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Use enumerate to get partition columns from dataframe

I am trying to get all columns and their datatypes into a variable, also only the partition columns into another variable of list type in python.
Getting details from describe extended.
df = spark.sql("describe extended schema_name.table_name")
+----------------------------------------------------------+
|col_name |data_type |
+----------------------------+-----------------------------+
|col1 |string |
|col2 |int
|col3 |string
|col4 |int
|col5 |string
|# Partition Information | |
|# col_name |data_type |
|col4 |int |
|col5 |string |
| | |
|# Detailed Table Information| |
|Database |schema_name |
|Table |table_name |
|Owner |owner.name |
Converting result into a list.
des_list=df.select(df.col_name,df.data_type).rdd.map(lambda x:(x[0],x[1])).collect()
Here is how I am trying to get all columns(all items until before # Partition Information).
all_cols_name_type=[]
for index,item in enumerate(des_list):
if item[0]=='# Partition Information':
all_cols_name_type.append(des_list[:index])
For partitions, i would like to get everything between the items '# col_name' and line before ''(line before # Detailed Table Information)
Any help is appreciated to be able to get this.

You can try using the following answer or equivalent in Scala:
val (partitionCols, dataCols) = spark.catalog.listColumns("schema_name.table_name")
.collect()
.partition(c => c.isPartition)
val parCols = partitionCols.map(c => (c.name, c.dataType))
val datCols = dataCols.map(c => (c.name, c.dataType))
If the table is not defined in the catalog (e.g reading parquet dataset directly from s3 using spark.read.parquet("s3://path/...")) then you can use the following snippet in Scala:
val (partitionSchema, dataSchema) = df.queryExecution.optimizedPlan match {
case LogicalRelation(hfs: HadoopFsRelation, _, _, _) =>
(hfs.partitionSchema, hfs.dataSchema)
case DataSourceV2ScanRelation(_, scan: FileScan, _) =>
(scan.readPartitionSchema, scan.readDataSchema)
case _ => (StructType(Seq()), StructType(Seq()))
}
val parCols = partitionSchema.map(f => (f.name, f.dataType))
val datCols = dataSchema.map(f => (f.name, f.dataType))

There is a trick to do so: You can use monotonically_increasing_id to give each row a number, find the row that has # col_name and get that index. Something like this
My sample table
df = spark.sql('describe data')
df = df.withColumn('id', F.monotonically_increasing_id())
df.show()
+--------------------+---------+-------+---+
| col_name|data_type|comment| id|
+--------------------+---------+-------+---+
| c1| int| null| 0|
| c2| string| null| 1|
|# Partition Infor...| | | 2|
| # col_name|data_type|comment| 3|
| c2| string| null| 4|
+--------------------+---------+-------+---+
tricky part
idx = df.where(F.col('col_name') == '# col_name').first()['id']
# 3
partition_cols = [r['col_name'] for r in df.where(F.col('id') > idx).collect()]
# ['c2']

PySpark - Transform multiple columns without using udf

I have a df like this one:
df = spark.createDataFrame(
[("1", "Apple", "cat"), ("2", "2.", "house"), ("3", "<strong>text</strong>", "HeLlo 2.5")],
["id", "text1", "text2"])
+---+---------------------+---------+
| id| text1| text2|
+---+---------------------+---------+
| 1| Apple| cat|
| 2| 2.| house|
| 3|<strong>text</strong>|HeLlo 2.5|
+---+---------------------+---------+
multiple functions to clean text like
def remove_html_tags(text):
document = html.fromstring(text)
return " ".join(etree.XPath("//text()")(document))
def lowercase(text):
return text.lower()
def remove_wrong_dot(text):
return re.sub(r'(?<!\d)[.,;:]|[.,;:](?!\d)', ' ', text)
and a list of columns to clean
COLS = ["text1", "text2"]
I would like to apply the functions to the columns in the list and also keep the original text
+---+---------------------+-----------+---------+-----------+
| id| text1|text1_clean| text2|text2_clean|
+---+---------------------+-----------+---------+-----------+
| 1| Apple| apple| cat| cat|
| 2| 2.| 2| house| house|
| 3|<strong>text</strong>| text|HeLlo 2.5| hello 2.5|
+---+---------------------+-----------+---------+-----------+
I already have an approach using UDF but it is not very efficient. I've been trying something like:
rdds = []
for col in TEXT_COLS:
rdd = df.rdd.map(lambda x: (x[col], lowercase(x[col])))
rdds.append(rdd.collect())
return df
My idea would be to join all rdds in the list but I don't know how efficient this would be or how to list more functions.
I appreciate any ideas or suggestions.
EDIT: Not all transformations can be done with regexp_replace. For example, the text can include nested html labels and in that case a simple replace wouldn't work or I don't want to replace all dots, only those at the end or beginning of substrings

Spark built-in functions can do all the transformations you wanted
from pyspark.sql import functions as F
cols = ["text1", "text2"]
for c in cols:
df = (df
.withColumn(f'{c}_clean', F.lower(c))
.withColumn(f'{c}_clean', F.regexp_replace(f'{c}_clean', '<[^>]+>', ''))
.withColumn(f'{c}_clean', F.regexp_replace(f'{c}_clean', '(?<!\d)[.,;:]|[.,;:](?!\d)', ''))
)
+---+--------------------+---------+-----------+-----------+
| id| text1| text2|text1_clean|text2_clean|
+---+--------------------+---------+-----------+-----------+
| 1| Apple| cat| apple| cat|
| 2| 2.| house| 2| house|
| 3|<strong>text</str...|HeLlo 2.5| text| hello 2.5|
+---+--------------------+---------+-----------+-----------+

Translating a SAS Ranking with Tie set to HIGH into PySpark

I'm trying to replicate the following SAS code in PySpark:
PROC RANK DATA = aud_baskets OUT = aud_baskets_ranks GROUPS=10 TIES=HIGH;
BY customer_id;
VAR expenditure;
RANKS basket_rank;
RUN;
The idea is to rank all expenditures under each customer_id block. The data would look like this:
+-----------+--------------+-----------+
|customer_id|transaction_id|expenditure|
+-----------+--------------+-----------+
| A| 1| 34|
| A| 2| 90|
| B| 1| 89|
| A| 3| 6|
| B| 2| 8|
| B| 3| 7|
| C| 1| 96|
| C| 2| 9|
+-----------+--------------+-----------+
In PySpark, I tried this:
spendWindow = Window.partitionBy('customer_id').orderBy(col('expenditure').asc())
aud_baskets = (aud_baskets_ranks.withColumn('basket_rank', ntile(10).over(spendWindow)))
The problem is that PySpark doesn't let the user change the way it will handle Ties, like SAS does (that I know of). I need to set this behavior in PySpark so that values are moved up to the next tier each time one of those edge cases occur, as oppose to dropping them to the rank below.
Or is there a way to custom write this approach?

Use dense_rank it will give same rank in case of ties and next rank will not be skipped
ntile function split the group of records in each partition into n parts. In your case which is 10
from pyspark.sql.functions import dense_rank
spendWindow = Window.partitionBy('customer_id').orderBy(col('expenditure').asc())
aud_baskets = aud_baskets_ranks.withColumn('basket_rank',dense_rank.over(spendWindow))

Try The following code. It is generated by an automated tool called SPROCKET. It should take care of ties.
df = (aud_baskets)
for (colToRank,rankedName) in zip(['expenditure'],['basket_rank']):
wA = Window.orderBy(asc(colToRank))
df_w_rank = (df.withColumn('raw_rank', rank().over(wA)))
ties = df_w_rank.groupBy('raw_rank').count().filter("""count > 1""")
df_w_rank = (df_w_rank.join(ties,['raw_rank'],'left').withColumn(rankedName,expr("""case when count is not null
then (raw_rank + count - 1) else
raw_rank end""")))
rankedNameGroup = rankedName
n = df_w_rank.count()
df_with_rank_groups = (df_w_rank.withColumn(rankedNameGroup,expr("""FLOOR({rankedName}
*{k}/({n}+1))""".format(k=10, n=n,
rankedName=rankedName))))
df = df_with_rank_groups
aud_baskets_ranks = df_with_rank_groups.drop('raw_rank', 'count')

how to identify people's relationship based on name, address and then assign a same ID through linux comman or Pyspark

I have one csv file.
D,FNAME,MNAME,LNAME,GENDER,DOB,snapshot,Address
2,66M,J,Rock,F,1995,201211.0,J
3,David,HM,Lee,M,1991,201211.0,J
6,66M,,Rock,F,1990,201211.0,J
0,David,H M,Lee,M,1990,201211.0,B
3,Marc,H,Robert,M,2000,201211.0,C
6,Marc,M,Robert,M,1988,201211.0,C
6,Marc,MS,Robert,M,2000,201211.0,D
I want to assign persons with same last name living in the same address a same ID or index. It's better that ID is made up of only numbers.
If persons have different last name in the same place, then ID should be different.
Such ID should be unique. Namely, people who are different in either address or last name, ID must be different.
My expected output is
D,FNAME,MNAME,LNAME,GENDER,DOB,snapshot,Address,ID
2,66M,J,Rock,F,1995,201211.0,J,11
3,David,HM,Lee,M,1991,201211.0,J,12
6,66M,,Rock,F,1990,201211.0,J,11
0,David,H M,Lee,M,1990,201211.0,B,13
3,Marc,H,Robert,M,2000,201211.0,C,14
6,Marc,M,Robert,M,1988,201211.0,C,14
6,Marc,MS,Robert,M,2000,201211.0,D,15
My datafile size is around 30 GB. I am thinking of using groupBy function in spark based on the key consisting of LNAME and address to group those observations together. Then assign it a ID by key. But I don't know how to do this. After that, maybe I can use flatMap to split the line and return those observations with a ID. But I am not sure about it. In addition, can I also make it in Linux environment? Thank you.

As discussed in the comments, the basic idea is to partition the data properly so that records with the same LNAME+Address stay in the same partition, run Python code to generate separate idx on each partition and then merge them into the final id.
Note: I added some new rows in your sample records, see the result of df_new.show() shown below.
from pyspark.sql import Window, Row
from pyspark.sql.functions import coalesce, sum as fsum, col, max as fmax, lit, broadcast
# ...skip code to initialize the dataframe
# tweak the number of repartitioning N based on actual data size
N = 5
# Python function to iterate through the sorted list of elements in the same
# partition and assign an in-partition idx based on Address and LNAME.
def func(partition_id, it):
idx, lname, address = (1, None, None)
for row in sorted(it, key=lambda x: (x.LNAME, x.Address)):
if lname and (row.LNAME != lname or row.Address != address): idx += 1
yield Row(partition_id=partition_id, idx=idx, **row.asDict())
lname = row.LNAME
address = row.Address
# Repartition based on 'LNAME' and 'Address' and then run mapPartitionsWithIndex()
# function to create in-partition idx. Adjust N so that records in each partition
# should be small enough to be loaded into the executor memory:
df1 = df.repartition(N, 'LNAME', 'Address') \
.rdd.mapPartitionsWithIndex(func) \
.toDF()
Get number of unique rows cnt (based on Address+LNAME) which is max_idx and then grab the running SUM of this rcnt.
# idx: calculated in-partition id
# cnt: number of unique ids in the same partition: fmax('idx')
# rcnt: starting_id for a partition(something like a running count): coalesce(fsum('cnt').over(w1),lit(0))
# w1: WindowSpec to calculate the above rcnt
w1 = Window.partitionBy().orderBy('partition_id').rowsBetween(Window.unboundedPreceding,-1)
df2 = df1.groupby('partition_id') \
.agg(fmax('idx').alias('cnt')) \
.withColumn('rcnt', coalesce(fsum('cnt').over(w1),lit(0)))
df2.show()
+------------+---+----+
|partition_id|cnt|rcnt|
+------------+---+----+
| 0| 3| 0|
| 1| 1| 3|
| 2| 1| 4|
| 4| 1| 5|
+------------+---+----+
Join df1 with df2 and create the final id which is idx + rcnt
df_new = df1.join(broadcast(df2), on=['partition_id']).withColumn('id', col('idx')+col('rcnt'))
df_new.show()
#+------------+-------+---+----+-----+------+------+-----+---+--------+---+----+---+
#|partition_id|Address| D| DOB|FNAME|GENDER| LNAME|MNAME|idx|snapshot|cnt|rcnt| id|
#+------------+-------+---+----+-----+------+------+-----+---+--------+---+----+---+
#| 0| B| 0|1990|David| M| Lee| H M| 1|201211.0| 3| 0| 1|
#| 0| J| 3|1991|David| M| Lee| HM| 2|201211.0| 3| 0| 2|
#| 0| D| 6|2000| Marc| M|Robert| MS| 3|201211.0| 3| 0| 3|
#| 1| C| 3|2000| Marc| M|Robert| H| 1|201211.0| 1| 3| 4|
#| 1| C| 6|1988| Marc| M|Robert| M| 1|201211.0| 1| 3| 4|
#| 2| J| 6|1991| 66M| F| Rek| null| 1|201211.0| 1| 4| 5|
#| 2| J| 6|1992| 66M| F| Rek| null| 1|201211.0| 1| 4| 5|
#| 4| J| 2|1995| 66M| F| Rock| J| 1|201211.0| 1| 5| 6|
#| 4| J| 6|1990| 66M| F| Rock| null| 1|201211.0| 1| 5| 6|
#| 4| J| 6|1990| 66M| F| Rock| null| 1|201211.0| 1| 5| 6|
#+------------+-------+---+----+-----+------+------+-----+---+--------+---+----+---+
df_new = df_new.drop('partition_id', 'idx', 'rcnt', 'cnt')
Some notes:
Practically, you will need to clean-out/normalize the column LNAME and Address before using them as uniqueness check. For example, use a separate column uniq_key which combine LNAME and Address as the unique key of the dataframe. see below for an example with some basic data cleansing procedures:
from pyspark.sql.functions import coalesce, lit, concat_ws, upper, regexp_replace, trim
#(1) convert NULL to '': coalesce(col, '')
#(2) concatenate LNAME and Address using NULL char '\x00' or '\0'
#(3) convert to uppercase: upper(text)
#(4) remove all non-[word/whitespace/NULL_char]: regexp_replace(text, r'[^\x00\w\s]', '')
#(5) convert consecutive whitespaces to a SPACE: regexp_replace(text, r'\s+', ' ')
#(6) trim leading/trailing spaces: trim(text)
df = (df.withColumn('uniq_key',
trim(
regexp_replace(
regexp_replace(
upper(
concat_ws('\0', coalesce('LNAME', lit('')), coalesce('Address', lit('')))
),
r'[^\x00\s\w]+',
''
),
r'\s+',
' '
)
)
))
Then in the code, replace 'LNAME' and 'Address' with uniq_key to find the idx
As mentioned by cronoik in the comment, you can also try one of the Window rank functions to calculate the in-partition idx. for example:
from pyspark.sql.functions import spark_partition_id, dense_rank
# use dense_rank to calculate the in-partition idx
w2 = Window.partitionBy('partition_id').orderBy('LNAME', 'Address')
df1 = df.repartition(N, 'LNAME', 'Address') \
.withColumn('partition_id', spark_partition_id()) \
.withColumn('idx', dense_rank().over(w2))
After you have df1, use the same methods as above to calculate df2 and df_new. This should be faster than using mapPartitionsWithIndex() which is basically an RDD-based method.
For your real data, adjust N to fit your actual data size. this N only influences the initial partitions, after dataframe join, the partition will be reset to default(200). you can adjust this using spark.sql.shuffle.partitions for example when you initialize the spark session:
spark = SparkSession.builder \
....
.config("spark.sql.shuffle.partitions", 500) \
.getOrCreate()

Since you have 30GB of input data, you probably don't want something that'll attempt to hold it all in in-memory data structures. Let's use disk space instead.
Here's one approach that loads all your data into a sqlite database, and generates an id for each unique last name and address pair, and then joins everything back up together:
#!/bin/sh
csv="$1"
# Use an on-disk database instead of in-memory because source data is 30gb.
# This will take a while to run.
db=$(mktemp -p .)
sqlite3 -batch -csv -header "${db}" <<EOF
.import "${csv}" people
CREATE TABLE ids(id INTEGER PRIMARY KEY, lname, address, UNIQUE(lname, address));
INSERT OR IGNORE INTO ids(lname, address) SELECT lname, address FROM people;
SELECT p.*, i.id AS ID
FROM people AS p
JOIN ids AS i ON (p.lname, p.address) = (i.lname, i.address)
ORDER BY p.rowid;
EOF
rm -f "${db}"
Example:
$./makeids.sh data.csv
D,FNAME,MNAME,LNAME,GENDER,DOB,snapshot,Address,ID
2,66M,J,Rock,F,1995,201211.0,J,1
3,David,HM,Lee,M,1991,201211.0,J,2
6,66M,"",Rock,F,1990,201211.0,J,1
0,David,"H M",Lee,M,1990,201211.0,B,3
3,Marc,H,Robert,M,2000,201211.0,C,4
6,Marc,M,Robert,M,1988,201211.0,C,4
6,Marc,MS,Robert,M,2000,201211.0,D,5
It's better that ID is made up of only numbers.
If that restriction can be relaxed, you can do it in a single pass by using a cryptographic hash of the last name and address as the ID:
$ perl -MDigest::SHA=sha1_hex -F, -lane '
BEGIN { $" = $, = "," }
if ($. == 1) { print #F, "ID" }
else { print #F, sha1_hex("#F[3,7]") }' data.csv
D,FNAME,MNAME,LNAME,GENDER,DOB,snapshot,Address,ID
2,66M,J,Rock,F,1995,201211.0,J,5c99211a841bd2b4c9cdcf72d7e95e46b2ae08b5
3,David,HM,Lee,M,1991,201211.0,J,c263f9d1feb4dc789de17a8aab8f2808aea2876a
6,66M,,Rock,F,1990,201211.0,J,5c99211a841bd2b4c9cdcf72d7e95e46b2ae08b5
0,David,H M,Lee,M,1990,201211.0,B,e86e81ab2715a8202e41b92ad979ca3a67743421
3,Marc,H,Robert,M,2000,201211.0,C,363ed8175fdf441ed59ac19cea3c37b6ce9df152
6,Marc,M,Robert,M,1988,201211.0,C,363ed8175fdf441ed59ac19cea3c37b6ce9df152
6,Marc,MS,Robert,M,2000,201211.0,D,cf5135dc402efe16cd170191b03b690d58ea5189
Or if the number of unique lname, address pairs is small enough that they can reasonably be stored in a hash table on your system:
#!/usr/bin/gawk -f
BEGIN {
FS = OFS = ","
}
NR == 1 {
print $0, "ID"
next
}
! ($4, $8) in ids {
ids[$4, $8] = ++counter
}
{
print $0, ids[$4, $8]
}

$ sort -t, -k8,8 -k4,4 <<EOD | awk -F, ' $8","$4 != last { ++id; last = $8","$4 }
{ NR!=1 && $9=id; print }' id=9 OFS=,
D,FNAME,MNAME,LNAME,GENDER,DOB,snapshot,Address
2,66M,J,Rock,F,1995,201211.0,J
3,David,HM,Lee,M,1991,201211.0,J
6,66M,,Rock,F,1990,201211.0,J
0,David,H M,Lee,M,1990,201211.0,B
3,Marc,H,Robert,M,2000,201211.0,C
6,Marc,M,Robert,M,1988,201211.0,C
6,Marc,MS,Robert,M,2000,201211.0,D
> EOD
D,FNAME,MNAME,LNAME,GENDER,DOB,snapshot,Address
0,David,H M,Lee,M,1990,201211.0,B,11
3,Marc,H,Robert,M,2000,201211.0,C,12
6,Marc,M,Robert,M,1988,201211.0,C,12
6,Marc,MS,Robert,M,2000,201211.0,D,13
3,David,HM,Lee,M,1991,201211.0,J,14
2,66M,J,Rock,F,1995,201211.0,J,15
6,66M,,Rock,F,1990,201211.0,J,15
$

Adding column to dataframe and updating in pyspark

I have a dataframe in pyspark:
ratings = spark.createDataFrame(
sc.textFile("transactions.json").map(lambda l: json.loads(l)),
)
ratings.show()
+--------+-------------------+------------+----------+-------------+-------+
|click_id| created_at| ip|product_id|product_price|user_id|
+--------+-------------------+------------+----------+-------------+-------+
| 123|2016-10-03 12:50:33| 10.10.10.10| 98373| 220.5| 1|
| 124|2017-02-03 11:51:33| 10.13.10.10| 97373| 320.5| 1|
| 125|2017-10-03 12:52:33| 192.168.2.1| 96373| 20.5| 1|
| 126|2017-10-03 13:50:33|172.16.11.10| 88373| 220.5| 2|
| 127|2017-10-03 13:51:33| 10.12.15.15| 87373| 320.5| 2|
| 128|2017-10-03 13:52:33|192.168.1.10| 86373| 20.5| 2|
| 129|2017-08-03 14:50:33| 10.13.10.10| 78373| 220.5| 3|
| 130|2017-10-03 14:51:33| 12.168.1.60| 77373| 320.5| 3|
| 131|2017-10-03 14:52:33| 10.10.30.30| 76373| 20.5| 3|
+--------+-------------------+------------+----------+-------------+-------+
ratings.registerTempTable("transactions")
final_df = sqlContext.sql("select * from transactions");
I want to add a new column to this data frame called status and then update the status column based on created_at and user_id.
The created_at and user_id are read from the given table transations and passed to a function get_status(user_id,created_at) which returns the status. This status needs to be put into the transaction table as a new column for the corresponding user_id and created_at
Can I run alter and update command in pyspark?
How can this be done using pyspark ?

It's not clear what you want to do exactly. You should check out window functions they allow you to compare, sum... rows in a frame.
For instance
import pyspark.sql.functions as psf
from pyspark.sql import Window
w = Window.partitionBy("user_id").orderBy(psf.desc("created_at"))
ratings.withColumn(
"status",
psf.when(psf.row_number().over(w) == 1, "active").otherwise("inactive")).sort("click_id").show()
+--------+-------------------+------------+----------+-------------+-------+--------+
|click_id| created_at| ip|product_id|product_price|user_id| status|
+--------+-------------------+------------+----------+-------------+-------+--------+
| 123|2016-10-03 12:50:33| 10.10.10.10| 98373| 220.5| 1|inactive|
| 124|2017-02-03 11:51:33| 10.13.10.10| 97373| 320.5| 1|inactive|
| 125|2017-10-03 12:52:33| 192.168.2.1| 96373| 20.5| 1| active|
| 126|2017-10-03 13:50:33|172.16.11.10| 88373| 220.5| 2|inactive|
| 127|2017-10-03 13:51:33| 10.12.15.15| 87373| 320.5| 2|inactive|
| 128|2017-10-03 13:52:33|192.168.1.10| 86373| 20.5| 2| active|
| 129|2017-08-03 14:50:33| 10.13.10.10| 78373| 220.5| 3|inactive|
| 130|2017-10-03 14:51:33| 12.168.1.60| 77373| 320.5| 3|inactive|
| 131|2017-10-03 14:52:33| 10.10.30.30| 76373| 20.5| 3| active|
+--------+-------------------+------------+----------+-------------+-------+--------+
It gives you each user's last click
If you want to pass a UDF to create a new column from two existing ones.
Say you have a function that takes the user_id and created_at as arguments
from pyspark.sql.types import *
def get_status(user_id,created_at):
...
get_status_udf = psf.udf(get_status, StringType())
StringType() or whichever datatype your function outputs
ratings.withColumn("status", get_status_udf("user_id", "created_at"))