OmbroCSV

pip install ombrocsv

A fast random CSV generator with a powerful yet simple template system that can be used either as a standalone Command line tool or easily integrated into any codebase.

Schema

The schema is basically the "template" of the CSV you are generating, it's a list of dictionaries (each dict represents a column). Here's how it should look:

[
    {
            "type": {see types for valid types}
            "header": {header name (optional)}
    },
    {
            "type": {see types for valid types}
            "header": {header name (optional)}
    },
    ...
]

the order in which the columns appear in the schema will be the order in which they will appear in the generated CSV.

Types

Here are the supported types with a brief description:

• "bit" - random integer value between 1 and 0.
• "phone_prefix" random 4-digit integer value.
• "phone_number" random 10-digit number.
• "phone_complete" random 14-digit number.
• "string" - completely random string.
• "name" - random string that loosely resembles a name.
• "email" - random email
• "number" - random integer number
• "id" - incremental id that starts from 0
• "date" - random date

Note: Any non recognized type will be treated as a string

Custom Types

you can easily override the default types by passing a different RandUtils in the constructor of CsvGenerator, just make sure that your custom RandUtils inherits from the base one and that every new type you add inherits from RandomBase.

Both RandUtils and RandomBase can be found in random_utils.py

To use your custom types then simply write the name it has been registered with in your custom RandUtils class in the "type" field when passing the schema.

Example

adding CustomType to code:

Class CustomType(RandomBase):
    ...


Class CustomRandUtils(RandUtils):

    def __init__():
        super().__init__()
        self.custom_type = CustomType()

Using CustomType in the Schema:

[
    {
            "type": "custom_type"
    },
    ...
]

Usage

This section details how to use the Package in both it's intended use cases.

As a Command Line Tool

Create a config file and use this package as a convenient way to generate CSV files on the fly.

Generate file and output to terminal

ombrocsv [config file] [number of lines to generate]

Generate file and output to file

ombrocsv [config file] [number of lines to generate] [output file]

Config file

{
    "schema": [
        {
            "type": {see types for valid types}
            "header": {header name (optional)}
        },
        ...
    ],
    "separator": {separator character (optional, default = ;)},
    "delimiter": {delimiter character (optional, default = ")},
    "headers": {true/false (optional, default = true)},
    "delimit all": {true/false (optional, default = false)}
}

Example

{
  "schema": [{
      "type": "email",
      "header": "Email"
    }, {
      "type": "name",
      "header": "Name"
    }, {
      "type": "name",
      "header": "Surname"
    }, {
      "type": "date",
      "header": "DateOfBirth"
    }, {
      "type": "CustomerID",
      "header": "string"
    }
  ],
  "separator": "|",
  "delimiter": "'"
}

As a Package

Just import CsvGenerator, initialize it with the required parameters and generate your CSV files.

Example

schema = [
    {
        "type": "email",
        "header": "Email"
    },
    {
        "type": "name",
        "header": "Name"
    },
    {
        "type": "name",
        "header": "Surname"
    },
    {
        "type": "date",
        "header": "DateOfBirth"
    },
    {
        "type": "CustomerID",
        "header": "string"
    }
]
generator = CsvGenerator(";", "\"", True, schema)
generator.generate(10)
generator.write(sys.stdout.buffer)