Command-line fake data generator
Project description
Faker CLI
Faker is an awesome Python library, but I often just want a simple command I can run to generate data in a variety of formats.
With Faker CLI, you can easily generate CSV, JSON, or Parquet data with fields of your choosing.
You can also utilize pre-built templates for common data formats!
Usage
By default, fake
will generate a CSV output for you. You just specify the number of rows you want and the column types.
fake -n 10 pyint,user_name,date_this_year
BAM! You've got a CSV file with your data.
pyint,user_name,date_this_year
8649,fward,2023-03-08
3933,zharris,2023-03-20
1469,jasonellis,2023-05-16
3660,heather91,2023-02-10
9160,cameronlopez,2023-05-05
2735,candacemoore,2023-05-12
7240,zachary06,2023-01-23
9778,thomasstacey,2023-05-23
5820,kenneth36,2023-04-26
2856,michael23,2023-01-16
JSON
Wnat a JSON file? Sweet, use -f json
.
fake -n 10 pyint,user_name,date_this_year -f json
{"pyint": 3854, "user_name": "cchavez", "date_this_year": "2023-01-20"}
{"pyint": 2008, "user_name": "vnguyen", "date_this_year": "2023-04-03"}
{"pyint": 1434, "user_name": "karen38", "date_this_year": "2023-03-02"}
{"pyint": 4922, "user_name": "duncanellen", "date_this_year": "2023-04-22"}
{"pyint": 230, "user_name": "tiffany72", "date_this_year": "2023-02-25"}
{"pyint": 7252, "user_name": "maydouglas", "date_this_year": "2023-04-01"}
{"pyint": 2716, "user_name": "sheilaflores", "date_this_year": "2023-03-20"}
{"pyint": 2827, "user_name": "parksandra", "date_this_year": "2023-04-01"}
{"pyint": 3353, "user_name": "melissaatkinson", "date_this_year": "2023-02-10"}
{"pyint": 5306, "user_name": "mark12", "date_this_year": "2023-04-16"}
Default column names aren't good enough for you? Fine, use your own.
fake -n 10 pyint,user_name,date_this_year -f json -c id,awesome_name,last_attention_at
{"id": 6048, "awesome_name": "jtran", "last_attention_at": "2023-04-24"}
{"id": 4310, "awesome_name": "stacey99", "last_attention_at": "2023-04-27"}
{"id": 1839, "awesome_name": "jho", "last_attention_at": "2023-03-07"}
{"id": 236, "awesome_name": "melissamassey", "last_attention_at": "2023-04-17"}
{"id": 6599, "awesome_name": "mwells", "last_attention_at": "2023-04-25"}
{"id": 6071, "awesome_name": "wilcoxrick", "last_attention_at": "2023-01-17"}
{"id": 9646, "awesome_name": "michael92", "last_attention_at": "2023-04-22"}
{"id": 6986, "awesome_name": "ballen", "last_attention_at": "2023-01-08"}
{"id": 6892, "awesome_name": "jennifer61", "last_attention_at": "2023-01-03"}
{"id": 1967, "awesome_name": "jmendoza", "last_attention_at": "2023-01-23"}
Parquet
OK, it had to happen, you can even write Parquet.
fake -n 10 pyint,user_name,date_this_year -f parquet -o sample.parquet
youcanevenwritestraighttos3 🤭
fake -n 10 pyint,user_name,date_this_year -f parquet -o s3://YOUR_BUCKET/data/sample.parquet
Delta Lake
Data can be exported as a delta lake table.
fake -n 10 pyint,user_name,date_this_year -f deltalake -o sample_data
Templates
Want to generate 1 MILLION S3 Access logs in ~2 minutes? Now you can.
fake -t s3access -n 10
How about CloudFront? Go ahead.
fake -t cloudfront -n 10
Warning: Both of these templates are still being validated - please be cautious!
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