2024-07-15 17:11:39.721584 new snippets

seeker/report.txt

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+--------------------------------------------------------------------------------
+ 2024-07-15 17:11:39.721584
+--------------------------------------------------------------------------------
+  On branch main
+Your branch is up to date with 'origin/main'.
+
+Changes not staged for commit:
+  (use "git add/rm <file>..." to update what will be committed)
+  (use "git restore <file>..." to discard changes in working directory)
+	deleted:    snippet/A29.java
+	deleted:    snippet/CalcSLM.java
+	deleted:    snippet/GPA.py
+	deleted:    snippet/bukuserver.sh
+	deleted:    snippet/buttsnatcher.sh
+	deleted:    snippet/copy_files
+	deleted:    snippet/devbox
+	deleted:    snippet/enc.py
+	deleted:    snippet/example_mssql.py
+	deleted:    snippet/fetch_chrome_bypass.py
+	deleted:    snippet/gistfile1.txt
+	deleted:    snippet/main.py
+	deleted:    snippet/netip.Prefix_to_net.IPNet.go
+	deleted:    snippet/pres.py
+	deleted:    snippet/record_camera_stream.sh
+	deleted:    snippet/s3.sh
+	deleted:    snippet/shell
+	deleted:    snippet/spark-local.py
+	deleted:    snippet/timer.py
+	deleted:    snippet/us_inflation.py
+	deleted:    snippet/us_unemployment.py
+	deleted:    snippet/vxlan_hp.py
+
+Untracked files:
+  (use "git add <file>..." to include in what will be committed)
+	snippet/Dockerfile
+	snippet/copilot-generate-inventory-data-python-excel.py
+	snippet/copilot-python-excel-customer-churn-dataset.py
+	snippet/copilot-python-fake-scores.py
+	snippet/create-socket-server.sh
+	snippet/deep_neural_network.py
+	snippet/python-script-100-fake-customers-excel-SEED.py
+	snippet/test.py
+
+no changes added to commit (use "git add" and/or "git commit -a")
+
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  2024-07-12 17:12:24.016161
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seeker/snippet/Dockerfile

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+#date: 2024-07-15T16:41:00Z
+#url: https://api.github.com/gists/a45166a36c2eb652bd7c02e2e5bd4a7b
+#owner: https://api.github.com/users/itzzjb
+
+# We will start from a apline version of node.js base image
+FROM node:lts-alpine
+
+# Create a directory to hold the application code inside the image
+WORKDIR /usr/src/app
+
+# Copy package.json and package-lock.json to the image
+COPY ./package*.json /usr/src/app
+
+# Installing all the node_modules according to the package.json file
+RUN npm install
+
+# Copy the rest of the application code to the image
+COPY ./ /usr/src/app
+
+# Expose the port that the app runs on
+EXPOSE 3000
+
+# Start the application
+CMD ["npm","start"] 

seeker/snippet/copilot-generate-inventory-data-python-excel.py

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+#date: 2024-07-15T16:58:40Z
+#url: https://api.github.com/gists/56abe14f64be817384c90b579f147268
+#owner: https://api.github.com/users/summerofgeorge
+
+import pandas as pd
+import numpy as np
+from faker import Faker
+
+# Set the random seed
+np.random.seed(1234)
+
+# Initialize the faker generator
+fake = Faker()
+
+# Generate inventory data
+inventory_data = {
+    'Item ID': [fake.unique.random_number(digits=6) for _ in range(2000)],
+    'Category': [fake.random_element(['Electronics', 'Clothing', 'Home Goods', 'Sports Equipment', 'Toys']) for _ in range(2000)],
+    'Stock Level': np.random.normal(loc=100, scale=30, size=2000),
+    'Reorder Level': np.random.uniform(low=20, high=50, size=2000),
+    'Lead Time': np.random.exponential(scale=1/0.05, size=2000)
+}
+
+# Create a DataFrame
+df = pd.DataFrame(inventory_data)
+
+# Save to an Excel file
+df.to_excel('warehouse_inventory.xlsx', index=False)
+print("Excel workbook 'warehouse_inventory.xlsx' created successfully!")

seeker/snippet/copilot-python-excel-customer-churn-dataset.py

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+#date: 2024-07-15T17:02:17Z
+#url: https://api.github.com/gists/6218180cf42c9e2297493947befc7031
+#owner: https://api.github.com/users/summerofgeorge
+
+import pandas as pd
+import numpy as np
+from faker import Faker
+
+# Set the random seed
+np.random.seed(1234)
+
+# Initialize the faker generator
+fake = Faker()
+
+# Generate customer churn data
+customer_data = {
+    'Customer ID': [fake.unique.random_number(digits=6) for _ in range(5000)],
+    'Age': np.random.normal(loc=35, scale=10, size=5000),
+    'Tenure': np.random.uniform(low=1, high=72, size=5000),
+    'Monthly Charges': np.random.normal(loc=70, scale=20, size=5000),
+}
+
+# Calculate Total Charges
+customer_data['Total Charges'] = customer_data['Monthly Charges'] * customer_data['Tenure']
+
+# Generate churn (0 or 1) based on probability
+customer_data['Churn'] = np.random.choice([0, 1], size=5000, p=[0.8, 0.2])
+
+# Create a DataFrame
+df = pd.DataFrame(customer_data)
+
+# Save to an Excel file
+df.to_excel('telecom_customer_churn.xlsx', index=False)
+print("Excel workbook 'telecom_customer_churn.xlsx' created successfully!")

seeker/snippet/copilot-python-fake-scores.py

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+#date: 2024-07-15T16:55:28Z
+#url: https://api.github.com/gists/48fff68259a7c7dd0e406cbad5d86e37
+#owner: https://api.github.com/users/summerofgeorge
+
+# Use Python and the faker package to create a synthetic Excel dataset for an organization's employee performance review for 500 employees with the following details:
+
+# - Columns: Employee ID, Department, Performance Score, Salary, Years of Experience
+# - Performance Score: Normally distributed with a mean of 70 and a standard deviation of 10
+# - Salary: Log-normally distributed with a mean of $50,000 and a standard deviation of $15,000
+# - Years of Experience: Exponentially distributed with a lambda of 0.1
+# - Department: Randomly chosen from 'Sales', 'HR', 'IT', 'Marketing', 'Finance
+# - Random seed: Set to 1234.
+
+import pandas as pd
+import numpy as np
+from faker import Faker
+
+# Set the random seed
+np.random.seed(1234)
+
+# Initialize the faker generator
+fake = Faker()
+
+# Generate employee data
+employee_data = {
+    'Employee ID': [fake.unique.random_number(digits=6) for _ in range(500)],
+    'Department': [fake.random_element(['Sales', 'HR', 'IT', 'Marketing', 'Finance']) for _ in range(500)],
+    'Performance Score': np.random.normal(loc=70, scale=10, size=500),
+    'Salary': np.random.lognormal(mean=np.log(50000), sigma=np.log(15000), size=500),
+    'Years of Experience': np.random.exponential(scale=1/0.1, size=500)
+}
+
+# Create a DataFrame
+df = pd.DataFrame(employee_data)
+
+# Save to an Excel file
+df.to_excel('employee_performance.xlsx', index=False)
+print("Excel workbook 'employee_performance.xlsx' created successfully!")