faster-than-csv | Faster CSV for Python | CSV Processing library

 by   juancarlospaco Python Version: 21.05.10 License: MIT

kandi X-RAY | faster-than-csv Summary

kandi X-RAY | faster-than-csv Summary

faster-than-csv is a Python library typically used in Utilities, CSV Processing applications. faster-than-csv has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install faster-than-csv' or download it from GitHub, PyPI.

Faster CSV for Python
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            kandi-support Support

              faster-than-csv has a low active ecosystem.
              It has 63 star(s) with 5 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 11 have been closed. On average issues are closed in 15 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of faster-than-csv is 21.05.10

            kandi-Quality Quality

              faster-than-csv has 0 bugs and 0 code smells.

            kandi-Security Security

              faster-than-csv has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              faster-than-csv code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              faster-than-csv is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              faster-than-csv releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              faster-than-csv saves you 4812 person hours of effort in developing the same functionality from scratch.
              It has 10147 lines of code, 2 functions and 6 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed faster-than-csv and discovered the below as its top functions. This is intended to give you an instant insight into faster-than-csv implemented functionality, and help decide if they suit your requirements.
            • Run all tests
            • Run a test
            • Plot the results of the benchmark
            Get all kandi verified functions for this library.

            faster-than-csv Key Features

            No Key Features are available at this moment for faster-than-csv.

            faster-than-csv Examples and Code Snippets

            Use
            Pythondot img1Lines of Code : 11dot img1License : Permissive (MIT)
            copy iconCopy
            import faster_than_csv as csv
            
            csv.csv2list("example.csv")                     # See Docs for more info.
                                                            # Custom Separators supported.
            csv.csv2json("example.csv", indentation=4)      # CSV to JSON, Pre  
            Docker
            Pythondot img2Lines of Code : 3dot img2License : Permissive (MIT)
            copy iconCopy
            $ ./build-docker.sh
            $ ./run-docker.sh
            $ ./run-benchmark.sh  # Inside Docker.
              

            Community Discussions

            QUESTION

            Peformance issues reading CSV files in a Java (Spring Boot) application
            Asked 2022-Jan-29 at 12:37

            I am currently working on a spring based API which has to transform csv data and to expose them as json. it has to read big CSV files which will contain more than 500 columns and 2.5 millions lines each. I am not guaranteed to have the same header between files (each file can have a completly different header than another), so I have no way to create a dedicated class which would provide mapping with the CSV headers. Currently the api controller is calling a csv service which reads the CSV data using a BufferReader.

            The code works fine on my local machine but it is very slow : it takes about 20 seconds to process 450 columns and 40 000 lines. To improve speed processing, I tried to implement multithreading with Callable(s) but I am not familiar with that kind of concept, so the implementation might be wrong.

            Other than that the api is running out of heap memory when running on the server, I know that a solution would be to enhance the amount of available memory but I suspect that the replace() and split() operations on strings made in the Callable(s) are responsible for consuming a large amout of heap memory.

            So I actually have several questions :

            #1. How could I improve the speed of the CSV reading ?

            #2. Is the multithread implementation with Callable correct ?

            #3. How could I reduce the amount of heap memory used in the process ?

            #4. Do you know of a different approach to split at comas and replace the double quotes in each CSV line ? Would StringBuilder be of any healp here ? What about StringTokenizer ?

            Here below the CSV method

            ...

            ANSWER

            Answered 2022-Jan-29 at 02:56

            I don't think that splitting this work onto multiple threads is going to provide much improvement, and may in fact make the problem worse by consuming even more memory. The main problem is using too much heap memory, and the performance problem is likely to be due to excessive garbage collection when the remaining available heap is very small (but it's best to measure and profile to determine the exact cause of performance problems).

            The memory consumption would be less from the replace and split operations, and more from the fact that the entire contents of the file need to be read into memory in this approach. Each line may not consume much memory, but multiplied by millions of lines, it all adds up.

            If you have enough memory available on the machine to assign a heap size large enough to hold the entire contents, that will be the simplest solution, as it won't require changing the code.

            Otherwise, the best way to deal with large amounts of data in a bounded amount of memory is to use a streaming approach. This means that each line of the file is processed and then passed directly to the output, without collecting all of the lines in memory in between. This will require changing the method signature to use a return type other than List. Assuming you are using Java 8 or later, the Stream API can be very helpful. You could rewrite the method like this:

            Source https://stackoverflow.com/questions/70900587

            QUESTION

            Inserting json column in Bigquery
            Asked 2021-Jun-02 at 06:55

            I have a JSON that I want to insert into BQ. The column data type is STRING. Here is the sample JSON value.

            ...

            ANSWER

            Answered 2021-Jun-02 at 06:55

            I think there is an issue with how you escape the double quotes. I could reproduce the issue you describe, and fixed it by escaping the double quotes with " instead of a backslash \:

            Source https://stackoverflow.com/questions/67799161

            QUESTION

            Avoid repeated checks in loop
            Asked 2021-Apr-23 at 11:51

            I'm sorry if this has been asked before. It probably has, but I just have not been able to find it. On with the question:

            I often have loops which are initialized with certain conditions that affect or (de)activate certain behaviors inside them, but do not drastically change the general loop logic. These conditions do not change through the loop's operation, but have to be checked every iteration anyways. Is there a way to optimized said loop in a pythonic way to avoid doing the same check every single time? I understand this would be a compiler's job in any compiled language, but there ain't no compiler here.

            Now, for a specific example, imagine I have a function that parses a CSV file with a format somewhat like this, where you do not know in advance the columns that will be present on it:

            ...

            ANSWER

            Answered 2021-Apr-23 at 11:36

            Your code seems right to me, performance-wise.

            You are doing your checks at the beginning of the loop:

            Source https://stackoverflow.com/questions/67228959

            QUESTION

            golang syscall, locked to thread
            Asked 2021-Apr-21 at 15:29

            I am attempting to create an program to scrape xml files. I'm experimenting with go because of it's goroutines. I have several thousand files, so some type of multiprocessing is almost a necessity...

            I got a program to successfully run, and convert xml to csv(as a test, not quite the end result), on a test set of files, but when run with the full set of files, it gives this:

            ...

            ANSWER

            Answered 2021-Apr-21 at 15:25

            I apologize for not including the correct error. as the comments pointed out i was doing something dumb and creating a routine for every file. Thanks to JimB for correcting me, and torek for providing a solution and this link. https://gobyexample.com/worker-pools

            Source https://stackoverflow.com/questions/67182393

            QUESTION

            How to break up a string into a vector fast?
            Asked 2020-Jul-31 at 21:54

            I am processing CSV and using the following code to process a single line.

            play with code

            ...

            ANSWER

            Answered 2020-Jul-31 at 21:54

            The fastest way to do something is to not do it at all.

            If you can ensure that your source string s will outlive the use of the returned vector, you could replace your std::vector with std::vector which would point to the beginning of each substring. You then replace your identified delimiters with zeroes.

            [EDIT] I have not moved up to C++17, so no string_view for me :)

            NOTE: typical CSV is different from what you imply; it doesn't use escape for the comma, but surrounds entries with comma in it with double quotes. But I assume you know your data.

            Implementation:

            Source https://stackoverflow.com/questions/63197165

            QUESTION

            CSV Regex skipping first comma
            Asked 2020-May-11 at 22:02

            I am using regex for CSV processing where data can be in Quotes, or no quotes. But if there is just a comma at the starting column, it skips it.

            Here is the regex I am using: (?:,"|^")(""|[\w\W]*?)(?=",|"$)|(?:,(?!")|^(?!"))([^,]*?|)(?=$|,)

            Now the example data I am using is: ,"data",moredata,"Data" Which should have 4 matches ["","data","moredata","Data"], but it always skips the first comma. It is fine if there is quotes on the first column, or it is not blank, but if it is empty with no quotes, it ignores it.

            Here is a sample code I am using for testing purposes, it is written in Dart:

            ...

            ANSWER

            Answered 2020-May-11 at 22:02

            Investigating your expression

            Source https://stackoverflow.com/questions/61584722

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install faster-than-csv

            pip install faster_than_csv

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            Install
          • PyPI

            pip install faster-than-csv

          • CLONE
          • HTTPS

            https://github.com/juancarlospaco/faster-than-csv.git

          • CLI

            gh repo clone juancarlospaco/faster-than-csv

          • sshUrl

            git@github.com:juancarlospaco/faster-than-csv.git

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