Tool for viewing the difference between two CSV, TSV or JSON files. See Generating a commit log for San Francisco’s official list of trees (and the sf-tree-history repo commit log) for background information on this project.

pip install csv-diff 

Consider two CSV files:

one.csv

id,name,age 1,Cleo,4 2,Pancakes,2 

two.csv

id,name,age 1,Cleo,5 3,Bailey,1 

csv-diff can show a human-readable summary of differences between the files:

$ csv-diff one.csv two.csv --key=id 1 row changed, 1 row added, 1 row removed 1 row changed Row 1 age: "4" => "5" 1 row added id: 3 name: Bailey age: 1 1 row removed id: 2 name: Pancakes age: 2 

The --key=id option means that the id column should be treated as the unique key, to identify which records have changed.

The tool will automatically detect if your files are comma- or tab-separated. You can over-ride this automatic detection and force the tool to use a specific format using --format=tsv or --format=csv.

You can also feed it JSON files, provided they are a JSON array of objects where each object has the same keys. Use --format=json if your input files are JSON.

Use --show-unchanged to include full details of the unchanged values for rows with at least one change in the diff output:

% csv-diff one.csv two.csv --key=id --show-unchanged 1 row changed id: 1 age: "4" => "5" Unchanged: name: "Cleo" 

You can use the --json option to get a machine-readable difference:

$ csv-diff one.csv two.csv --key=id --json { "added": [ { "id": "3", "name": "Bailey", "age": "1" } ], "removed": [ { "id": "2", "name": "Pancakes", "age": "2" } ], "changed": [ { "key": "1", "changes": { "age": [ "4", "5" ] } } ], "columns_added": [], "columns_removed": [] } 

You can also import the Python library into your own code like so:

from csv_diff import load_csv, compare diff = compare( load_csv(open("one.csv"), key="id"), load_csv(open("two.csv"), key="id") ) 

diff will now contain the same data structure as the output in the --json example above.

If the columns in the CSV have changed, those added or removed columns will be ignored when calculating changes made to specific rows.