df.to_csv("/path/to/destination.zip", compression="zip")
The above line will generate a file called destination.zip in the directory /path/to/.
Decompressing the ZIP file, will result in a directory structure path/to/destination.zip where destination.zip is the CSV file.
Why is the path/to/ folder structure included in the compressed file? Is there any way to avoid this?
Was blown away by this, currently writing the ZIP locally (destination.zip) and using os.rename to move it to the desired location.. Is this a bug ?
Related
I was building lately a dataset that I gather from the internet to use for training NN models. now I have a bunch of jpg images in one file and their labels on a txt file. the question is to which file format should I convert this data to make it easily callable in frameworks (python). a second question is how to build a metadata file about this dataset and which format should it have
In my opinion the easiest way is to build csv file to with two columns: directory and label. The directory value is the path (relative path) to the image, and label is of course the label. It requires you a merge from txt file and all jpg file into one csv files, but essentially it is easier to work with csv in pandas
I would like to use pandas.read_csv to open a gzip file (.asc.gz) within a zipped directory (.zip). Is there an easy way to do this?
This code doesn't work:
csv = pd.read_csv(r'C:\folder.zip\file.asc.gz') // can't find the file
This code does work (however, it requires me to unzip the folder, which I want to avoid because my dataset currently contains thousands of zipped folders):
csv = pd.read_csv(r'C:\folder\file.asc.gz')
Is there an easy way to do this? I have tried using a combination of zipfile.Zipfile and read_csv, but have been unsuccessful (I think partly due to the fact that this is an ascii file as well)
Maybe the followings might help.
df = pd.read_csv('filename.gz', compression='gzip')
OR
import gzip
file=gzip.open('filename.gz','rb')
content=file.read()
I have received a file from a customer. The file is said to be
SQL code (application/sql)
However, this has turned out to be wrong: nothing could open it. It turns out it was secretely a .zip file. By renaming it to '.zip' and manually extracting it I was able to get the files contained in it. I would like to do a similar process in python.
So far I've renamed the file:
file_name_zip = file_name.replace('.sql', '.zip')
os.rename(file_name, file_name_zip)
And I've tried extracting it:
zip_ref = zipfile.ZipFile(file_name_zip, 'r')
zip_ref.extractall(extracted_file)
However, this failed because
zipfile.BadZipFile: File is not a zip file
I've googled, and apparently this can sometimes be fixed using:
zip_file_name_2 = zip_file_name.replace('.zip', '2.zip')
os.system(f'zip -FF {zip_file_name} --out {zip_file_name_2}')
This required me to put in a bunch of settings, which I wasn't able to figure out. There must be a better way to go about this.
Does anybody know how to parse such an .sql file?
I'm trying to merge a large number of small files (200k+) and have come up with the following super-easy Pig code:
Files = LOAD 'hdfs/input/path' using PigStorage();
store Files into 'hdfs/output/path' using PigStorage();
Once Pig is done with the merging is there a way to remove the input files? I'd like to check that the file has been written and is not empty (i.e. 0 bytes). I can't simply remove everything in the input path because new files may have been inserted in the meantime, so that ideally I'd remove only the ones in the Files variable.
With Pig it is not possible i guess. Instead what you can do is use -tagsource with the LOAD statement and get the filename and stored it somewhere. Then use HDFS FileSystem API and read from the stored file to remove those files which are merged by pig.
A = LOAD '/path/' using PigStorage('delimiter','-tagsource');
You should be able to use hadoop commands in your Pig script
Move input files to a new folder
Merge input files to output folder
Remove input files from the new folder
distcp 'hdfs/input/path' 'hdfs/input/new_path'
Files = LOAD 'hdfs/input/new_path' using PigStorage();
STORE Files into 'hdfs/output/path' using PigStorage();
rmdir 'hdfs/input/new_path'
I have a csv file that is tared and zipped. So I have test.tar.gz.
I would like, through text file input, read csv file.
I try this tar:gz:file://C:/test/test.tar.gz!/test.tar! use wildcard like ".*\.csv".
But it sometime can't read success.
It throws Exception
org.apache.commons.vfs.FileNotFolderException:
Could not list the contents of
"tar:gz:file:///C:/test/test.tar.gz!/test.tar!/"
because it is not a folder.
I use windows8.1, pdi 5.2
Where it might be wrong?
For a compressed file csv reading, "Text File Input" step in Pentaho Kettle only supports the first files inside the compressed folder(either in Zip/GZip file). Check the Pentaho Wiki in the compression section.
Now for your issue, try removing the wildcard entry since only the first file inside the zip/gzip file will be read. (as explained above)
I have placed a sample code containing both reading zip and gzip files. Check it here.
Hope it helps :)