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    "info": {
        "author": "Alex Broley",
        "author_email": "alex.broley@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# Datamaker\n\nRapidly build tabular datasets in the following formats:\n- SQL Tables (Generate code for SQL CREATE and INSERT queries)\n- CSV \n- JSON\n\nSupports: PostgreSQL, MySQL, MS SQL, SQLite\n\n## Getting Started\nDatamaker uses core python 3 and has been extensively tested on python 3.6+. \n\n### Prerequisites\nPython 3.6+\n\n### Installing\n```pip install datamaker```\n\n### Build a dataset\nIn this example we build a SQL table with 50 rows and two columns: id, name. The create and \ninsert queries will be written to the file example.sql.\n```\nfrom datamaker.SQLTableGenerator import SQLTableGen\n\n\ntable_name = \"Example\"\nnum_rows = 50\ncolumn_defs = {\n    \"id\": {\"col_type\": \"int\", \"is_primary_key\": True},\n    \"name\": {\"col_type\": \"varchar\", \"is_name\": True}\n}\noutput_path = \"example.sql\"\n\ngen = SQLTableGen(table_name, num_rows, column_defs, output_path)\n\ngen.build_sql_doc()\n```\n\n## Documentation\n### Input parameters\n- **table_name**  - This is the name of the table to be created\n- **num_rows** - The number of rows to insert into the table\n- **column_defs** - A dictionary object with key value pairs.\n- **path** - The path to the directory and file name where you want to export the sql\nqueries or dataset for the specified format.\n\n**column_defs** is a dictionary object of the form:\n```\n{column_name: {\"col_type\": type, ...}}\n```\nNote that col_type is always specified for each column. The definitions of the parameters for **colum_defs** is given \nbelow.\n\n- col_type: int, float, varchar, bool\n- varchar_length: int (required for MySQL and MS SQL)\n- is_primary_key: bool - True if this field is the primary key\n- is_name: bool - True if values for this column should come from the names collection\n- is_email: bool - True if this column is an email. Note that you must have at least one column with is_name = True \nto use is_email.\n- is_date: bool - True if this column is a date. Default format is YYYY-mm-dd\n- is_timestamp: bool - True if this column is a timestamp. Default format is YYYY-mm-dd HH:MM:SS\n- is_country: bool - True of this column is a country code. Codes are ISO Alpha-2.\n- is_state: bool - True if this column is a US state abbreviation. \n- is_address: bool - True if this column is a street address.\n- bounds: tuple(lowerbnd, upperbnd) - Forces lowerbnd <= column values <= upperbnd\n- fixed_collection: tuple(elem1, elem2, ..., elemn) - Forces column values to be elements of this tuple\n- fixed_map: tuple(column_name, {key: value}) - Forces values for this column to be associated to the value of another \ncolumn\n- is_nullable: bool - True if this column can have null values\n- proportion_null: float - The approximate proportion of expected null values in this column. Note that is_nullable \nmust be true to use proportion_null\n\nNote that using is_primary_key assumes the column you're defining is an integer column starting at 1 and incrementing\nby 1 for each row.\n\nIf you're planning on running queries on a MySQL or MS SQL Server machine then you\nneed to specify the length of each varchar field using the varchar_length parameter.\n\n## CSV and JSON generators\nDatamaker can also be used to generate CSV and JSON files.\n\nTo create a CSV file add the following import statement to your script.\n\n```from datamaker.CSVGenerator import CSVGen```\n\nAnd to create a JSON file add the following import to your script.\n\n```from datamaker.JSONGenerator import JSONGen```\n\n\nThe input parameters are no different for CSVGen and JSONGen are the same as given above. If we want to create the \n**Example** table given in the example code above as a CSV or JSON document we only to change the import statement and \ninstantiate the CSVGen or JSONGen object using the same column_defs dictionary. Then call the appropriate build method\nfor the given class. The CSVGen class uses ```build_csv_doc``` and the JSONGen class uses ```build_json_doc```.\n\n```\nfrom datamaker.CSVGenerator import CSVGen\n\n\ntable_name = \"Example\"\nnum_rows = 50\ncolumn_defs = {\n    \"id\": {\"col_type\": \"int\", \"is_primary_key\": True},\n    \"name\": {\"col_type\": \"varchar\", \"is_name\": True}\n}\noutput_path = \"example.csv\"\n\ngen = CSVGen(table_name, num_rows, column_defs, output_path)\n\ngen.build_csv_doc()\n```\n\nHere's the same **Example** table built as a JSON document.\n\n```\nfrom datamaker.JSONGenerator import JSONGen\n\n\ntable_name = \"Example\"\nnum_rows = 50\ncolumn_defs = {\n    \"id\": {\"col_type\": \"int\", \"is_primary_key\": True},\n    \"name\": {\"col_type\": \"varchar\", \"is_name\": True}\n}\noutput_path = \"example.json\"\n\ngen = JSONGen(table_name, num_rows, column_defs, output_path)\n\ngen.build_json_doc()\n```\n\n## A more complex example\nIn this example we're generating SQL code to create a MySQL table which tracks orders placed for an imaginary online \nstore. We'll create a table with the following columns:\n- id - the primary key for this table\n- name - the name associated to the user who placed this order\n- order_ts - the timestamp associated with this order\n- product_id - the id for the product ordered\n- price - the cost of the product being ordered. Since we want every row with a given product_id to have the same cost we \nuse a fixed map to implement this column \n\nNote that in this example we're defining the output path as a valid directory instead of a path to a file. In this \ncase datamaker will automatically generate the name of the file as: \n\n**table_name**.**current_date**.sql\n\nWhere **table_name** is the name of the table and **current_date** is today's date.\n\n```\nfrom datamaker.SQLTableGenerator import SQLTableGen\n\n\ntable_name = \"Orders\"\nnum_rows = 500\ncolumn_defs = {\n    \"id\": {\"col_type\": \"int\", \"is_primary_key\": True},\n    \"name\": {\"col_type\": \"varchar\", \"varchar_length\": 50, \"is_name\": True},\n    \"order_ts\": {\"col_type\": \"varchar\", \"varchar_length\": 26, \"is_timestamp\": True},\n    \"product_id\": {\"col_type\": \"int\", \"bounds\": (1, 5)},\n    \"price\": {\"col_type\": \"float\", \"fixed_map\": (\"product_id\", {1: 5.99, 2: 10.99, 3: 0.99, 4: 99.99, 5: 20})},\n}\noutput_path = \".\"\n\ngen = SQLTableGen(table_name, num_rows, column_defs, output_path)\n\ngen.build_sql_doc()\n```\n\n### Future improvements:\n- Add support for sequential fields\n- Add support for column type inference\n- Add support for tsv generation\n\n\n",
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