I have an import query (table a) and an imported Excel file (table b) with records I am trying to match it up with.
I am looking for a method to replicate this type of SQL in M:
SELECT a.loc_id, a.other_data, b.stk
FROM a INNER JOIN b on a.loc_id BETWEEN b.from_loc AND b.to_loc
Table A
| loc_id | other data |
-------------------------
| 34A032B1 | ... |
| 34A3Z011 | ... |
| 3DD23A41 | ... |
Table B
| stk | from_loc | to_loc |
--------------------------------
| STKA01 | 34A01 | 34A30ZZZ |
| STKA02 | 34A31 | 34A50ZZZ |
| ... | ... | ... |
Goal
| loc_id | other data | stk |
----------------------------------
| 34A032B1 | ... | STKA01 |
| 34A3Z011 | ... | STKA02 |
| 3DD23A41 | ... | STKD01 |
All of the other queries I can find along these lines use numbers, dates, or times in the BETWEEN clause, and seem to work by exploding the (from, to) range into all possible values and then filtering out the extra rows. However I need to use string comparisons, and exploding those into all possible values would be unfeasable.
Between all the various solutions I could find, the closest I've come is to add a custom column on table a:
Table.SelectRows(
table_b,
(a) => Value.Compare([loc_id], table_b[from_loc]) = 1
and Value.Compare([loc_id], table_b[to_loc]) = -1
)
This does return all the columns from table_b, however, when expanding the column, the values are all null.
This is not very specific "After 34A01 could be any string..." in trying to figure out how your series progresses.
But maybe you can just test for how a value "sorts" using the native sorting function in PQ.
add custom column with table.Select Rows:
= try Table.SelectRows(TableB, (t)=> t[from_loc]<=[loc_id] and t[to_loc] >= [loc_id])[stk]{0} otherwise null
To reproduce with your examples:
let
TableB=Table.FromColumns(
{{"STKA01","STKA02"},
{"34A01","34A31"},
{"34A30ZZZ","34A50ZZZ"}},
type table[stk=text,from_loc=text,to_loc=text]),
TableA=Table.FromColumns(
{{"34A032B1","34A3Z011","3DD23A41"},
{"...","...","..."}},
type table[loc_id=text, other data=text]),
//determine where it sorts and return the stk
#"Added Custom" = Table.AddColumn(#"TableA", "stk", each
try Table.SelectRows(TableB, (t)=> t[from_loc]<=[loc_id] and t[to_loc] >= [loc_id])[stk]{0} otherwise null)
in
#"Added Custom"
Note: if the above algorithm is too slow, there may be faster methods of obtaining these results
Related
I'm trying to interact with this List[Option[Map[String, DataFrame]]] but I'm having a bit of trouble.
Inside it has something like this:
customer1 -> dataframeX
customer2 -> dataframeY
customer3 -> dataframeZ
Where the customer is an identifier that will become a new column.
I need to do an union of dataframeX, dataframeY and dataframeZ (all df have the same columns). Before I had this:
map(_.get).reduce(_ union _).select(columns:_*)
And it was working fine because I only had a List[Option[DataFrame]] and didn't need the identifier but I'm having trouble with the new list. My idea is to modify my old mapping, I know I can do stuff like "(0).get" and that would bring me "Map(customer1 -> dataframeX)" but I'm not quite sure how to do that iteration in the mapping and get the final dataframe that is the union of all three plus the identifier. My idea:
map(/*get identifier here along with dataframe*/).reduce(_ union _).select(identifier +: columns:_*)
The final result would be something like:
-------------------------------
|identifier | product |State |
-------------------------------
| customer1| prod1 | VA |
| customer1| prod132 | VA |
| customer2| prod32 | CA |
| customer2| prod51 | CA |
| customer2| prod21 | AL |
| customer2| prod52 | AL |
-------------------------------
You could use collect to unnest Option[Map[String, Dataframe]] to Map[String, DataFrame]. To put an identifier into the column you should use withColumn. So your code could look like:
import org.apache.spark.sql.functions.lit
val result: DataFrame = frames.collect {
case Some(m) =>
m.map {
case (identifier, dataframe) => dataframe.withColumn("identifier", lit(identifier))
}.reduce(_ union _)
}.reduce(_ union _)
Something like this perhaps?
list
.flatten
.flatMap {
_.map { case (id, df) =>
df.withColumn("identifier", id) }
}.reduce(_ union _)
I am trying to use Django StrIndex to find all rows with the value a substring of a given string.
Eg:
my table contains:
+----------+------------------+
| user | domain |
+----------+------------------+
| spam1 | spam.com |
| badguy+ | |
| | protonmail.com |
| spammer | |
| | spamdomain.co.uk |
+----------+------------------+
but the query
SpamWord.objects.annotate(idx=StrIndex(models.Value('xxxx'), 'user')).filter(models.Q(idx__gt=0) | models.Q(domain='spamdomain.co.uk')).first()
matches <SpamWord: *#protonmail.com>
The query it is SELECT `spamwords`.`id`, `spamwords`.`user`, `spamwords`.`domain`, INSTR('xxxx', `spamwords`.`user`) AS `idx` FROM `spamwords` WHERE (INSTR('xxxx', `spamwords`.`user`) > 0 OR `spamwords`.`domain` = 'spamdomain.co.uk')
It should be <SpamWord: *#spamdomain.co.uk>
this is happening because
INSTR('xxxx', '') => 1
(and also INSTR('xxxxasd', 'xxxx') => 1, which it is correct)
How can I write this query in order to get entry #5 (spamdomain.co.uk)?
The order of the parameters of StrIndex [Django-doc] is swapped. The first parameter is the haystack, the string in which you search, and the second one is the needle, the substring you are looking for.
You thus can annotate with:
from django.db.models import Q, Value
SpamWord.objects.annotate(
idx=StrIndex('user', Value('xxxx'))
).filter(
Q(idx__gt=0) | Q(domain='spamdomain.co.uk')
).first()
Just filter rows where user is empty:
(~models.Q(user='') & models.Q(idx__gt=0)) | models.Q(domain='spamdomain.co.uk')
I have a table with numeric values and blank records. I'm trying to calculate a number of rows that are not blank and bigger than 20.
+--------+
| VALUES |
+--------+
| 2 |
| 0 |
| 13 |
| 40 |
| |
| 1 |
| 200 |
| 4 |
| 135 |
| |
| 35 |
+--------+
I've tried different options but constantly get the next error: "Cannot convert value '' of type Text to type Number". I understand that blank cells are treated as text and thus my filter (>20) doesn't work. Converting blanks to "0" is not an option as I need to use the same values later to calculate AVG and Median.
CALCULATE(
COUNTROWS(Table3),
VALUE(Table3[VALUES]) > 20
)
OR getting "10" as a result:
=CALCULATE(
COUNTROWS(ALLNOBLANKROW(Table3[VALUES])),
VALUE(Table3[VALUES]) > 20
)
The final result in the example table should be: 4
Would be grateful for any help!
First, the VALUE function expects a string. It converts strings like "123"into the integer 123, so let's not use that.
The easiest approach is with an iterator function like COUNTX.
CountNonBlank = COUNTX(Table3, IF(Table3[Values] > 20, 1, BLANK()))
Note that we don't need a separate case for BLANK() (null) here since BLANK() > 20 evaluates as False.
There are tons of other ways to do this. Another iterator solution would be:
CountNonBlank = COUNTROWS(FILTER(Table3, Table3[Values] > 20))
You can use the same FILTER inside of a CALCULATE, but that's a bit less elegant.
CountNonBlank = CALCULATE(COUNT(Table3[Values]), FILTER(Table3, Table3[Values] > 20))
Edit
I don't recommend the CALCULATE version. If you have more columns with more conditions, just add them to your FILTER. E.g.
CountNonBlank =
COUNTROWS(
FILTER(Table3,
Table3[Values] > 20
&& Table3[Text] = "xyz"
&& Table3[Number] <> 0
&& Table3[Date] <= DATE(2018, 12, 31)
)
)
You can also do OR logic with || instead of the && for AND.
Not sure why I'm having a difficult time with this, it seems so simple considering it's fairly easy to do in R or pandas. I wanted to avoid using pandas though since I'm dealing with a lot of data, and I believe toPandas() loads all the data into the driver’s memory in pyspark.
I have 2 dataframes: df1 and df2. I want to filter df1 (remove all rows) where df1.userid = df2.userid AND df1.group = df2.group. I wasn't sure if I should use filter(), join(), or sql For example:
df1:
+------+----------+--------------------+
|userid| group | all_picks |
+------+----------+--------------------+
| 348| 2|[225, 2235, 2225] |
| 567| 1|[1110, 1150] |
| 595| 1|[1150, 1150, 1150] |
| 580| 2|[2240, 2225] |
| 448| 1|[1130] |
+------+----------+--------------------+
df2:
+------+----------+---------+
|userid| group | pick |
+------+----------+---------+
| 348| 2| 2270|
| 595| 1| 2125|
+------+----------+---------+
Result I want:
+------+----------+--------------------+
|userid| group | all_picks |
+------+----------+--------------------+
| 567| 1|[1110, 1150] |
| 580| 2|[2240, 2225] |
| 448| 1|[1130] |
+------+----------+--------------------+
EDIT:
I've tried many join() and filter() functions, I believe the closest I got was:
cond = [df1.userid == df2.userid, df2.group == df2.group]
df1.join(df2, cond, 'left_outer').select(df1.userid, df1.group, df1.all_picks) # Result has 7 rows
I tried a bunch of different join types, and I also tried different
cond values:
cond = ((df1.userid == df2.userid) & (df2.group == df2.group)) # result has 7 rows
cond = ((df1.userid != df2.userid) & (df2.group != df2.group)) # result has 2 rows
However, it seems like the joins are adding additional rows, rather than deleting.
I'm using python 2.7 and spark 2.1.0
Left anti join is what you're looking for:
df1.join(df2, ["userid", "group"], "leftanti")
but the same thing can be done with left outer join:
(df1
.join(df2, ["userid", "group"], "leftouter")
.where(df2["pick"].isNull())
.drop(df2["pick"]))
I have a DataFrame inside of a function:
using DataFrames
myservs = DataFrame(serverName = ["elmo", "bigBird", "Oscar", "gRover", "BERT"],
ipAddress = ["12.345.6.7", "12.345.6.8", "12.345.6.9", "12.345.6.10", "12.345.6.11"])
myservs
5x2 DataFrame
| Row | serverName | ipAddress |
|-----|------------|---------------|
| 1 | "elmo" | "12.345.6.7" |
| 2 | "bigBird" | "12.345.6.8" |
| 3 | "Oscar" | "12.345.6.9" |
| 4 | "gRover" | "12.345.6.10" |
| 5 | "BERT" | "12.345.6.11" |
How can I write the function to take a single parameter called server, case-insensitive match the server parameter in the myservs[:serverName] DataArray, and return the match's corresponding ipAddress?
In R this can be done by using
myservs$ipAddress[grep("server", myservs$serverName, ignore.case = T)]
I don't want it to matter if someone uses ElMo or Elmo as the server, or if the serverName is saved as elmo or ELMO.
I referenced how to accomplish the task in R and tried to do it using the DataFrames pkg, but I only did this because I'm coming from R and am just learning Julia. I asked a lot of questions from coworkers and the following is what we came up with:
This task is much cleaner if I was to stop thinking in terms of
vectors in R. Julia runs plenty fast iterating through a loop.
Even still, looping wouldn't be the best solution here. I was told to look into
Dicts (check here for an example). Dict(), zip(), haskey(), and
get() blew my mind. These have many applications.
My solution doesn't even need to use the DataFrames pkg, but instead
uses Julia's Matrix and Array data representations. By using let
we keep the global environment clutter free and the server name/ip
list stays hidden from view to those who are only running the
function.
In the sample code, I'm recreating the server matrix every time, but in reality/practice I'll have a permission restricted delimited file that gets read every time. This is OK for now since the delimited files are small, but this may not be efficient or the best way to do it.
# ONLY ALLOW THE FUNCTION TO BE SEEN IN THE GLOBAL ENVIRONMENT
let global myIP
# SERVER MATRIX
myservers = ["elmo" "12.345.6.7"; "bigBird" "12.345.6.8";
"Oscar" "12.345.6.9"; "gRover" "12.345.6.10";
"BERT" "12.345.6.11"]
# SERVER DICT
servDict = Dict(zip(pmap(lowercase, myservers[:, 1]), myservers[:, 2]))
# GET SERVER IP FUNCTION: INPUT = SERVER NAME; OUTPUT = IP ADDRESS
function myIP(servername)
sn = lowercase(servername)
get(servDict, sn, "That name isn't in the server list.")
end
end
# Test it out
myIP("SLIMEY")
#>"That name isn't in the server list."
myIP("elMo")
#>"12.345.6.7"
Here's one way:
julia> using DataFrames
julia> myservs = DataFrame(serverName = ["elmo", "bigBird", "Oscar", "gRover", "BERT"],
ipAddress = ["12.345.6.7", "12.345.6.8", "12.345.6.9", "12.345.6.10", "12.345.6.11"])
5x2 DataFrames.DataFrame
| Row | serverName | ipAddress |
|-----|------------|---------------|
| 1 | "elmo" | "12.345.6.7" |
| 2 | "bigBird" | "12.345.6.8" |
| 3 | "Oscar" | "12.345.6.9" |
| 4 | "gRover" | "12.345.6.10" |
| 5 | "BERT" | "12.345.6.11" |
julia> grep{T <: String}(pat::String, dat::DataArray{T}, opts::String = "") = Bool[isna(d) ? false : ismatch(Regex(pat, opts), d) for d in dat]
grep (generic function with 2 methods)
julia> myservs[:ipAddress][grep("bigbird", myservs[:serverName], "i")]
1-element DataArrays.DataArray{ASCIIString,1}:
"12.345.6.8"
EDIT
This grep works faster on my platform.
julia> function grep{T <: String}(pat::String, dat::DataArray{T}, opts::String = "")
myreg = Regex(pat, opts)
return convert(Array{Bool}, map(d -> isna(d) ? false : ismatch(myreg, d), dat))
end