6| 0|
| 7| 872| 197.0| 0|
| 6| 11| 15.0| 1|
| 6| 858| 167.16| 0|
| 6| 299| 50.0| 1|
| 17| 2|65.4785| 1|
+---+----+-------+------+
You can create a result list for every row and then zip pid, cost and result list. After that use explode on the zipped column.
from pyspark.sql.functions import udf, explode
from pyspark.sql.types import *
def zip_cols(pid_list,cost_list):
mean = np.mean(cost_list)
res_list = list(map(lambda cost:1 if mean >= cost else 0,cost_list))
return[(x,y,z) for x,y,z in zip(pid_list, cost_list, res_list)]
udf_zip = udf(zip_cols, ArrayType(StructType([StructField("pid",IntegerType()),
StructField("cost", DoubleType()),
StructField("result",IntegerType())])))
df1 = (df.withColumn("temp",udf_zip("collect_list(p_id)","collect_list(cost)")).
drop("collect_list(p_id)","collect_list(cost)"))
df2 = (df1.withColumn("temp",explode(df1.temp)).
select("id",col("temp.pid").alias("pid"),
col("temp.cost").alias("cost"),
col("temp.result").alias("result")))
df2.show()
output
+---+---+-------+------+
| id|pid| cost|result|
+---+---+-------+------+
| 7| 10| 12.0| 1|
| 7| 98| 124.6| 0|
| 7|872| 197.0| 0|
| 6| 11| 15.0| 1|
| 6|858| 167.16| 0|
| 6|299| 50.0| 1|
| 17| 2|65.4758| 1|
+---+---+-------+------+
I'm trying to concatenate two PySpark dataframes with some columns that are only on one of them:
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:
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?
Maybe you can try creating the unexisting columns and calling union (unionAll for Spark 1.6 or lower):
from pyspark.sql.functions import lit
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)
# To remove the duplicates:
result = result.dropDuplicates()
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_1 and normal_2
unionByName is a built-in option available in spark which is available from spark 2.3.0.
with spark version 3.1.0, there is allowMissingColumns option with the default value set to False to handle missing columns. Even if both dataframes don't have the same set of columns, this function will work, setting missing column values to null in the resulting dataframe.
df_1.unionByName(df_2, allowMissingColumns=True).show()
+---+--------------------+--------------------+--------------------+
| id| uniform| normal| normal_2|
+---+--------------------+--------------------+--------------------+
| 0| 0.8122802274304282| 1.2423430583597714| null|
| 1| 0.8642043127063618| 0.3900018344856156| null|
| 2| 0.8292577771850476| 1.8077401259195247| null|
| 3| 0.198558705368724| -0.4270585782850261| null|
| 4|0.012661361966674889| 0.702634599720141| null|
| 5| 0.8535692890157796|-0.42355804115129153| null|
| 6| 0.3723296190171911| 1.3789648582622995| null|
| 7| 0.9529794127670571| 0.16238718777444605| null|
| 8| 0.9746632635918108| 0.02448061333761742| null|
| 9| 0.513622008243935| 0.7626741803250845| null|
| 11| 0.3221262660507942| null| 1.0269298899109824|
| 12| 0.4030672316912547| null| 1.285648175568798|
| 13| 0.9690555459609131| null|-0.22986601831364423|
| 14|0.011913836266515876| null| -0.678915153834693|
| 15| 0.9359607054250594| null|-0.16557488664743034|
| 16| 0.45680471157575453| null| -0.3885563551710555|
| 17| 0.6411908952297819| null| 0.9161177183227823|
| 18| 0.5669232696934479| null| 0.7270125277020573|
| 19| 0.513622008243935| null| 0.7626741803250845|
+---+--------------------+--------------------+--------------------+
You can use unionByName to make this:
df = df_1.unionByName(df_2)
unionByName is available since Spark 2.3.0.
To make it more generic of keeping both columns in df1 and 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))
To concatenate multiple pyspark dataframes into one:
from functools import reduce
reduce(lambda x,y:x.union(y), [df_1,df_2])
And you can replace the list of [df_1, df_2] to a list of any length.
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.
PS: I guess you meant to use different seeds for the df_1 df_2 and the code below reflects that.
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:
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|
+---+-----------+--------------------+--------------------+
Above answers are very elegant. I have written this function long back where i was also struggling to concatenate two dataframe with distinct columns.
Suppose you have dataframe sdf1 and 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)
This should do it for you ...
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()
I was trying to implement pandas append functionality in pyspark and what I created a custom function where we can concat 2 or more data frame even they are having different no. of columns only condition is if dataframes have identical name then their datatype should be same/match.
I have written a custom function to merge 2 dataframes.
def append_dfs(df1,df2):
list1 = df1.columns
list2 = df2.columns
for col in list2:
if(col not in list1):
df1 = df1.withColumn(col, F.lit(None))
for col in list1:
if(col not in list2):
df2 = df2.withColumn(col, F.lit(None))
return df1.unionByName(df2)
usage:
concate 2 dataframes
final_df = append_dfs(df1,df2)
concate more than 2(say3) dataframes
final_df = append_dfs(append_dfs(df1,df2),df3)
example:
df1:
df2:
result=append_dfs(df1,df2)
result :
Hope this will useful.
I would solve this in this way:
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()
Maybe, you want to concatenate more of two Dataframes.
I found a issue which use pandas Dataframe conversion.
Suppose you have 3 spark Dataframe who want to concatenate.
The code is the following:
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_))