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How to concatenate many .psv files in google collaboratory?


python - What is the format of the WAV file for a Text to Speech Neural Network?Create top 10 index fund based on >100 stocksData Cleansing - Handling CSV filesCosine similarity between two folders (1 and 2) with documents, and find the most relevant set of documents (in folder 2) for each doc (in folder 2)Tensorflow CNN sometimes converges, sometimes notMapping column values of one DataFrame to another DataFrame using a key with different header namesHow can I merge 2+ DataFrame objects without duplicating column names?Split unprocessed dataset into train and test setsMerging dataframes in Pandas is taking a surprisingly long timeIs shuffling training data beneficial for machine learning?













1












$begingroup$


I have a folder named 'training' in my local drive which has 20000 .psv files. I zipped it and uploaded to google collaboratory, with the upload option in the Files
section. I unzipped it with the following command.



!unzip training


Now I have a folder called training in Files. Each file contains 40 columns which are same for all the files and rows of different lengths.I wish to merge all the files. The resulting file should contain all the rows of all the files with 40 columns(ignore the index columnas to avoid duplicate index). The header should have column names since they are common to all the files.
The merged file should be converted to a single data frame.
Thanks in advance.










share|improve this question









$endgroup$
















    1












    $begingroup$


    I have a folder named 'training' in my local drive which has 20000 .psv files. I zipped it and uploaded to google collaboratory, with the upload option in the Files
    section. I unzipped it with the following command.



    !unzip training


    Now I have a folder called training in Files. Each file contains 40 columns which are same for all the files and rows of different lengths.I wish to merge all the files. The resulting file should contain all the rows of all the files with 40 columns(ignore the index columnas to avoid duplicate index). The header should have column names since they are common to all the files.
    The merged file should be converted to a single data frame.
    Thanks in advance.










    share|improve this question









    $endgroup$














      1












      1








      1





      $begingroup$


      I have a folder named 'training' in my local drive which has 20000 .psv files. I zipped it and uploaded to google collaboratory, with the upload option in the Files
      section. I unzipped it with the following command.



      !unzip training


      Now I have a folder called training in Files. Each file contains 40 columns which are same for all the files and rows of different lengths.I wish to merge all the files. The resulting file should contain all the rows of all the files with 40 columns(ignore the index columnas to avoid duplicate index). The header should have column names since they are common to all the files.
      The merged file should be converted to a single data frame.
      Thanks in advance.










      share|improve this question









      $endgroup$




      I have a folder named 'training' in my local drive which has 20000 .psv files. I zipped it and uploaded to google collaboratory, with the upload option in the Files
      section. I unzipped it with the following command.



      !unzip training


      Now I have a folder called training in Files. Each file contains 40 columns which are same for all the files and rows of different lengths.I wish to merge all the files. The resulting file should contain all the rows of all the files with 40 columns(ignore the index columnas to avoid duplicate index). The header should have column names since they are common to all the files.
      The merged file should be converted to a single data frame.
      Thanks in advance.







      machine-learning python deep-learning pandas data-cleaning






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Apr 6 at 13:15









      MalathiMalathi

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