When you have a big CSV file, typically you have following ways to process it:
1, Use a text editor or command-line tools like grep, sed, and awk to search, filter, and manipulate the data in the CSV file.
2, Use a programming language like Python or R to process the CSV file using libraries like Pandas or data.table. These libraries can handle large CSV files efficiently and offer a wide range of data manipulation and analysis functions.
3, Use a database management system like MySQL or PostgreSQL to import the CSV file into a database table. You can then query and analyze the data using SQL.
4, Split the CSV file into smaller chunks, process each chunk separately, and then combine the results. You can use command-line tools like split, or programming languages like Python, to split the file.
5, Use cloud-based services like Amazon S3 or Google Cloud Storage to store and process the CSV file. These services provide scalable storage and computing resources that can handle large datasets.
6, Optimize the CSV file by removing unnecessary columns, compressing the data, or converting it to a more efficient file format like Parquet or Arrow.
You also can clean and split with an online tool for free. The tool is: https://www.quicktable.io/apps.
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