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Скачать или смотреть How to Use awk to Filter Rows Based on Multiple Conditions in a CSV File

  • vlogize
  • 2025-10-10
  • 0
How to Use awk to Filter Rows Based on Multiple Conditions in a CSV File
awk filter on numeric column but only when other string column equals some valueunixawk
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Описание к видео How to Use awk to Filter Rows Based on Multiple Conditions in a CSV File

A detailed guide on how to filter rows in a CSV file using `awk`, specifically focusing on combining conditions for numeric and string columns.
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This video is based on the question https://stackoverflow.com/q/68410816/ asked by the user 'curious' ( https://stackoverflow.com/u/12350966/ ) and on the answer https://stackoverflow.com/a/68410959/ provided by the user 'anubhava' ( https://stackoverflow.com/u/548225/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: awk filter on numeric column but only when other string column equals some value

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The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
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Filtering Rows in CSV Files with awk: A Comprehensive Guide

When working with data from CSV files in Unix, you often encounter the need to filter rows based on specific conditions. This can involve numeric columns being compared to certain values, while also taking string columns into account. One common scenario involves wanting to exclude rows based on multiple criteria, such as a specific value in one column along with a range of numerical values in another. In this post, we'll explore how to effectively use awk to achieve this.

The Problem Statement

Suppose you have the following dataset in a file, where each line consists of two columns: a string identifier and a numeric value.

[[See Video to Reveal this Text or Code Snippet]]

You want to filter this dataset to exclude any rows where the first column (col1) is "chr6" and the second column (col2) is between 25000000 and 33500000. Your desired output would look like this:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

To solve this problem using awk, we need to construct a command that effectively filters the rows based on the specified conditions. Here's how we can do this.

Using Negation and Logical Operators

Instead of directly trying to match and filter out the unwanted rows, we can cleverly reverse the conditions using logical operators. Here’s the awk command that accomplishes this:

[[See Video to Reveal this Text or Code Snippet]]

Breaking it Down:

$1 != "chr6": This condition allows any row where the first column is not equal to "chr6" to pass through.

|| (Logical OR): This operator combines multiple conditions. If any of the conditions connected by || are true, the row will be included in the output.

$2 <= 25000000: This allows rows where the numeric value in the second column is less than or equal to 25000000.

$2 >= 33500000: This permits rows where the numeric value is greater than or equal to 33500000.

Running the Command

When you run the above awk command in your terminal with the data file, you will get the expected filtered output:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Filtering data efficiently is crucial when working with large datasets in Unix. Mastering tools like awk can greatly enhance your ability to manipulate and process data. By leveraging logical operators and reversing the conditions, you can create flexible filters that meet your specific needs.

In this post, we explored how to exclude unwanted rows based on multiple conditions in a straightforward way. Try out this method and see how it streamlines your data filtering process with awk!

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