I am working with a pyspark dataframe as shown below:
df1:
+-----------+-------+------------+----------+
|parsed_date| id| count| date|
+-----------+-------+------------+----------+
| 2018-01-16|1520036| 1277|2018-01-17|
| 2018-01-14|1516457| 767|2018-01-17|
| 2018-01-15|1518451| 1074|2018-01-17|
| 2018-01-24|1536787| 1306|2018-01-27|
| 2018-01-25|1537211| 1105|2018-01-27|
| 2018-01-26|1539203| 1100|2018-01-27|
| 2019-01-03|2325105| 1298|2019-01-16|
+-----------+-------+------------+----------+
I am want to sum all the count for same date :
df2:
+----------+----------+
| date| sum |
+----------+----------+
|2018-01-17| 3118|
|2018-01-27| 3511|
|2019-01-16| 1298|
+----------+----------+
So far I could do the following inside a for loop for different date:
df1_list = []
for d in date_list:
df1= my_func(df, d)
df1 = df1.withColumn("sum", F.sum("count").over(Window.partitionBy("date")))
df1_list.append(df1)
full_df1 = reduce(DataFrame.unionAll, df1_list)
But now there can be a case when there is a date with no records in df1 (or let's say some date is not there in df1) so I want to add sum as zero as shown below:
expected output:
example -> date_list: 2018-01-17, 2018-01-27, 2019-01-16, 2019-01-18
+----------+----------+
| date| sum |
+----------+----------+
|2018-01-17| 3118|
|2018-01-27| 3511|
|2019-01-16| 1298|
|2019-01-18| 0|
+----------+----------+
How can I use if condition (or any other logic) while making new column sum to get this done?
You can create a dataframe from date_list and do a left join to df, before a group by and sum:
import pyspark.sql.functions as F
date_list = ['2018-01-17', '2018-01-27', '2019-01-16', '2019-01-18']
date_df = spark.createDataFrame([[d] for d in date_list], 'date string')
result = (date_df.join(df, 'date', 'left')
.fillna(0, 'count')
.groupBy('date')
.agg(F.sum('count').alias('sum'))
)
result.show()
+----------+----+
| date| sum|
+----------+----+
|2018-01-17|3118|
|2019-01-16|1298|
|2018-01-27|3511|
|2019-01-18| 0|
+----------+----+
I have a df that only has one row.
id |id2 |score|score2|
----------------------
0 |1 |4 |2 |
and i want to add a row of the percent of these to the bottom, i.e. every number divided by 7
0/7 |1/7 |4/7 |2/7 |
but the solution I came up with is incredibly slow
temp = [i/7 for i in df.collect()[0]]
row = sc.parallelize(Row(temp)).toDF()
df.union(row)
This took 21 seconds to run, almost all of which is the last two lines of code. Is there a better way to do this? My other thought was to transpose the table then this can easily be done with df.withColumn(). Ideally, I also want to filter out the column with 0, but I haven't really looked into that yet
check this out and let me know if it helps
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
spark = SparkSession.builder \
.appName('practice')\
.getOrCreate()
sc= spark.sparkContext
df = sc.parallelize([
(0,1,4,2)]).toDF(["id", "id2","score","score2"])
df2 = df.select(*[(F.col(column)/7).alias(column) for column in df.columns])
df3 = df.union(df2)
df3.show()
+---+-------------------+------------------+------------------+
| id| id2| score| score2|
+---+-------------------+------------------+------------------+
|0.0| 1.0| 4.0| 2.0|
|0.0|0.14285714285714285|0.5714285714285714|0.2857142857142857|
+---+-------------------+------------------+------------------+
If you want to. filter out the column having 0 you can use below code
non_zero_cols = [c for c in df.columns if df[[c]].first()[c] > 0]
df1 = df.select(*non_zero_cols)
df2 = df1.select(*[(F.col(column)/7).alias(column) for column in
df1.columns])
df3 = df1.union(df2)
df3.show()
+-------------------+------------------+------------------+
| id2| score| score2|
+-------------------+------------------+------------------+
| 1.0| 4.0| 2.0|
|0.14285714285714285|0.5714285714285714|0.2857142857142857|
+-------------------+------------------+------------------+
Please check the below code for df having type column
non_zero_cols = [c for c in df.columns if df[[c]].first()[c] > 0]
df1 = df.select(*non_zero_cols, F.lit('count').alias('type') )
df2 = df1.select(*[(F.col(column)/7).alias(column) for column in
df1.columns if not column=='type'], F.lit('percent').alias('type'))
df3 = df1.union(df2)
df3.show()
+-------------------+------------------+------------------+-------+
| id2| score| score2| type|
+-------------------+------------------+------------------+-------+
| 1.0| 4.0| 2.0| count|
|0.14285714285714285|0.5714285714285714|0.2857142857142857|percent|
+-------------------+------------------+------------------+-------+
I have two spark dataframes:
Dataframe A:
|col_1 | col_2 | ... | col_n |
|val_1 | val_2 | ... | val_n |
and dataframe B:
|col_1 | col_2 | ... | col_m |
|val_1 | val_2 | ... | val_m |
Dataframe B can contain duplicate, updated and new rows from dataframe A. I want to write an operation in spark where I can create a new dataframe containing the rows from dataframe A and the updated and new rows from dataframe B.
I started by creating a hash column containing only the columns that are not updatable. This is the unique id. So let's say col1 and col2 can change value (can be updated), but col3,..,coln are unique. I have created a hash function as hash(col3,..,coln):
A=A.withColumn("hash", hash(*[col(colname) for colname in unique_cols_A]))
B=B.withColumn("hash", hash(*[col(colname) for colname in unique_cols_B]))
Now I want to write some spark code that basically selects the rows from B that have the hash not in A (so new rows and updated rows) and join them into a new dataframe together with the rows from A. How can I achieve this in pyspark?
Edit:
Dataframe B can have extra columns from dataframe A, so a union is not possible.
Sample example
Dataframe A:
+-----+-----+
|col_1|col_2|
+-----+-----+
| a| www|
| b| eee|
| c| rrr|
+-----+-----+
Dataframe B:
+-----+-----+-----+
|col_1|col_2|col_3|
+-----+-----+-----+
| a| wew| 1|
| d| yyy| 2|
| c| rer| 3|
+-----+-----+-----+
Result:
Dataframe C:
+-----+-----+-----+
|col_1|col_2|col_3|
+-----+-----+-----+
| a| wew| 1|
| b| eee| null|
| c| rer| 3|
| d| yyy| 2|
+-----+-----+-----+
This is closely related to update a dataframe column with new values, except that you also want to add the rows from DataFrame B. One approach would be to first do what is outlined in the linked question and then union the result with DataFrame B and drop duplicates.
For example:
dfA.alias('a').join(dfB.alias('b'), on=['col_1'], how='left')\
.select(
'col_1',
f.when(
~f.isnull(f.col('b.col_2')),
f.col('b.col_2')
).otherwise(f.col('a.col_2')).alias('col_2'),
'b.col_3'
)\
.union(dfB)\
.dropDuplicates()\
.sort('col_1')\
.show()
#+-----+-----+-----+
#|col_1|col_2|col_3|
#+-----+-----+-----+
#| a| wew| 1|
#| b| eee| null|
#| c| rer| 3|
#| d| yyy| 2|
#+-----+-----+-----+
Or more generically using a list comprehension if you have a lot of columns to replace and you don't want to hard code them all:
cols_to_update = ['col_2']
dfA.alias('a').join(dfB.alias('b'), on=['col_1'], how='left')\
.select(
*[
['col_1'] +
[
f.when(
~f.isnull(f.col('b.{}'.format(c))),
f.col('b.{}'.format(c))
).otherwise(f.col('a.{}'.format(c))).alias(c)
for c in cols_to_update
] +
['b.col_3']
]
)\
.union(dfB)\
.dropDuplicates()\
.sort('col_1')\
.show()
I would opt for different solution, which I believe is less verbose, more generic and does not involve column listing. I would first identify subset of dfA that will be updated (replaceDf) by performing inner join based on keyCols (list). Then I would subtract this replaceDF from dfA and union it with dfB.
replaceDf = dfA.alias('a').join(dfB.alias('b'), on=keyCols, how='inner').select('a.*')
resultDf = dfA.subtract(replaceDf).union(dfB).show()
Even though there will be different columns in dfA and dfB, you can still overcome this with obtaining list of columns from both DataFrames and finding their union. Then I would
prepare select query (instead of "select.('a.')*") so that I would just list columns from dfA that exist in dfB + "null as colname" for those that do not exist in dfB.
If you want to keep only unique values, and require strictly correct results, then union followed by dropDupilcates should do the trick:
columns_which_dont_change = [...]
old_df.union(new_df).dropDuplicates(subset=columns_which_dont_change)
When you join two DFs with similar column names:
df = df1.join(df2, df1['id'] == df2['id'])
Join works fine but you can't call the id column because it is ambiguous and you would get the following exception:
pyspark.sql.utils.AnalysisException: "Reference 'id' is ambiguous,
could be: id#5691, id#5918.;"
This makes id not usable anymore...
The following function solves the problem:
def join(df1, df2, cond, how='left'):
df = df1.join(df2, cond, how=how)
repeated_columns = [c for c in df1.columns if c in df2.columns]
for col in repeated_columns:
df = df.drop(df2[col])
return df
What I don't like about it is that I have to iterate over the column names and delete them why by one. This looks really clunky...
Do you know of any other solution that will either join and remove duplicates more elegantly or delete multiple columns without iterating over each of them?
If the join columns at both data frames have the same names and you only need equi join, you can specify the join columns as a list, in which case the result will only keep one of the join columns:
df1.show()
+---+----+
| id|val1|
+---+----+
| 1| 2|
| 2| 3|
| 4| 4|
| 5| 5|
+---+----+
df2.show()
+---+----+
| id|val2|
+---+----+
| 1| 2|
| 1| 3|
| 2| 4|
| 3| 5|
+---+----+
df1.join(df2, ['id']).show()
+---+----+----+
| id|val1|val2|
+---+----+----+
| 1| 2| 2|
| 1| 2| 3|
| 2| 3| 4|
+---+----+----+
Otherwise you need to give the join data frames alias and refer to the duplicated columns by the alias later:
df1.alias("a").join(
df2.alias("b"), df1['id'] == df2['id']
).select("a.id", "a.val1", "b.val2").show()
+---+----+----+
| id|val1|val2|
+---+----+----+
| 1| 2| 2|
| 1| 2| 3|
| 2| 3| 4|
+---+----+----+
df.join(other, on, how) when on is a column name string, or a list of column names strings, the returned dataframe will prevent duplicate columns.
when on is a join expression, it will result in duplicate columns. We can use .drop(df.a) to drop duplicate columns. Example:
cond = [df.a == other.a, df.b == other.bb, df.c == other.ccc]
# result will have duplicate column a
result = df.join(other, cond, 'inner').drop(df.a)
Assuming 'a' is a dataframe with column 'id' and 'b' is another dataframe with column 'id'
I use the following two methods to remove duplicates:
Method 1: Using String Join Expression as opposed to boolean expression. This automatically remove a duplicate column for you
a.join(b, 'id')
Method 2: Renaming the column before the join and dropping it after
b.withColumnRenamed('id', 'b_id')
joinexpr = a['id'] == b['b_id']
a.join(b, joinexpr).drop('b_id)
The code below works with Spark 1.6.0 and above.
salespeople_df.show()
+---+------+-----+
|Num| Name|Store|
+---+------+-----+
| 1| Henry| 100|
| 2| Karen| 100|
| 3| Paul| 101|
| 4| Jimmy| 102|
| 5|Janice| 103|
+---+------+-----+
storeaddress_df.show()
+-----+--------------------+
|Store| Address|
+-----+--------------------+
| 100| 64 E Illinos Ave|
| 101| 74 Grand Pl|
| 102| 2298 Hwy 7|
| 103|No address available|
+-----+--------------------+
Assuming -in this example- that the name of the shared column is the same:
joined=salespeople_df.join(storeaddress_df, ['Store'])
joined.orderBy('Num', ascending=True).show()
+-----+---+------+--------------------+
|Store|Num| Name| Address|
+-----+---+------+--------------------+
| 100| 1| Henry| 64 E Illinos Ave|
| 100| 2| Karen| 64 E Illinos Ave|
| 101| 3| Paul| 74 Grand Pl|
| 102| 4| Jimmy| 2298 Hwy 7|
| 103| 5|Janice|No address available|
+-----+---+------+--------------------+
.join will prevent the duplication of the shared column.
Let's assume that you want to remove the column Num in this example, you can just use .drop('colname')
joined=joined.drop('Num')
joined.show()
+-----+------+--------------------+
|Store| Name| Address|
+-----+------+--------------------+
| 103|Janice|No address available|
| 100| Henry| 64 E Illinos Ave|
| 100| Karen| 64 E Illinos Ave|
| 101| Paul| 74 Grand Pl|
| 102| Jimmy| 2298 Hwy 7|
+-----+------+--------------------+
After I've joined multiple tables together, I run them through a simple function to drop columns in the DF if it encounters duplicates while walking from left to right. Alternatively, you could rename these columns too.
Where Names is a table with columns ['Id', 'Name', 'DateId', 'Description'] and Dates is a table with columns ['Id', 'Date', 'Description'], the columns Id and Description will be duplicated after being joined.
Names = sparkSession.sql("SELECT * FROM Names")
Dates = sparkSession.sql("SELECT * FROM Dates")
NamesAndDates = Names.join(Dates, Names.DateId == Dates.Id, "inner")
NamesAndDates = dropDupeDfCols(NamesAndDates)
NamesAndDates.saveAsTable("...", format="parquet", mode="overwrite", path="...")
Where dropDupeDfCols is defined as:
def dropDupeDfCols(df):
newcols = []
dupcols = []
for i in range(len(df.columns)):
if df.columns[i] not in newcols:
newcols.append(df.columns[i])
else:
dupcols.append(i)
df = df.toDF(*[str(i) for i in range(len(df.columns))])
for dupcol in dupcols:
df = df.drop(str(dupcol))
return df.toDF(*newcols)
The resulting data frame will contain columns ['Id', 'Name', 'DateId', 'Description', 'Date'].
In my case I had a dataframe with multiple duplicate columns after joins and I was trying to same that dataframe in csv format, but due to duplicate column I was getting error. I followed below steps to drop duplicate columns. Code is in scala
1) Rename all the duplicate columns and make new dataframe
2) make separate list for all the renamed columns
3) Make new dataframe with all columns (including renamed - step 1)
4) drop all the renamed column
private def removeDuplicateColumns(dataFrame:DataFrame): DataFrame = {
var allColumns: mutable.MutableList[String] = mutable.MutableList()
val dup_Columns: mutable.MutableList[String] = mutable.MutableList()
dataFrame.columns.foreach((i: String) =>{
if(allColumns.contains(i))
if(allColumns.contains(i))
{allColumns += "dup_" + i
dup_Columns += "dup_" +i
}else{
allColumns += i
}println(i)
})
val columnSeq = allColumns.toSeq
val df = dataFrame.toDF(columnSeq:_*)
val unDF = df.drop(dup_Columns:_*)
unDF
}
to call the above function use below code and pass your dataframe which contains duplicate columns
val uniColDF = removeDuplicateColumns(df)
Here is simple solution for remove duplicate column
final_result=df1.join(df2,(df1['subjectid']==df2['subjectid']),"left").drop(df1['subjectid'])
If you join on a list or string, dup cols are automatically]1 removed
This is a scala solution, you could translate the same idea into any language
// get a list of duplicate columns or use a list/seq
// of columns you would like to join on (note that this list
// should include columns for which you do not want duplicates)
val duplicateCols = df1.columns.intersect(df2.columns)
// no duplicate columns in resulting DF
df1.join(df2, duplicateCols.distinct.toSet)
Spark SQL version of this answer:
df1.createOrReplaceTempView("t1")
df2.createOrReplaceTempView("t2")
spark.sql("select * from t1 inner join t2 using (id)").show()
# +---+----+----+
# | id|val1|val2|
# +---+----+----+
# | 1| 2| 2|
# | 1| 2| 3|
# | 2| 3| 4|
# +---+----+----+
This works for me when multiple columns used to join and need to drop more than one column which are not string type.
final_data = mdf1.alias("a").join(df3.alias("b")
(mdf1.unique_product_id==df3.unique_product_id) &
(mdf1.year_week==df3.year_week) ,"left" ).select("a.*","b.promotion_id")
Give a.* to select all columns from one table and from the other table choose specific columns.
Suppose I've got a data frame df (created from a hard-coded array for tests)
+----+----+---+
|name| c1|qty|
+----+----+---+
| a|abc1| 1|
| a|abc2| 0|
| b|abc3| 3|
| b|abc4| 2|
+----+----+---+
I am grouping and aggregating it to get df1
import pyspark.sql.functions as sf
df1 = df.groupBy('name').agg(sf.min('qty'))
df1.show()
+----+--------+
|name|min(qty)|
+----+--------+
| b| 2|
| a| 0|
+----+--------+
What is the expected order of the rows in df1 ?
Suppose now that I am writing a unit test. I need to compare df1 with the expected data frame. Should I compare them ignoring the order of rows. What is the best way to do it ?
The ordering of the rows in the dataframe is not fixed. There is an easy way to use the expected Dataframe in test cases
Do a dataframe diff . For scala:
assert(df1.except(expectedDf).count == 0)
And
assert(expectedDf.except(df1).count == 0)
For python you need to replace except by subtract
From documentation:
subtract(other)
Return a new DataFrame containing rows in this frame but not in another frame.
This is equivalent to EXCEPT in SQL.