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Update_[FString_OSInfo]_[11_Aug_2025]
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‎README.md‎

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# python-feature-set
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# Python language feature set
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## Basic language features
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## Basic features
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- [Hello world](com/inbravo/core/HelloWorld.py)
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- [Hello world using Jupitor Notebook](com/inbravo/core/HelloWorld.ipynb)
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- [Data types](com/inbravo/core/DataTypeTest.py)
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- [Variable types](com/inbravo/core/VariableTest.py)
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- [Why intendation matters in Python](com/inbravo/core/IntendationTest.py)
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- [Main function](com/inbravo/core/MainFunctionTest.py)
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- [String handling](com/inbravo/core/StringHandling.py)
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- [Handling Tuples](com/inbravo/core/TupleTest.py)
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- [Handling Sets](com/inbravo/core/SetTest.py)
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## Data Structures
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- [Tuples](com/inbravo/core/TupleTest.py)
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- [Sets](com/inbravo/core/SetTest.py)
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## String handling
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- [Formatted Strings](com/inbravo/string/FString.py)
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## System
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- [Operating system information][com/inbravo/system/OSInfo.py]
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- [Operating system information](com/inbravo/system/OSInfo.py)
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## PySpark
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- [Calculate gross income of a super marker](com/inbravo/dbx/super-market/Gross_Income.ipynb)
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- [Calculate gross income of a super marker](com/inbravo/dbx/super-market/Gross_Income.ipynb)

‎com/inbravo/dbx/dbldatagen/Synthentic_Data_Test.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'jmespath'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyspark\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataFrame\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyspark\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SparkSession\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdbldatagen\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataGenerator\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Initialize Spark session\u001b[39;00m\n\u001b[32m 10\u001b[39m spark = SparkSession.builder \\\n\u001b[32m 11\u001b[39m .appName(\u001b[33m\"\u001b[39m\u001b[33mSynthetic Data Test\u001b[39m\u001b[33m\"\u001b[39m) \\\n\u001b[32m 12\u001b[39m .getOrCreate()\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/python-feature-set/.venv/lib/python3.13/site-packages/dbldatagen/__init__.py:26\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# Copyright (C) 2019 Databricks, Inc.\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 18\u001b[39m \u001b[33;03mThis module defines the package contents for the test data generator library\u001b[39;00m\n\u001b[32m 19\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 23\u001b[39m \u001b[33;03mMost of the other classes are used for internal purposes only\u001b[39;00m\n\u001b[32m 24\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata_generator\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataGenerator\n\u001b[32m 27\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatagen_constants\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_RANDOM_SEED, RANDOM_SEED_RANDOM, RANDOM_SEED_FIXED, \\\n\u001b[32m 28\u001b[39m RANDOM_SEED_HASH_FIELD_NAME, MIN_PYTHON_VERSION, MIN_SPARK_VERSION, \\\n\u001b[32m 29\u001b[39m INFER_DATATYPE, SPARK_DEFAULT_PARALLELISM\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ensure, topologicalSort, mkBoundsList, coalesce_values, \\\n\u001b[32m 31\u001b[39m deprecated, parse_time_interval, DataGenError, split_list_matching_condition, strip_margins, \\\n\u001b[32m 32\u001b[39m json_value_from_path, system_time_millis\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/python-feature-set/.venv/lib/python3.13/site-packages/dbldatagen/data_generator.py:15\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyspark\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LongType, IntegerType, StringType, StructType, StructField, DataType\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_version\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _get_spark_version\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcolumn_generation_spec\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ColumnGenerationSpec\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconstraints\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconstraint\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Constraint\n\u001b[32m 17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconstraints\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql_expr\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SqlExpr\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/python-feature-set/.venv/lib/python3.13/site-packages/dbldatagen/column_generation_spec.py:21\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyspark\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfunctions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mF\u001b[39;00m\n\u001b[32m 18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyspark\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FloatType, IntegerType, StringType, DoubleType, BooleanType, \\\n\u001b[32m 19\u001b[39m TimestampType, DataType, DateType, ArrayType, MapType, StructType\n\u001b[32m---> \u001b[39m\u001b[32m21\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcolumn_spec_options\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ColumnSpecOptions\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatagen_constants\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RANDOM_SEED_FIXED, RANDOM_SEED_HASH_FIELD_NAME, RANDOM_SEED_RANDOM, \\\n\u001b[32m 23\u001b[39m DEFAULT_SEED_COLUMN, OPTION_RANDOM, OPTION_RANDOM_SEED, OPTION_RANDOM_SEED_METHOD, INFER_DATATYPE\n\u001b[32m 25\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdaterange\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DateRange\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/python-feature-set/.venv/lib/python3.13/site-packages/dbldatagen/column_spec_options.py:13\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[33;03m.. title::Column Spec Options\u001b[39;00m\n\u001b[32m 7\u001b[39m \n\u001b[32m 8\u001b[39m \u001b[33;03mThis file defines the `ColumnSpecOptions` class\u001b[39;00m\n\u001b[32m 9\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcopy\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ensure\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mColumnSpecOptions\u001b[39;00m(\u001b[38;5;28mobject\u001b[39m):\n\u001b[32m 17\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\" Column spec options object - manages options for column specs.\u001b[39;00m\n\u001b[32m 18\u001b[39m \n\u001b[32m 19\u001b[39m \u001b[33;03m This class has limited functionality - mainly used to validate and document the options,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 140\u001b[39m \n\u001b[32m 141\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/GitHub/python-feature-set/.venv/lib/python3.13/site-packages/dbldatagen/utils.py:18\u001b[39m\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mwarnings\u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdatetime\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m timedelta\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjmespath\u001b[39;00m\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdeprecated\u001b[39m(message=\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 22\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 23\u001b[39m \u001b[33;03m Define a deprecated decorator without dependencies on 3rd party libraries\u001b[39;00m\n\u001b[32m 24\u001b[39m \n\u001b[32m 25\u001b[39m \u001b[33;03m Note there is a 3rd party library called `deprecated` that provides this feature but goal is to only have\u001b[39;00m\n\u001b[32m 26\u001b[39m \u001b[33;03m dependencies on packages already used in the Databricks runtime\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jmespath'"
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]
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}
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],
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"source": [
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"# This script generates synthetic data using the Databricks dbldatagen library\n",
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"# and performs basic analysis on the generated data.\n",
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"# Ensure you have the dbldatagen library installed in your Databricks environment.\n",
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"# Import necessary libraries\n",
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"from pyspark.sql import DataFrame\n",
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"from pyspark.sql import SparkSession\n",
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"from dbldatagen import DataGenerator\n",
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"\n",
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"# Initialize Spark session\n",
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"spark = SparkSession.builder \\\n",
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" .appName(\"Synthetic Data Test\") \\\n",
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" .getOrCreate()\n",
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"\n",
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"def generate_synthetic_data(rows=1000, partitions=4):\n",
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" \"\"\"\n",
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" Function to generate synthetic data using Databricks dbldatagen.\n",
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" \n",
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" Args:\n",
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" rows (int): Number of rows to generate.\n",
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" partitions (int): Number of partitions for the generated data.\n",
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" \n",
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" Returns:\n",
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" DataFrame: A Spark DataFrame containing the synthetic data.\n",
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" \"\"\"\n",
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" # Define the schema and data generation rules\n",
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" data_spec = (DataGenerator(spark, name=\"synthetic_data\", rows=rows, partitions=partitions)\n",
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" .withIdOutput()\n",
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" .withColumn(\"name\", \"string\", values=[\"Alice\", \"Bob\", \"Charlie\", \"David\"])\n",
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" .withColumn(\"age\", \"integer\", minValue=18, maxValue=60)\n",
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" .withColumn(\"salary\", \"float\", minValue=30000, maxValue=120000)\n",
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" .withColumn(\"department\", \"string\", values=[\"HR\", \"Engineering\", \"Marketing\", \"Sales\"])\n",
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" .withColumn(\"join_date\", \"date\", begin=\"2020-01-01\", end=\"2023-12-31\"))\n",
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"\n",
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" # Generate the synthetic data\n",
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" synthetic_data = data_spec.build()\n",
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" return synthetic_data\n",
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"\n",
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"def analyze_data(dataframe):\n",
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" \"\"\"\n",
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" Function to analyze the test data.\n",
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" \n",
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" Args:\n",
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" dataframe (DataFrame): A Spark DataFrame to analyze.\n",
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" \n",
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" Returns:\n",
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" None\n",
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" \"\"\"\n",
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" print(\"Schema of the DataFrame:\")\n",
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" dataframe.printSchema()\n",
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" \n",
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" print(\"\\nSample Data:\")\n",
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" dataframe.show(10, truncate=False)\n",
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" \n",
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" print(\"\\nSummary Statistics:\")\n",
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" dataframe.describe().show()\n",
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"\n",
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"# Example usage\n",
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"if __name__ == \"__main__\":\n",
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" # Generate synthetic data\n",
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" synthetic_df = generate_synthetic_data(rows=1000, partitions=4)\n",
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" \n",
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" # Analyze the generated data\n",
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" analyze_data(synthetic_df)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎com/inbravo/string/FString.py‎

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# Sample program to demonstrate the use of f-strings in Python
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def main():
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# Basic usage of f-strings
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name = "Alice"
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age = 30
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print(f"My name is {name} and I am {age} years old.")
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# Using expressions inside f-strings
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num1 = 10
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num2 = 20
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print(f"The sum of {num1} and {num2} is {num1 + num2}.")
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# Formatting numbers
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pi = 3.14159
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print(f"The value of pi rounded to 2 decimal places is {pi:.2f}.")
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# Using f-strings with dictionaries
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person = {"name": "Bob", "age": 25}
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print(f"{person['name']} is {person['age']} years old.")
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# Multiline f-strings
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greeting = f"""
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Hello, {name}!
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Welcome to the world of Python.
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"""
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print(greeting)
28+
29+
if __name__ == "__main__":
30+
main()

‎com/inbravo/system/OSInfo.py‎

Lines changed: 34 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,4 @@
1-
# Databricks notebook source
2-
# MAGIC %md
3-
# MAGIC This Program prints the Operating System details on the console
4-
5-
# COMMAND ----------
6-
7-
# Script Name : osinfo.py
1+
# Script Name : OSInfo.py
82
# Authors : {Amit Dixit after Forking the Git repo from 'geekcomputers'}
93
# Created : 10th August 2025
104
# Version : 1.0
@@ -15,9 +9,33 @@
159

1610
import platform as pl
1711

12+
# A utility class for terminal text formatting with ANSI escape sequences.
13+
# Provides color and style codes for text output.
14+
class BColors:
15+
"""Class for terminal text formatting with ANSI escape sequences."""
16+
HEADER = "\033[95m"
17+
OKBLUE = "\033[94m"
18+
OKGREEN = "\033[92m"
19+
WARNING = "\033[93m"
20+
FAIL = "\033[91m"
21+
ENDC = "\033[0m"
22+
BOLD = "\033[1m"
23+
UNDERLINE = "\033[4m"
24+
25+
@staticmethod
26+
def format_text(text, color):
27+
"""Formats the given text with the specified color."""
28+
return f"{color}{text}{BColors.ENDC}"
29+
30+
@staticmethod
31+
def bold_text(text):
32+
"""Formats the given text in bold."""
33+
return f"{BColors.BOLD}{text}{BColors.ENDC}"
34+
35+
# List of platform attributes to retrieve OS information
1836
profile = [
1937
"architecture",
20-
"linux_distribution",
38+
# "linux_distribution", # Removed in Python 3.8
2139
"mac_ver",
2240
"machine",
2341
"node",
@@ -32,18 +50,13 @@
3250
"version",
3351
]
3452

35-
36-
class bcolors:
37-
HEADER = "\033[95m"
38-
OKBLUE = "\033[94m"
39-
OKGREEN = "\033[92m"
40-
WARNING = "\033[93m"
41-
FAIL = "\033[91m"
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ENDC = "\033[0m"
43-
BOLD = "\033[1m"
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UNDERLINE = "\033[4m"
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46-
4753
for key in profile:
4854
if hasattr(pl, key):
49-
print(key + bcolors.BOLD + ": " + str(getattr(pl, key)()) + bcolors.ENDC)
55+
try:
56+
value = getattr(pl, key)()
57+
# Print the key in GREEN and the value in BLUE color
58+
print(f"{BColors.OKGREEN}{key}:{BColors.OKBLUE} {value}")
59+
except (AttributeError, TypeError) as e:
60+
print(
61+
f"{BColors.FAIL}{key}:{BColors.OKBLUE} Unable to retrieve information ({e})"
62+
)

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