fexcel 0.0.1

Add your description here

Fexcel

A simple mock excel generator. fexcel reads a JSON file declaring the excel schema to use and creates a new excel file with said configuration.

Content

• Installation
• Plugins
• Usage
  • CLI
  • API
• Schema
  • Text Fields
  • Numeric Fields
  • Choice Fields
  • Temporal fields
  • Boolean fields
  • Network fields

Installation

Install fexcel with pip

pip install fexcel

check the installation was successful

fexcel --help

Plugins

fexcel relies on faker to generate quality mock data and pyexcel for excel file handling.

As pyexcel has a plugin structure, fexcel has a plugin structure too, and to work with each different kind of file a dedicated pyexcel plugin should be installed.

As a commodity, several extras can be installed alongside fexcel to work with the most usual file type (based on pyexcel own plugin table). The following table lists them:

extra	file types
csv	.csv, .tsv, .csvz and .tsvz
xls	.xls
xlsx	.xlsx
ods	.ods
all	All of the above

Therefore, to handle .xlsx files you would install fexcel as

pip install 'fexcel[xlsx]'

Usage

CLI

You can use the command line to create a fake excel based on an existing schema declaration

fexcel /path/to/input/schema.json /path/to/output/file.xlsx --num-fakes 100

API

You can leverage fexcel's main interface Fexcel to parse a schema and write the resulting excel in a file as such

from fexcel import Fexcel

fexcel = Fexcel.from_file("schema.json")
fexcel.write_to_file("output.xlsx")

Schema

Fexcel expects to have its schema defined in a JSON file with the following structure

[
  {
    "name": "FIRST_FIELD_NAME",
    "type": "FIRST_FIELD_TYPE",
    "constraints": {
      "constraint1": "CONSTRAINT_VALUE",
      "constraint2": "CONSTRAINT_VALUE"
    }
  },
  {
    "name": "SECOND_FIELD_NAME",
    "type": "SECOND_FIELD_TYPE",
    "constraints": {
      "constraint1": "CONSTRAINT_VALUE"
    }
  }
]

• The name attribute defines how the final excel field will be named.
• The type attribute specifies the data type of the field.
• The constraints attribute specifies a set of key-value pairs to customize how the data will be generated.

The name and type attributes are required and the constraints attribute is always optional and has a default implementation.

The possible constraints for each type are listed below.

Text Fields

The supported text fields are

type	description
Name	An ordinary randomly generated name
Email	A randomly generated e-mail
Phone	A phone address
Address	An address of a person or institution
UUID	A Universally Unique Identifier
Location	A locale string (e.g. en_EN)

Currently this fields admit no constraints

Numeric Fields

The supported numeric fields are

type	description
float	A random decimal value following some distribution
int / integer	A random integer value following some distribution

The possible constraints are

constraint	description	values
distribution	Statistic distribution of the data	uniform (default), normal, or lognormal
min_value	Lower bound for the field values, can only be specified with the uniform distribution	numeric value, defaults to -1e30
max_value	Upper bound for the field values, can only be specified with the uniform distribution	numeric value, defaults to 1e30
mean	Mean value for normal or lognormal distributions	numeric value, defaults to 0
std	Standard deviation for normal or lognormal distributions	numeric value, defaults to 1

Choice Fields

The supported choice fields are

type	description
choice	Choose between an array of possible values

The possible constraints are

constraint	description	values
allowed_values	Array of values to choose from when filling the data	Array of string values, defaults to ["NULL"]
probabilities	Array of float values defining the probability of each allowed value. Each probability corresponds to the probability of the value in that position, if there are values left unspecified the remaining probability will be equidistributed amongst the remaining values	Array of floating values between 0 and 1. Must sum up to 1 (or less if some probabilities are left unspecified). Defaults to an empty array.

Temporal fields

The supported temporal fields are

type	description
date	A day of the year in the Gregorian Calendar, without time part
time	A time part of a date consisting only on hours, minutes, seconds and microseconds
datetime	A date with time part of the Gregorian Calendar

The possible constraints are

constraint	description	values
format_string	The format string to which the values will be displayed	A valid datetime format string, defaults to "%Y-%m-%d %H-%M-%S" for datetime fields and "%Y-%m-%d" for date fields
start_date	A date to which all values of the field will precede	A date represented in the format_string representation or in ISO 8601, defaults to 1970-01-01 00:00:00
end_date	A date to which all values of the field will be prior to	A date represented in the format_string representation or in ISO 8601, defaults to the current execution time

Boolean fields

The supported boolean fields are

type	description
bool / boolean	A boolean True or False value

The possible constraints are

constraint	description	values
probability	The probability for the field to be True	A number between 0 and 1, defaults to 0.5

Network fields

The supported network fields are

type	description
url	HTTP and HTTPS random valid URLs
IPv4	A random IPv4 address or network with a valid CIDR
IPv6	A random IPv6 address or network with a valid CIDR