I have a dataset of around 5 million records. The dates are read in as strings. They are in the form MM/DD/YYYY HH:MM:SS. I am only interested in the date part of it so I read them in as (A10) format which effectively trims the time.
I then do ALTER TYPE DateVar (SDATE10). I do this as I thought sorting dates would be quicker but I can't find confirmation of this.
Is there a way to time SPSS commands to work out questions like this?
The quickest way I can think of is to use python for the timestamps, and normal SPSS syntax for the sorting - just to replicate real-life conditions
***Start timer, in python.
begin program.
import time
start = time.time()
end program.
***go out of python, into normal SPSS syntax, and do your stuff.
/*Put the syntax you want to test here
***get back to python, stop timer, and calculate time difference.
begin program.
end = time.time()
print("It took ",end - start, " seconds")
end program.
Check the output log, and it will show you the time.
Not very scientific, but quick and easy.
I recommend re-starting SPSS between tests - just to be sure one test is not affecting the other.
From my experience, alter type does something that affects code execution times. Not sure what, but everything seems slower after an alter type. So you might also consider saving and re-opening after using alter type.
You should keep the Date format, because:
Dates In spss are actually numbers (formatted in the display as dates but just numbers all the same). Sorting numbers is faster than sorting strings.
In any case, sorting by dates as strings will not order the file by dates (eg. "12-OCT-2017" > "11-NOV-2017").
See another good reason in #horace_vr's comment below.
Related
I have joined in a Data competition for students. They gave me timestamps of users' interaction. Is it useful?
Some of them are:
1615983880510
1615767552552
1615767577100
1616036788631
What you are looking at is a linux timestamp. It's actually a pretty interesting time representation. In short, it's just a number. And it represents the total number of seconds that have passed since January 1st 1970. A date known as "Unix Epoch Time".
Now, if you want to convert that into a readable date there are many ways to do it in basically every programing language. For example in python you might do something like this:
from datetime import datetime
def printdate(unix):
print(datetime.utcfromtimestamp(unix).strftime('%Y-%m-%d %H:%M:%S'))
BUT! It seems like your dates might actually be in miliseconds. Meaning that you might actually want to divide your dates by 1000 before passing them trough the function. So...
def printdatems(unix):
return printdate(unix/1000)
And there you go!
printdatems(1615983880510) #2021-03-17 12:24:40
printdatems(1615767552552) #2021-03-15 00:19:12
printdatems(1615767577100) #2021-03-15 00:19:37
printdatems(1616036788631) #2021-03-18 03:06:28
That's the output for the example dates you provided.
Of course you can find much more information on Wikipedia:
https://en.wikipedia.org/wiki/Unix_time
It's an intresting read!
I am trying to reduce the size of my data and I cannot make it work. I have data points taken every minute over 1 month. I want to reduce this data to have one sample for every hour. The problem is: Some of my runs have "NA" value, so I delete these rows. There is not exactly 60 points for every hour - it varies.
I have a 'Timestamp' column. I have used this to make a 'datehour' column which has the same value if the data set has the same date and hour. I want to average all the values with the same 'datehour' value.
How can I do this? I have tried using the if and for loop below, but it takes so long to run.
Thanks for all your help! I am new to Julia and come from a Matlab background.
======= CODE ==========
uniquedatehour=unique(datehour,1)
index=[]
avedata=reshape([],0,length(alldata[1,:]))
for j in uniquedatehour
for i in 1:length(datehour)
if datehour[i]==j
index=vcat(index,i)
else
rows=alldata[index,:]
rows=convert(Array{Float64,2},rows)
avehour=mean(rows,1)
avedata=vcat(avedata,avehour)
index=[]
continue
end
end
end
There are several layers to optimizing this code. I am assuming that your data is sorted on datehour (your code assumes this).
Layer one: general recommendation
Wrap your code in a function. Executing code in global scope in Julia is much slower than within a function. By wrapping it make sure to either pass data to your function as arguments or if data is in global scope it should be qualified with const;
Layer two: recommendations to your algorithm
Statement like [] creates an array of type Any which is slow, you should use type qualifier like index=Int[] to make it fast;
Using vcat like index=vcat(index,i) is inefficient, it is better to do push!(index, i) in place;
It is better to preallocate avedata with e.g. fill(NA, length(uniquedatehour), size(alldata, 2)) and assign values to an existing matrix than to do vcat on it;
Your code will produce incorrect results if I am not mistaken as it will not catch the last entry of uniquedatehour vector (assume it has only one element and check what happens - avedata will have zero rows)
Line rows=convert(Array{Float64,2},rows) is probably not needed at all. If alldata is not Matrix{Float64} it is better to convert it at the beginning with Matrix{Float64}(alldata);
You can change line rows=alldata[index,:] to a view like view(alldata, index, :) to avoid allocation;
In general you can avoid creation of index vector as it is enough that you remember start s and end e position of the range of the same values and then use range s:e to select rows you want.
If you correct those things please post your updated code and maybe I can help further as there is still room for improvement but requires a bit different algorithmic approach (but maybe you will prefer option below for simplicity).
Layer three: how I would do it
I would use DataFrames package to handle this problem like this:
using DataFrames
df = DataFrame(alldata) # assuming alldata is Matrix{Float64}, otherwise convert it here
df[:grouping] = datehour
agg = aggregate(df, :grouping, mean) # maybe this is all what you need if DataFrame is OK for you
Matrix(agg[2:end]) # here is how you can convert DataFrame back to a matrix
This is not the fastest solution (as it converts to a DataFrame and back but it is much simpler for me).
I need logic for below scenario which needs to be implemented using Pig scripts. Can anyone please help in providing some ideas on how to do this.
Input contains a column groupName with some data like others and unknown. This data needs to be replaced by its previous record data.
Input:
id,groupName
123,casc0001
124,casc0002
125,sale0001
126,unknown
127,nave9876
128,casc0001
129,sale0002
130,others
131,casc0004
132,unknown
133,unknown
134,others
135,nave1234
output:
123,casc0001
124,casc0002
125,sale0001
126,sale0001
127,nave9876
128,casc0001
129,sale0002
130,sale0002
131,casc0004
132,casc0004
133,casc0004
134,casc0004
135,nave1234
In the above input 126,unknown to be replaced with 125,sale0001. 130,others need to be replaced by 129,sale0002. 132,unknown 133,unknown 134,others to be replaced with 131,casc0004.
--Edit--
I tried lead function in Pig. But it is used only to compare n rows at a time. Which cannot solve this completely.
Another logic which is working, but looking for optimized one.
Cogroup for the same data set (like Dataset and Dataset_self)
-Filter Dataset.id=Dataset_self.id or Dataset_self.groupname='others' or Dataset_self.groupname='unknown'
-Generate IdDiff like (Dataset_self.id-Dataset.id), CASE when id=id then ( id, group) else (id_self,group)
-Foreach (group id){
ordered = order by id,diff,group;
limited = ordered limit 1;
generate limited ;
}
This is going to be a complicated problem on a distributed system like hadoop, especially that your file is going to be split between nodes. In your case what if 126 happens to be the first record in a new split. Then you will need to trace the previous file split which is most likely on a different node. Lets say you come up with a MapReduce program to do this, in all likelyhood it would an extremely slow and inefficient way to do it. The solution might be simpler if you are in a single node system where the splittable property of your input format is false, and the nuber of reducers is set to 1.
In that case you could almost make the argument that a traditional database like Oracle or Terra data might be a better fit for your problem as you have lead or lag functions readily available which could be used to do exactly what u need.
This is something that has puzzled me for some time and I have yet to find an answer.
I am in a situation where I am applying a standardized data cleaning process to (supposedly) similarly structured files, one file for each year. I have a statement such as the following:
replace field="Plant" if field=="Plant & Machinery"
Which was a result of the original code-writing based on the data file for year 1. Then I generalize the code to loop through the years of data. The problem becomes if in year 3, the analogous value in that variable was coded as "Plant and MachInery ", such that the code line above would not make the intended change due to the difference in the text string, but not result in an error alerting the change was not made.
What I am after is some sort of confirmation that >0 observations actually satisfied the condition each instance the code is executed in the loop, otherwise return an error. Any combination of trimming, removing spaces, and standardizing the text case are not workaround options. At the same time, I don't want to add a count if and then assert statement before every conditional replace as that becomes quite bulky.
Aside from going to the raw files to ensure the variable values are standardized, is there any way to do this validation "on the fly" as I have tried to describe? Maybe just write a custom program that combines a count if, assert and replace?
The idea has surfaced occasionally that replace should return the number of observations changed, but there are good reasons why not, notably that it is not a r-class or e-class command any way and it's quite important not to change the way it works because that could break innumerable programs and do-files.
So, I think the essence of any answer is that you have to set up your own monitoring process counting how many values have (or would be) changed.
One pattern is -- when working on a current variable:
gen was = .
foreach ... {
...
replace was = current
replace current = ...
qui count if was != current
<use the result>
}
I'd like to calculate the standard deviation over two fields from the same dataset.
example:
MyFields1 = 10, 10
MyFields2 = 20
What I want now, is the standard deviation for (10,10,20), the expected result is 4.7
In SSRS I'd like to have something like this:
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value)
Unfortunately this isn't possible, since (Fields!MyField1.Value + Fields!MyField2.Value) returns a single value and not a list of values. Is there no way to combine two fields from the same dataset into some kind of temporary dataset?
The only solutions I have are:
To create a new Dataset that contains all values from both fields. But this is very annoying because I need about twenty of those and I have six report parameters that need to filter every query. => It's probably getting very slow and annoying to maintain.
Write the formula by hand. But I don't really know how yet. StDevP is not that trivial to me. This is how I did it with Avg which is mathematically simpler:
=(SUM(Fields!MyField1.Value)+SUM(Fields!MyField2.Value))/2
found here: http://social.msdn.microsoft.com/Forums/is/sqlreportingservices/thread/7ff43716-2529-4240-a84d-42ada929020e
Btw. I know that it's odd to make such a calculation, but this is what my customer wants and I have to deliver somehow.
Thanks for any help.
CTDevP is standard deviation.
Such expression works fine for me
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value) but it's deviation from one value (Fields!MyField1.Value + Fields!MyField2.Value) which is always 0.
you can look here for formula:
standard deviation (wiki)
I believe that you need to calculate this for some group (or full dataset), to do this you need set in the CTDevP your scope:
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value, "MyDataSet1")