How to view differences between CSV files using 'csv-diff' (with examples)

The csv-diff command is a powerful tool for identifying and displaying differences between CSV, TSV, or JSON files. This utility is incredibly useful for data analysts, developers, or anyone working with data files who needs to track changes or updates between data sets. With csv-diff, you can generate human-readable or JSON-formatted reports of these differences, using specific columns as unique identifiers to pinpoint the precise changes.

Use case 1: Display a human-readable summary of differences between files using a specific column as a unique identifier

Code:

csv-diff path/to/file1.csv path/to/file2.csv --key column_name

Motivation: In many scenarios, comparing two versions of data sets can be crucial for understanding updates, changes, or errors. This use case allows users to quickly spot deviations between two CSV files. By defining a unique column identifier, you can ensure that each row is matched correctly between the files, providing a reliable comparison of the datasets.

Explanation:

• csv-diff is the command used to invoke the tool.
• path/to/file1.csv and path/to/file2.csv specify the paths to the two files you wish to compare.
• --key column_name sets a specific column as the unique identifier for each row. This is crucial because it helps csv-diff understand which rows in each file correspond to each other, allowing it to accurately report differences.

Example Output:

1 row changed, 2 rows added, 1 row removed

Changes:
  [Unique_Key]: Field [column1] updated from [value1] to [value2]
Additions:
  [Unique_Key]: [column1=value3, column2=value4]
Removals:
  [Unique_Key]: [column1=value5, column2=value6]

Use case 2: Display a human-readable summary of differences between files that includes unchanged values in rows with at least one change

Code:

csv-diff path/to/file1.csv path/to/file2.csv --key column_name --show-unchanged

Motivation: When tracking changes, especially for data verification or debugging, it’s often useful to see not only what has changed but also what has remained the same in the context of those changes. This helps in understanding the full scope of modifications in rows where at least one value has changed, offering a more comprehensive view of the differences.

Explanation:

• csv-diff starts the command for comparing files.
• path/to/file1.csv and path/to/file2.csv are the file paths you’re comparing.
• --key column_name sets the unique identifier column for mapping rows between files.
• --show-unchanged is an option that ensures all values in a row are shown when reporting changes, giving context by including values that did not change.

Example Output:

1 row changed with full details:

[Unique_Key]: 
  column1: [unchanged_value]
  column2: [value_old] -> [value_new]
  column3: [unchanged_value]

Use case 3: Display a summary of differences between files in JSON format using a specific column as a unique identifier

Code:

csv-diff path/to/file1.csv path/to/file2.csv --key column_name --json

Motivation: Incorporating JSON-formatted output is beneficial for integration with various data processing workflows, APIs, and automated systems. JSON provides a structured and standardized format that is easily parsed by computers, making it ideal for developers and automated scripts that need to process difference reports programmatically.

Explanation:

• csv-diff is the command invoked to compare the files.
• path/to/file1.csv and path/to/file2.csv denote the locations of the files being compared.
• --key column_name identifies the unique column used for matching rows in the two files.
• --json specifies that the output should be formatted in JSON, suitable for machine readability and further processing in programs that handle JSON data.

Example Output:

{
  "changed": {
    "Unique_Key": {
      "column2": ["value_old", "value_new"]
    }
  },
  "added": {
    "Unique_Key": {
      "column1": "value3",
      "column2": "value4"
    }
  },
  "removed": {
    "Unique_Key": {
      "column1": "value5",
      "column2": "value6"
    }
  }
}

Conclusion:

Through these examples, csv-diff proves invaluable in various data processing contexts, allowing users to identify changes, track updates, and maintain data integrity with ease. Whether used for generating human-readable summaries or JSON reports for programmatic use, this command-line tool offers flexibility and functionality essential for modern data analysis and management tasks.