Jaygithubtraining

.github/workflows/test-notebook.yml

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+name: Test Notebook Code
+# Trigger the workflow on push events to all branches
+on:
+push:
+
+branches:
+- '*'
+jobs:
+test:
+runs-on: ubuntu-latest
+steps:
+- name: Set PYTHONPATH
+run: echo "PYTHONPATH=$PYTHONPATH:$(pwd)" >> $GITHUB_ENV
+# Step 1: Checkout the repository
+- name: Checkout code
+uses: actions/checkout@v3
+# Step 2: Set up Python environment
+- name: Set up Python
+uses: actions/setup-python@v4
+with:
+python-version: 3.9
+# Step 3: Install dependencies
+- name: Install dependencies
+run: |
+pip install -r requirements.txt
+pip install pytest
+# Step 4: Run tests
+- name: Run tests
+run: |
+pytest tests/

Mixed_Language_Demo.py

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+# Databricks notebook source
+# MAGIC %md
+# MAGIC # Simple Databricks Notebook with Python, R, SQL, and Markdown
+# MAGIC This notebook demonstrates a combination of Python, R, SQL, and Markdown cells
+# MAGIC in Databricks. It performs basic operations, suitable for a low-cost AWS
+# MAGIC Databricks environment.
+# MAGIC ### Steps:
+# MAGIC 1. Perform basic Python operations.
+# MAGIC 2. Use R for basic calculations.
+# MAGIC 3. Execute SQL queries on an in-memory table.
+# MAGIC 4. Add comments and explanations using Markdown.
+
+# COMMAND ----------
+
+# Python: Create a sample data frame and display it
+import pandas as pd
+# Sample data
+data = {"Name": ["Alice", "Bob", "Charlie"], "Age": [25, 30, 35], "Salary":
+[50000, 60000, 70000]}
+df = pd.DataFrame(data)
+# Convert to Spark DataFrame
+spark_df = spark.createDataFrame(df)
+# Display the Spark DataFrame
+display(spark_df)
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ### Python Code Explanation:
+# MAGIC - Created a Pandas DataFrame with sample data (Name, Age, Salary).
+# MAGIC - Converted it into a Spark DataFrame for use in Databricks.
+# MAGIC
+# MAGIC - Displayed the Spark DataFrame using the `display` function.
+
+# COMMAND ----------
+
+# MAGIC %r
+# MAGIC R: Perform basic operations
+# MAGIC # Load a small vector of numbers and calculate their mean
+# MAGIC numbers <- c(10, 20, 30, 40, 50)
+# MAGIC mean_value <- mean(numbers)
+# MAGIC # Print the mean value
+# MAGIC print(paste("The mean of the numbers is:", mean_value))
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ### R Code Explanation:
+# MAGIC - Created a numeric vector `numbers` with five values.
+# MAGIC - Calculated the mean of the numbers using the `mean` function.
+# MAGIC - Printed the result to the output.
+
+# COMMAND ----------
+
+# MAGIC %sql
+# MAGIC
+# MAGIC -- SQL: Create and query a temporary table
+# MAGIC CREATE OR REPLACE TEMP VIEW employee AS
+# MAGIC SELECT * FROM VALUES
+# MAGIC ('Alice', 25, 50000),
+# MAGIC ('Bob', 30, 60000),
+# MAGIC ('Charlie', 35, 70000)
+# MAGIC AS employees(Name, Age, Salary);
+# MAGIC -- Query the table
+# MAGIC SELECT Name, Salary FROM employee WHERE Salary > 55000;
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ### SQL Code Explanation:
+# MAGIC - Created a temporary table `employee` using the `CREATE OR REPLACE TEMP VIEW`
+# MAGIC command.
+# MAGIC - Inserted sample data for employees (Name, Age, Salary).
+# MAGIC - Queried the table to select employees with a salary greater than 55,000.
+
+# COMMAND ----------
+
+# Python: Stop all active sessions to minimize costs
+# This ensures you don't leave any expensive resources running
+spark.catalog.clearCache()

deplaoyment.yaml

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+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  creationTimestamp: null
+  labels:
+    app: vote
+    tier: front
+  name: vote
+spec:
+  replicas: 2
+  selector:

requirements.txt

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+pandas
+pytest

service.yaml

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+apiVersion: v1
+kind: Service
+metadata:
+  creationTimestamp: null
+  labels:
+    app: vote
+    tier: front
+  name: vote
+spec:
+  ports:
+  - name: "80"
+    nodePort: 30000
+    port: 80
+    protocol: TCP
+    targetPort: 80
+  selector:
+    app: vote
+  type: NodePort
+status:
+  loadBalancer: {}

tests/test_notebook.py

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+import pytest
+import Mixed_Language_Demo as databrics_code # Import the module
+def test_dataframe_creation():
+"""
+Test for the `create_dataframe` function
+"""
+# Create the DataFrame
+df = databrics_code.create_dataframe()
+# Verify the DataFrame structure
+assert df is not None
+assert len(df) == 3 # Three rows
+assert "Name" in df.columns
+
+assert list(df["Name"]) == ["Amar", "Akbar", "Anthony"]
+def test_dataframe_summary():
+"""
+Test the `print_dataframe_summary` function
+"""
+# Create the DataFrame
+df = databrics_code.create_dataframe()
+# Ensure no exceptions are raised during summary printing
+try:
+databrics_code.print_dataframe_summary(df)
+except Exception as e:
+pytest.fail(f"Summary printing failed with error: {e}")