I want to join two Datasets with one overlapping key variable. I just want the data of persons in dataset 1 whose ID is also mentioned in dataset one. Here is my code:
STAR JOIN
/SELECT t0.var1, t0.var2, t1.lfdn, t1.var4,
t1.tester, t1.dispcode, t1.lastpage, t1.quality, t1.duration, t1.E1
/FROM * AS t0
/JOIN 'dataset2.sav' AS t1
ON t0.p_0001=t1.p_0001
/OUTFILE FILE=*.
When I merge them I get the variable names added to the file, but not the data in the variables.
Try using match files instead of star join.
your command should go approximately like this:
*first make sure both files are sorted by `p_0001`.
match files /file=* /tab='dataset2.sav'/by p_0001/keep var1 var2 var3.
Use the keep (or drop) subcommand to select the variables you need in your final dataset.
Related
I have a CSV File in the Following format which want to copy from an external share to my datalake:
Test; Text
"1"; "This is a text
which goes on on a second line
and on on a third line"
"2"; "Another Test"
I do now want to load it with a Copy Data Task in an Azure Synapse Pipeline. The result is the following:
Test; Text
"1";" \"This is a text"
"which goes on on a second line";
"and on on a third line\"";
"2";" \"Another Test\""
So, yo see, it is not handling the Multi-Line Text correct. I also do not see an option to handle multiline text within a Copy Data Task. Unfortunately i'm not able to use a DataFlow Task, because it is not allowing to run with an external Azure Runtime, which i'm forced to use, due to security reasons.
In fact, i'm of course not speaking about this single test file, instead i do have x thousands of files.
My settings for the CSV File look like follows:
CSV Connector Settings
Can someone tell me how to handle this kind of multiline data correctly?
Do I have any other options within Synapse (apart from the Dataflows)?
Thanks a lot for your help
Well turns out this is not possible with a CSV File.
The pragmatic solution is to use "binary" files instead, to transfer the CSV Files and only load and transform them later on with a Python Notebook in Synapse.
You can achieve this in azure data factory by iterating through all lines and check for delimiter in each line. And then, use string manipulation functions with set variable activities to convert multi-line data to a single line.
Look at the following example. I have a set variable activity with empty value (taken from parameter) for req variable.
In lookup, create a dataset with following configuration to the multiline csv:
In foreach, where I iterate each row by giving items value as #range(0,sub(activity('Lookup1').output.count,1)). Inside for each, I have an if activity with following condition:
#contains(activity('Lookup1').output.value[item()]['Prop_0'],';')
If this is true, then I concat the current result to req variable using 2 set variable activities.
temp: #if(contains(activity('Lookup1').output.value[add(item(),1)]['Prop_0'],';'),concat(variables('req'),activity('Lookup1').output.value[item()]['Prop_0'],decodeUriComponent('%0D%0A')),concat(variables('req'),activity('Lookup1').output.value[item()]['Prop_0'],' '))
actual (req variable): #variables('val')
For false, I have handled the concatenation in the following way:
temp1: #concat(variables('req'),activity('Lookup1').output.value[item()]['Prop_0'],' ')
actual1 (req variable): #variables('val2')
Now, I have used a final variable to handle last line of the file. I have used the following dynamic content for that:
#if(contains(last(activity('Lookup1').output.value)['Prop_0'],';'),concat(variables('req'),decodeUriComponent('%0D%0A'),last(activity('Lookup1').output.value)['Prop_0']),concat(variables('req'),last(activity('Lookup1').output.value)['Prop_0']))
Finally, I have taken copy data activity with a sample source file with 1 column and 1 row (using this to copy our actual data).
Now, take source file configuration as shown below:
Create an additional column with value as final variable value:
Create a sink with following configuration and select mapping for only above created column:
When I run the pipeline, I get the data as required. The following is an output image for reference.
i'm tryimg to loop over a diffrent countries folder that got fixed sub folder named survey (i.e Spain/survey , USA/survey ).
where and how I Need to define a wildcard / parameter for the countries so I could loop over all the files that in the survey folder ?
what is the right wildcard syntax ? ( the equivalent of - like 'survey%' in SQL) ?
I tried several ways to define it with no success and I would be happy to get some help on this - Thanks !
In case if the list of paths are static, you can create a parameter or add it in a SQL database and get that result from a lookup activity.
Pass the output to a for each activity and within foreach activity use a copy activity.
You can parameterize the input dataset to get the file paths thereby you need not think of any wildcard characters but use the actual paths itself.
Hope this is helpful.
Lets say I have a directory called 'all_data', and inside this, I have several other directories based on the date of the data that it contains. These directories are named date_2020_11_01 to date_2020_11_30 and each one of these contain csv files which I intend to read in a single dataframe.
But I don't want to read the data for date_2020_11_15 and date_2020_11_16. How do I do it?
I'm not sure how to exclude certain files, but you can specify a range of file names using brackets. Code below would select all files without 11_15 and 11_16:
spark.read.csv("date_2020_11_{1[0-4,7-9],[0,2-3][0-9]}.csv")
df= spark.read.format("parquet").option("header", "true").load(paths)
where paths is a list of all the paths where data is present, worked for me.
Simple method is, read all data directory as it is and apply filter condition
df.filter("dataColumn != 'date_2020_11_15' & 'date_2020_11_16'")
Else you can use OS module read directory and iterate to that list to eliminate those date directory using condition.
I have a set of multiple API's I need to source data from and need four different data categories. This data is then used for reporting purposes in Excel.
I initially created web queries in Excel, but my Laptop just crashes because there is too many querie which have to be updated. Do you guys know a smart workaround?
This is an example of the API I will source data from (40 different ones in total)
https://api.similarweb.com/SimilarWebAddon/id.priceprice.com/all
The data points I need are:
EstimatedMonthlyVisits, TopOrganicKeywords, OrganicSearchShare, TrafficSources
Any ideas how I can create an automated report which queries the above data on request?
Thanks so much.
If Excel is crashing due to the demand, and that doesn't surprise me, you should consider using Python or R for this task.
install.packages("XML")
install.packages("plyr")
install.packages("ggplot2")
install.packages("gridExtra")
require("XML")
require("plyr")
require("ggplot2")
require("gridExtra")
Next we need to set our working directory and parse the XML file as a matter of practice, so we're sure that R can access the data within the file. This is basically reading the file into R. Then, just to confirm that R knows our file is in XML, we check the class. Indeed, R is aware that it's XML.
setwd("C:/Users/Tobi/Documents/R/InformIT") #you will need to change the filepath on your machine
xmlfile=xmlParse("pubmed_sample.xml")
class(xmlfile) #"XMLInternalDocument" "XMLAbstractDocument"
Now we can begin to explore our XML. Perhaps we want to confirm that our HTTP query on Entrez pulled the correct results, just as when we query PubMed's website. We start by looking at the contents of the first node or root, PubmedArticleSet. We can also find out how many child nodes the root has and their names. This process corresponds to checking how many entries are in the XML file. The root's child nodes are all named PubmedArticle.
xmltop = xmlRoot(xmlfile) #gives content of root
class(xmltop)#"XMLInternalElementNode" "XMLInternalNode" "XMLAbstractNode"
xmlName(xmltop) #give name of node, PubmedArticleSet
xmlSize(xmltop) #how many children in node, 19
xmlName(xmltop[[1]]) #name of root's children
To see the first two entries, we can do the following.
# have a look at the content of the first child entry
xmltop[[1]]
# have a look at the content of the 2nd child entry
xmltop[[2]]
Our exploration continues by looking at subnodes of the root. As with the root node, we can list the name and size of the subnodes as well as their attributes. In this case, the subnodes are MedlineCitation and PubmedData.
#Root Node's children
xmlSize(xmltop[[1]]) #number of nodes in each child
xmlSApply(xmltop[[1]], xmlName) #name(s)
xmlSApply(xmltop[[1]], xmlAttrs) #attribute(s)
xmlSApply(xmltop[[1]], xmlSize) #size
We can also separate each of the 19 entries by these subnodes. Here we do so for the first and second entries:
#take a look at the MedlineCitation subnode of 1st child
xmltop[[1]][[1]]
#take a look at the PubmedData subnode of 1st child
xmltop[[1]][[2]]
#subnodes of 2nd child
xmltop[[2]][[1]]
xmltop[[2]][[2]]
The separation of entries is really just us, indexing into the tree structure of the XML. We can continue to do this until we exhaust a path—or, in XML terminology, reach the end of the branch. We can do this via the numbers of the child nodes or their actual names:
#we can keep going till we reach the end of a branch
xmltop[[1]][[1]][[5]][[2]] #title of first article
xmltop[['PubmedArticle']][['MedlineCitation']][['Article']][['ArticleTitle']] #same command, but more readable
Finally, we can transform the XML into a more familiar structure—a dataframe. Our command completes with errors due to non-uniform formatting of data and nodes. So we must check that all the data from the XML is properly inputted into our dataframe. Indeed, there are duplicate rows, due to the creation of separate rows for tag attributes. For instance, the ELocationID node has two attributes, ValidYN and EIDType. Take the time to note how the duplicates arise from this separation.
#Turning XML into a dataframe
Madhu2012=ldply(xmlToList("pubmed_sample.xml"), data.frame) #completes with errors: "row names were found from a short variable and have been discarded"
View(Madhu2012) #for easy checking that the data is properly formatted
Madhu2012.Clean=Madhu2012[Madhu2012[25]=='Y',] #gets rid of duplicated rows
Here is a link that should help you get started.
http://www.informit.com/articles/article.aspx?p=2215520
If you have never used R before, it will take a little getting used to, but it's worth it. I've been using it for a few years now and when compared to Excel, I have seen R perform anywhere from a couple hundred percent faster to many thousands of percent faster than Excel. Good luck.
I'm not sure how to use proc datasets statement. Here is the error and code attached as a picture.
I just don't know what it wants when it says error 22-322 expecting a name. A simple example or solution would be great thanks.
There are several issues with your syntax:
Proc datasets expects a library name, but you've given it a dataset name. Try using library = work;.
In conjunction with the above, you need to add the line modify passengers; before the format statement so that proc datasets knows which dataset to modify. Otherwise, it will run without errors, but it won't apply the format.
You need a quit; after the run; when using proc datasets, as mentioned in your log output. This is because a proc datasets call can contain multiple run; groups, so you need to indicate that you've got to the last one.
You also have the option to put the format statement somewhere else, which would avoid having to use proc datasets at all:
The data step where you're creating work.passengers, or
The proc print where you're viewing it, if you don't want to apply the format permanently.