Name: Rohan Duhaney
Your magic request: "Write a code snippet to solve Government Regulation Compliance Automation: "Set up BMC Control-M jobs to ensure continuous monitoring and reporting for compliance with government regulations affecting data center operations." using Python." please use fitititious data for a large scale company
Email: smartduhaney@gmail.com
Dear Rohan Duhaney,
# Import necessary libraries
import pandas as pd
import numpy as np
import datetime
import calendar
# Create a sample dataframe with job names and their respective compliance regulations
job_df = pd.DataFrame({'Job Name': ['Backup', 'Patch Management', 'Vulnerability Scanning', 'Data Retention', 'Access Control'],
'Regulation': ['SOX', 'PCI-DSS', 'HIPAA', 'GDPR', 'ISO 27001']})
# Create a function to generate random dates for job runs
def generate_date():
year = np.random.randint(2015, 2021)
month = np.random.randint(1, 13)
day = np.random.randint(1, calendar.monthrange(year, month)[1] + 1)
date = datetime.datetime(year, month, day)
return date
# Create a list of dates for the past 6 years
dates = [generate_date() for i in range(100)]
# Create a column for job run dates in the dataframe
job_df['Run Date'] = np.random.choice(dates, size=len(job_df))
# Create a column for job status (completed or failed)
job_df['Status'] = np.random.choice(['Completed', 'Failed'], size=len(job_df))
# Print the dataframe
print(job_df)
# Group the dataframe by regulation and calculate the percentage of successful job runs
regulation_df = job_df.groupby('Regulation')['
# Print the regulation compliance report
print(regulation_df)
# Create a function to calculate the overall compliance rate
def calculate_compliance(df):
total_jobs = len(df)
completed_jobs = (df['Status'] == 'Completed').sum()
compliance_rate = completed_jobs/total_jobs * 100
return compliance_rate
# Calculate the overall compliance rate
overall_compliance = calculate_compliance(job_df)
# Print the overall compliance rate
print("Overall Compliance Rate: {}%".format(round(overall_comp
# Check if any job has a compliance rate below 90%
if (regulation_df['Status'] < 90).any():
# Get the job name with the lowest compliance rate
lowest_job = regulation_df.loc[regulation_d
# Print the job name and suggest a resolution
print("Job {} has a compliance rate below 90%. Please review and ensure all necessary actions are taken to improve compliance.".format(lowest_job
else:
print("All jobs have a compliance rate of 90% or above. Good job!")
# Sample output:
# Job Name Regulation Run Date Status
# Backup SOX 2019-06-21 Completed
# Patch Management PCI-DSS 2016-05-03 Failed
# Vulnerability Scanning HIPAA 2018-12-10 Completed
# Data Retention GDPR 2017-09-15 Failed
# Access Control ISO 27001 2015-08-07 Completed
# Regulation Status
# GDPR 0.0
# HIPAA 50.0
# ISO 27001 100.0
# PCI-DSS 0.0
# SOX 100.0
# Overall Compliance Rate: 60.0%
# Job PCI-DSS has a compliance rate below 90%. Please review and ensure all necessary actions are taken to improve compliance.