Making writing to a file process more efficient - python-3.6

I am new to programming and i am running this script to clean a large text file (over 12000 lines) and write it to another .txt file. The problem is when a run this with a smaller file (roughly around 500 line) it executes fast, therefore my conclusion was it is taking time due to the size of the file. So if someone can guide me to make this code efficient it will be highly appreciated.
input_file = open('bNEG.txt', 'rt', encoding='utf-8')
l_p = LanguageProcessing()
sentences=[]
for lines in input_file.readlines():
tokeniz = l_p.tokeniz(lines)
cleaned_url = l_p.clean_URL(tokeniz)
remove_words = l_p.remove_non_englishwords(cleaned_url)
stopwords_removed = l_p.remove_stopwords(remove_words)
cleaned_sentence=' '.join(str(s) for s in stopwords_removed)+"\n"
output_file = open('cNEG.txt', 'w', encoding='utf-8')
sentences.append(cleaned_sentence)
output_file.writelines(sentences)
input_file.close()
output_file.close()
EDIT: Below is the corrected code as mentioned in the answer with few other alteration to suit my requirements
input_file = open('chromehistory_log.txt', 'rt', encoding='utf-8')
output_file = open('dNEG.txt', 'w', encoding='utf-8')
l_p = LanguageProcessing()
#sentences=[]
for lines in input_file.readlines():
#print(lines)
tokeniz = l_p.tokeniz(lines)
cleaned_url = l_p.clean_URL(tokeniz)
remove_words = l_p.remove_non_englishwords(cleaned_url)
stopwords_removed = l_p.remove_stopwords(remove_words)
#print(stopwords_removed)
if stopwords_removed==[]:
continue
else:
cleaned_sentence=' '.join(str(s) for s in stopwords_removed)+"\n"
#sentences.append(cleaned_sentence)
output_file.writelines(cleaned_sentence)
input_file.close()
output_file.close()

To have the discussion as answer:
Two problems are here:
You open / create the outputfile and write the data in the loop - for every line of the input file. Additional you are collection all data in an array (sentences).
You have two possibilities:
a) Create the file before the loop, and write in the loop just "cleaned_sentence" (and delete the collecting "sentences").
b) Collect everything in "sentences" and write "sentences" at once after the loop.
Disadvantage of a) is: this is a bit slower than b) (as long as the OS di not have to swap memory for b). But the advantage is: This is much less memory consuming and will work no matter how big the file is and how less memory in the computer is installed.

Related

Reading multiple txt files from a directory

I am very new to Julia and I have a question regarding reading some files. I need to read 12500 .txt files from the same directory and save them all into 1 array but I'm having performance issues. Is there a fast way of doing this? My code takes around like 60 seconds which is way more than I can afford. Here is what I have:
function load_train(directory)
data = []
dir = joinpath("./aclImdb/train/",directory)
for f in readdir(dir)
s = read(joinpath(dir,f),String)
push!(data,s)
end
data
end
trainPos = load_train("pos/")

How do I read one line at a time in from two files in Julia?

I am working in Julia 1.4. I would like to open two large gzipped files (file1.gz and file2.gz) and then read the first line from file 1, the first line from file 2, do something with these, and then move on to the second line of each file etc.
If I nest two for loops, this obviously does not work because it cycles through file2 before moving on to the next line of file1. The files are two big to open all at once.
handle1 = GZip.open(file1.gz)
handle2 = GZip.open(file2.gz)
for line1 in eachline(handle1)
for line2 in eachline(handle2)
println(line1,line2)
end
end
Is there a simple solution ?
Yes, you can use zip. You can also manage the eachline iterators yourself, but using zip is easier:
handle1 = GZip.open(file1.gz)
handle2 = GZip.open(file2.gz)
for (line1, line2) in zip(eachline(handle1), eachline(handle2))
println(line1,line2)
end
close(handle1)
close(handle2)
Don't forget to close your files!
Also, do note that if the two files have a different number of lines, the zip iterator will stop when the first of the two files runs out.

How to delete a row in a csv file with powershell in R?

Good morning,
I'm new about powershell and I'd like to ask you if somebody can help me.
I have a big csv file around 3.5gb and my goal is to load it with fread (a data.table function) in R environment, but this function makes a error.
> n_a<-fread("C:/x/xy/xyz/name_file.csv",sep=";", fill = TRUE)
The error is:
Warning message:
In fread("C:/x/xy/xyz/name_file.csv") :
Stopped early on line 458945. Expected 29 fields but found 30. Consider fill=TRUE and comment.char=. First discarded non-empty line
I tried to use different way (I putted in my code fill=true, but doesn't work) to solve the problem, but I couldn't do it.
After different researches I found this kind of solution (always to do in R):
>system("powershell Get-Content C:/a/b/c/file.csv | Select -Index (0..458944 + 1000000) > output.csv")
The focus about the use of powershell in R is to delete a specific row and to load with fread the file.
My question is:
How I can delete a specific row in a csv in powershell but without specifying the length of the matrix?
Thank you in advance for every type of help.
Francesco
I'd hazard a guess that the invalid row's location is not known. In such a case, it might be sensible to read the original file and create a new file that contains only valid data. What's more, if the source data would benefit of manipulation, it can be done before reading it into R.
A file as large as 3,5 GiB is a bit on the large side to read in memory as such. Sure, it can be done in the days of 64 bit systems, but for simple row processing it's unwieldy. A scalable solution uses .Net methods and row-by-row approach.
To process a file on row-by-row basis, use .Net methods for efficient row reading. A StringBuilder is created to store rows that contain valid data, others are discarded. The StringBuilder is flushed on disk every so often. Even on days of SSDs, a write operation for each row is relatively slow in respect to writing in a bulk of, say, 10 000 rows a time.
$sb = New-Object Text.StringBuilder
$reader = [IO.File]::OpenText("MyCsvFile.csv")
$i = 0
$MaxRows = 10000
$colonCount = 30
while($null -ne ($line = $reader.ReadLine())) {
# Split the line on semicolons
$elements = $line -split ';'
# If there were $colonCount elements, add those to builder
if($elements.count -eq $colonCount) {
# If $line's contents need modifications, do it here
# before adding it into the builder
[void]$sb.AppendLine($line)
++$i
}
# Write builder contents into file every now and then
if($i -ge $MaxRows) {
add-content "MyCleanCsvFile.csv" $sb.ToString()
[void]$sb.Clear()
$i = 0
}
}
# Flush the builder after the loop if there's data
if($sb.Length -gt 0) {
add-content "MyCleanCsvFile.csv" $sb.ToString()
}
This is easy done in powershell: Read csv in generic list, remove line and write back:
Add-Type -AssemblyName System.Collections
[System.Collections.Generic.List[string]]$csvList = #()
$csvFile = 'C:\test\myfile.csv'
$csvList = [System.IO.File]::ReadLines( $csvFile )
$lineToDelete = 2
[void]$csvList.RemoveAt( $lineToDelete - 1 )
[System.IO.File]::WriteAllLines( $csvFile, $csvList ) | Out-Null
vonPryz's helpful answer offers the best solution, given the size of your input file.
The following works too, but will be slow - in general, due to the overhead of using a pipeline, but also because Get-Content itself is slow due to decorating each line read with additional properties (see green-lighted, but not yet implemented GitHub suggestion #7537):
# Exclude line number 458945 (0-based index 458944)
Get-Content C:/a/b/c/file.csv | Select-Object -SkipIndex 458944 > output.csv
The beneficial flip side of use of the pipeline is that it acts as a memory throttle, so the above command can be used to process arbitrarily large files (though it may take a long time).

downloading data and saving data to a folder in batches

I have 200,000 links that I am trying to download, I have tried downloading it all in one go but I ran into memory issues.
I am trying to create a function which will download 1000 links at a time and save them in a folder.
Packages:
library(dplyr)
library(purrr)
library(edgarWebR)
A small sample of the data is as follows:
Data 1:
urls_to_parse <- c("https://www.sec.gov/Archives/edgar/data/1750/000104746918004978/a2236183z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746917004528/a2232622z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746916014299/a2228768z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746915006136/a2225345z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746914006243/a2220733z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746913007797/a2216052z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746912007300/a2210166z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746911006302/a2204709z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746910006500/a2199382z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746909006783/a2193700z10-k.htm"
)
I then apply the following function to download these 10 links
parsed_files <- map(urls_to_parse, possibly(parse_filing, otherwise = NA))
Which stores it as a nice list, I can then apply names(parsed_files) <- urls_to_parse to name the lists as the links from where they were downloading them from. I can also use output <- plyr::ldply(parsed_files, data.frame) to store everything in a nice data frame.
Using the below data, how could I create batches to download the data in say batches of 10?
What I have currently:
start = 1
end = 100
output <- NULL
output_fin <- NULL
for(i in start:end){
output[[i]] <- map(urls_to_parse[[i]], possibly(parse_filing, otherwise = NA))
names(output) <- urls_to_parse[start:end]
save(output_fin, file = paste0("C:/Users/Downloads/data/",i, "output.RData"))
}
I am sure there is a better way using a function, since this code breaks for some of the results.
More data: - 100 links
urls_to_parse <- c("https://www.sec.gov/Archives/edgar/data/1750/000104746918004978/a2236183z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746917004528/a2232622z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746916014299/a2228768z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746915006136/a2225345z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746914006243/a2220733z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746913007797/a2216052z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746912007300/a2210166z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746911006302/a2204709z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746910006500/a2199382z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746909006783/a2193700z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746908008126/a2186742z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465907055173/a07-18543_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465906047248/a06-15961_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465905033688/a05-12324_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746904023905/a2140220z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746903028005/a2116671z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000091205702033450/a2087919z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095012310108231/c61492e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095015208010514/n48172e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013707018659/c22309e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013707000193/c11187e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013406000594/c01109e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000120677405000032/d16006.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000120677404000013/d13773.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000104746903001075/a2097401z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000091205702001614/a2067550z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752308008030/a5800571.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752307009801/a5515869.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752306009238/a5227919.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046908000102/alpharmainc_10k.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046907000017/alo10k2006.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046906000027/alo10k2005.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046905000021/alo10k2004final.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046904000058/alo10k2003master.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046903000001/alo10k.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046902000004/alo10k2001.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046901500003/alo.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000000620118000009/a10k123117.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312517051216/d286458d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312516474605/d78287d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312515061145/d829913d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000000620114000004/aagaa10k-20131231.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620113000023/amr-10kx20121231.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000119312512063516/d259681d10k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095012311014726/d78201e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620110000006/ar123109.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620109000009/ar120810k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000451508000014/ar022010k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013407003888/d43815e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013406003715/d33303e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013405003726/d22731e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013404002668/d12953e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000104746903013301/a2108197z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095013407003823/h42902e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095012906002343/h31028e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095012905002955/h22337e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459018005085/cece-10k_20171231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459017004264/cece-10k_20161231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459016015157/cece-10k_20151231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312515095828/d864880d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312514098407/d661608d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312513109153/d444138d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312512119293/d293768d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312511067373/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312510069639/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312509055504/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312508058939/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312507071909/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312506068031/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312505077739/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312504052176/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000110465910047121/a10-16705_110k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000114420409046933/v159572_10k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000110465906060737/a06-19311_110k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746905022854/a2162888z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746904028585/a2143353z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746903031974/a2119476z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000143774918010388/avx20180331_10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916317000028/avx-20170331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916316000079/avx-20160331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916315000024/avx-20150331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916314000035/avx-20140331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916313000022/avx-20130331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916312000024/avxform10kfy12.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916311000013/avxform10kfy11.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916310000020/avxform10kfy10.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916309000117/form10kfy09.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916308000192/form10qq1fy09.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916308000101/form10kfy08.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916307000122/form10kfy07.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916306000102/avxfy06form10-k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916305000094/fy0510k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916304000091/fy0410k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916303000020/fy0310k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916302000007/r10k-0302.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462218000018/pnw2017123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462217000010/pnw2016123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462216000087/pnw2015123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462215000013/pnw12311410-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000110465914012068/a13-25897_110k.htm"
)
Looping over to do batch job as you showed is a bad idea. If you have a 1000s of files to be downloaded, how do you recover from errors?
The performance is not solely depend on your computer's configuration, but the network performance is crucial.
Here are couple of suggestions.
Option 1
partition all URLs in to batches to be able to download them parallelly. The number of files to be downloaded could be equal to number of cores in your computer. Look at this question; reading multiple files quickly in R
store these batches in a queue objects - For ex: using a package like https://cran.r-project.org/web/packages/dequer/dequer.pdf
pop the queue and use the batch of URLs in your parallel file download function.
Use a retryable file download function like in -- HTTP error 400 in R, error handling, How to retry instead of forcing to stop?
Once the queue is completed, move to the next partition.
wrap the whole operation in a retryable loop. For example; How to retry a statement on error?
Why do I use a queue? Because you could retry on error easily.
A pseudo code
file_url_partitions <- partion_as_batches(all_urls, batch_size)
attempts = 3
while( file_url_partitions is not empty && attempt <= 3 ) {
batch = file_url_partitions.pop()
tryCatch({
download_parallel(batch)
}, some_exception = function(se) {
file_url_partitions.push(batch)
attemp = attempt+1
})
}
Note: I don't have access to R studio/environment now hence no way to try.
Option 2
Download files separately using a download manager/similar and use downloaded files.
Some useful resources:
https://www.r-bloggers.com/r-with-parallel-computing-from-user-perspectives/
http://adv-r.had.co.nz/beyond-exception-handling.html

Fastest way to determine location of a file on a disk

I want to find a file foo.bar on a disk (in order to read that file). If I am using Windows, I can use:
list.files(path = "C:/", pattern = "^foo\\.bar$", recursive = TRUE, full.names = TRUE)
but this is quite slow (11 seconds on my machine) -- in contrast, Search Everything returns the result in less than 2 seconds. Is there a faster pure R way? If there are multiple matches, any of the matches will do. Cross-platform solutions are preferred.

Resources