I have a data set that that looks like this:
adjuster adjuster_zip
A-20 98216
A-14 98214
A-17 98216
A-20 California
I need to format this data set so that adjuster_zip is all numeric. I have several hundred adjusters and they all show up several hundred times. However, they each adjuster only has one zip code. As you can see with A-20, this adjuster has both a valid and invalid zip code. All of the adjusters that have invalid zip codes also have valid zip codes. How can I automate this so that SAS switches invalid zip codes with valid ones by adjuster?
Thanks for any and all help.
Also, I couldn't figure out how to format the data so that it shows up in a table. Sorry.
My suggestion would be to build a format table per adjuster. Start with your input dataset; then filter to only valid zip codes (you could use NOTDIGIT to check for any nondigit values, and LENGTH to check it is only five long). Then create a dataset with FMTNAME as a constant string with any legal format name you wish preceded by $ ($ADJZIPF would be a good cohice), START equal to the variable that contains the adjuster name, LABEL being the zip. Then use PROC FORMAT with cntlin= the dataset you just defined.
That would allow you to look up the zip for each adjuster using PUT and your custom format. You still have to worry about a few things; that table must be non-duplicated per adjuster, so you need to decide how to handle adjusters with two or more zips; and you need to check when you use PUT that it does find a zip code.
Related
I downloaded daily MODIS DATA LEVEL 3 data for a few months from https://disc.gsfc.nasa.gov/datasets. The filenames are of the form MCD06COSP_M3_MODIS.A2006001.061.2020181145945 but the files do not contain any time dimension. Hence when I use ncecat to concatenate various files, the date information is missing in the resulting file. I want to know how to add the time information in the combined dataset.
Your commands look correct. Good job crafting them. Not sure why it's not working. Possibly the input files are HDF4 format (do they have a .hdf suffix?) and your NCO is not HDF4-enabled. Try to download the files in netCDF3 or netCDF4 format and your commands above should work. If that's not what's wrong, then examine the output files in each step of your procedure and identify which step produces the unintended results and then narrow your question. Good luck.
i have many .csv files which are stored into gcs and i want to load data from.csv to BigQuery using below commands:
bq load 'datasate.table' gs://path.csv json_schema
i have tried but giving errors, same error is giving for many file.
error screenshot
how can i remove unwanted values from .csv files before importing into table.
Suggest me to load file in easiest way
The answer depends on what do you want to do with this junk rows. If you look at the documentation, you have several options
Number of errors allowed. By default, it's set to 0 and that why the load job fails at the first line. If you know the total number of rom, set this value to the Number of errors allowed and all the errors will be ignored in the Load Job
Ignore unknown values. If your errors are made because some line contains more column as defined in the schema, this option keep the line in error and only the known column, the others are ignore
Allow jagged rows. If your errors are made by too short line (and it is in your message) and you still want to keep the first columns (because the last ones are optional and/or not relevant), you can check this option
For more advanced and specific filters, you have to perform pre or post processing. If it's the case, let me know to add this part to my answer.
I have a task to complete.
There are two types of csv files 4000+ both related to each other.
2 types are:
1. Country2.csv
2. Security_Name.csv
Contents of Country2.csv:
Company Name;Security Name;;;;Final NOS;Final FFR
Contents of Security_Name.csv:
Date;Close Price;Volume
There are multiple countries and for each country multiple security files
Now I need to READ them do some CALCULATION and then WRITE the output in another files
READ
Read both the file Country 2.csv and Security.csv and extract all the data from them.
For example :
Read France 2.csv, extract Security_Name, Final NOS, Final FFR
Then Read Security.csv(which matches the Security_Name) and extract Date, Close Price, Volume
Calculation
Calculations are basically finding Median of the values extracted which is quite simple.
For Example:
Monthly Median Traded Values
Daily Traded Value of a Security ... and so on
Write
Based on the month I need to sort the output in two different file with following formats:
If Month % 3 = 0
Save It as MONTH_NAME.csv in following format:
Security name; 12-month indicator; 3-month indicator; FOT
Else
Save It as MONTH_NAME.csv in following format:
Security Name; Monthly Median Traded Value Ratio; Number of days Volume > 0
My question is how do I design my application in such a way that it is maintainable and the flow of data throughout the execution is seamless?
So first thing. Based on the kind of data you are looking to generate, I would probably be looking at moving this data to a SQL db if at all possible. This is "one SQL query" kind of stuff. And far more maintainable than C++ that generates CSV files from CSV files.
Barring that, I would probably look at using datamash and/or perl. On a Windows platform, you could do this through Cygwin or WSL. Probably less maintainable, but so much easier it's not too much of an issue.
That said, if you're looking for something moderately maintainable, C++ could work. The first thing I would do is design my input classes. Data-centric, but it can work. It sounds like you could have a Country class, a Security class, and a SecurityClose class...or something along those lines. You can think about whether a Security class should contain a collection of SecurityClosees (data), or whether the data should just be "loose" and reference the Security it belongs to. Same with the Country->Security relationship.
Once you've decided how all that's going to look, you want something (likely a function) that can tokenize a CSV line. So "1,2,3" gets turned into a vector<string> with the contents "1" "2" "3". Then, each of your input classes should have a constructor or initializer that takes a vector<string> and populates itself. You might need to pass higher level data along too. Like the filename if you want the security data to know which security it belongs to..
That's basically most of the battle there. Once you've pulled your data into sensibly organized classes, the rest should come more easily. And if you run into bumps, hopefully you can ask specific design or implementation questions from there.
I'm working with a project using Kettle (PDI).
I have to input multiple file of .csv or .xls and insert it into DB.
The file name are AAMMDDBBBB, where AA is code for city and BBBB is code for shop. MMDD is date format like MM-DD. For example LA0326F5CA.csv.
The Regexp I use in the Input file steps look like LA.\*\\.csv or DT.*\\.xls, which is return all files to insert it into DB.
Can you indicate me how to select the files the file just for yesterday (based on the MMDD of the file name).
As you need some "complex" logic in your selection, you cannot filter based only on regexp. I suggest you first read all filenames, then filter the filenames based on their "age", then read the file based on the selected filenames.
In detail:
Use the Get File Names step with the same regexp you currently use (LA.*\.csv or DT.*\.xls). You may be more restrictive at that stage with a Regexp like LA\d\d\d\d.....csv, to ensure MM and DD are numbers, and DDDD is exactly 4 characters.
Filter based on the date. You can do this with a Java Filter, but it would be an order of magnitude easier to use a Javascript Script to compute the "age" of you file and then to use a Filter rows to keep only the file of yesterday.
To compute the age of the file, extract the MM and DD, you can use (other methods are available):
var regexp = filename.match(/..(\d\d)(\d\d).*/);
if(regexp){
var age = new Date() - new Date(2018, regexp[1], regexp[2]);
age = age /1000 /60 /60 /24;
};
If you are not familiar with Javascript regexp: the match will test
the filename against the regexp and keep the values of the parenthesis
in an array. If the test succeed (which you must explicitly check to
avoid run time failure), use the values of the match to compute the
corresponding date, and subtract the date of today to get the age.
This age is in milliseconds, which is converted in days.
Use the Text File Input and Excel Input with the option Accept file from previous step. Note that CSV Input does not have this option, but the more powerful Text File Input has.
well I change the Java Filter with Modified Java Script Value and its work fine now.
Another question, how can I increase the Performance and Speed of my current transformation(now I have 2 trans. for 2 cities)? My insert update make my tranformation to slow and need almost 1hour and 30min to process 500k row of data with alot of field(300mb) and my data not only this, if is it work more fast and my company like to used it, im gonna do it with 10TB of data/years and its alot of trans and rows. I need sugestion about it
Suppose we have a large dataset of anonymized data. Dataset consist if certain number of variables and observations. All we can learn about data is a type(numeric, char, date, etc.) of variable. We can do it by looking to data manually.
What are the best practise steps of pre-proccessing dataset for the further analysis?
Just for instance, let this data set be just one table, so we don't need to check any relations between tables.
This link gives the complete set of validations currently in practice. Still, to start with:
wherever possible, have your data written in such a way that you can parse it as fast and as easily as possible, using your preferred programming language's methods/constructors;
you can verify if all the data types match correctly - like int fields do not contain string data etc;
you can verify that your values are in acceptable range;
check if a non-nullable field has null values;
check if dates are in expected ranges;
check if data follows correct set-membership constraints wherever applicable;
if you have pattern following data like phone numbers, make sure they are in (XXX) XXX-XXXX design, if you prefer them that way;
are the zip codes at correct accuracy level (in US you may have 5 or 9 digits of accuracy);
if your data is time-series, is it complete (i.e. you have values for all dates)?
is there any unwanted duplication?
Hope this is good enough to get you started...