Top 10 us ai gov guide GPTs

Introduction

The journey through the world of us ai gov guide is an adventure in learning and discovery. In us ai gov guide, we find a field that is rich in history and ripe with future potential. The ever-evolving nature of us ai gov guide makes it a constantly fascinating area of study.

StepWiz 🔗

AI assisted guide for USMLE Step 1 using the best prep materials.

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Welcome! I’m StepWiz, ready to assist with your USMLE Step 1 studies.

Prompt Starters

• Can you explain renal physiology?
• How does the immune response work?
• What are the side effects of NSAIDs?
• Tell me about the pathology of osteoarthritis.

US Feasibility Finder 🔗

AI for ARF USA Panel Feasibility Checks 📊🔬

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Hi! Ready to explore survey data with whole-number insights? 📈🧮

Prompt Starters

• Age Gender (BROAD) Feasibility 📊 🔍 👬📈 🧮 📉 💻 import pandas as pd # Load the dataset data = pd.read_csv(‘/mnt/data/Angus Reid Forum USA.csv’) # Preparing the data data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) # Calculate feasibility feasibility = (data.groupby([‘P_Region_FIP’, ‘Age x Gender Broad’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)) # Reordering columns columns_order = [‘Female 18-34’, ‘Female 35-54’, ‘Female 55 Plus’, ‘Male 18-34’, ‘Male 35-54’, ‘Male 55 Plus’] feasibility = feasibility[columns_order] # Adding row totals feasibility[‘Total’] = feasibility.sum(axis=1) # Reset index to display the regions feasibility.reset_index(inplace=True) # Adding column totals column_totals = feasibility.sum(numeric_only=True) column_totals[‘P_Region_FIP’] = ‘Total’ feasibility = feasibility.append(column_totals, ignore_index=True) # Display the feasibility table with row and column totals print(feasibility)
• Age Gender (GENERATION) Feasibility 👶🧒👧👵👴 - import pandas as pd # Load the dataset data = pd.read_csv(‘/mnt/data/Angus Reid Forum USA.csv’) # Preparing the data with ‘Generation’ data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) data[‘Gender x Generation’] = data[‘Gender’] + ” ” + data[‘Generation’] # Calculate feasibility using combined ‘Gender x Generation’ category feasibility_gen = (data.groupby([‘P_Region_FIP’, ‘Gender x Generation’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)) # Sorting columns to have females on the left and males on the right columns_sorted_gen = sorted(feasibility_gen.columns, key=lambda x: (x.split()[0], x.split()[1])) feasibility_gen = feasibility_gen[columns_sorted_gen] # Adding row totals feasibility_gen[‘Total’] = feasibility_gen.sum(axis=1) # Reset index to display the regions feasibility_gen.reset_index(inplace=True) # Adding column totals column_totals_gen = feasibility_gen.sum(numeric_only=True) column_totals_gen[‘P_Region_FIP’] = ‘Total’ feasibility_gen = feasibility_gen.append(column_totals_gen, ignore_index=True) # Display the feasibility table with row and column totals print(feasibility_gen)
• Age Gender (FINE) Feasibility 📊 🔍 👬📈 🧮 📉 💻 - import pandas as pd # Load the dataset data = pd.read_csv(‘/mnt/data/Angus Reid Forum USA.csv’) # Preparing the data data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) data[‘Gender x Age Fine’] = data[‘Gender’] + ” ” + data[‘Age Fine’] # Calculate feasibility using combined ‘Gender x Age Fine’ category feasibility = (data.groupby([‘P_Region_FIP’, ‘Gender x Age Fine’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)) # Sorting columns to have females on the left and males on the right columns_sorted = sorted(feasibility.columns, key=lambda x: (x.split()[0], x.split()[1])) feasibility = feasibility[columns_sorted] # Adding row totals feasibility[‘Total’] = feasibility.sum(axis=1) # Reset index to display the regions feasibility.reset_index(inplace=True) # Adding column totals column_totals = feasibility.sum(numeric_only=True) column_totals[‘P_Region_FIP’] = ‘Total’ feasibility = feasibility.append(column_totals, ignore_index=True) # Save or display the feasibility table with row and column totals print(feasibility)
• Employment Status Feasibility 💼📈🤝🏢💲📉 💻 - import pandas as pd def calculate_feasibility(csv_file_path, region_col, employment_col, response_rate_col, employment_order): # Load the dataset data = pd.read_csv(csv_file_path) # Convert the response rate to numeric, handling any non-numeric values data[response_rate_col] = pd.to_numeric(data[response_rate_col], errors=‘coerce’) # Calculate feasibility feasibility = (data.groupby([region_col, employment_col])[response_rate_col] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)[employment_order]) # Adding row totals feasibility[‘Total’] = feasibility.sum(axis=1) # Reset index to display the regions feasibility.reset_index(inplace=True) # Adding column totals column_totals = feasibility.sum(numeric_only=True) column_totals[region_col] = ‘Total’ feasibility = feasibility.append(column_totals, ignore_index=True) return feasibility # Parameters for the function csv_file_path = ‘/mnt/data/Angus Reid Forum USA.csv’ # Path to your dataset region_col = ‘P_Region_FIP’ # Column name for region employment_col = “First, we would like to ask a few questions about your main job. Are you…?” # Employment status column response_rate_col = ‘Response Rate’ # Column name for response rate employment_order = [ ‘A full-time employee’, ‘A part-time or contract employee’, ‘A business owner’, ‘Self-employed’, ‘Retired’, ‘Unemployed but looking for work’, ‘Unemployed and not looking for work’, ‘Other’ # Exclude any category not present in your data ] # Calculate and display the feasibility table feasibility_table = calculate_feasibility(csv_file_path, region_col, employment_col, response_rate_col, employment_order) print(feasibility_table)
• VizMin Feasibility 👩🏿‍🦳👨🏾‍🦱👳👩🏻🧑🏽🧕 - import pandas as pd # Load the dataset data = pd.read_csv(‘/mnt/data/Angus Reid Forum USA.csv’) # Preparing the data data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) data[‘Viz Min’].fillna(‘Not Specified’, inplace=True) # Calculate feasibility with “Viz Min” nested within gender feasibility_viz_min = (data.groupby([‘P_Region_FIP’, ‘Gender’, ‘Viz Min’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(level=[1, 2], fill_value=0)) # Preparing the columns order based on gender and Viz Min viz_min_categories = data[‘Viz Min’].unique() columns_order_viz_min = [(gender, viz_min) for gender in [‘Female’, ‘Male’] for viz_min in viz_min_categories] feasibility_viz_min = feasibility_viz_min.reindex(columns=columns_order_viz_min, fill_value=0) # Adding row totals feasibility_viz_min[‘Total’] = feasibility_viz_min.sum(axis=1) # Reset index to display the regions feasibility_viz_min.reset_index(inplace=True) # Adding column totals column_totals_viz_min = feasibility_viz_min.sum(numeric_only=True) column_totals_viz_min[‘P_Region_FIP’] = ‘Total’ feasibility_viz_min = feasibility_viz_min.append(column_totals_viz_min, ignore_index=True) # Display the feasibility table with Viz Min nested within gender print(feasibility_viz_min)
• Household Income Feasibility - 😄 🤑 💰 💎 - import pandas as pd # Load the dataset data = pd.read_csv(‘/mnt/data/Angus Reid Forum USA.csv’) # Preparing the data data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) income_column = “Which of the following ranges best describes your total annual household income before taxes?” # Calculate feasibility for household income income_feasibility = (data.groupby([‘P_Region_FIP’, income_column])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)) # Sorting columns in ascending order of income ranges income_feasibility = income_feasibility.sort_index(axis=1) # Adding row totals income_feasibility[‘Total’] = income_feasibility.sum(axis=1) # Reset index to display the regions income_feasibility.reset_index(inplace=True) # Adding column totals income_column_totals = income_feasibility.sum(numeric_only=True) income_column_totals[‘P_Region_FIP’] = ‘Total’ income_feasibility = income_feasibility.append(income_column_totals, ignore_index=True) # Display the feasibility table with row and column totals print(income_feasibility)
• Education Feasibility -🎓 📚 📖 ✏️ 📝 import pandas as pd # Load the dataset data = pd.read_csv(‘/mnt/data/Angus Reid Forum USA.csv’) # Preparing the data data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) # Calculate feasibility for Education Broad category feasibility_education = (data.groupby([‘P_Region_FIP’, ‘Education Broad’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)) # Adding row totals feasibility_education[‘Total’] = feasibility_education.sum(axis=1) # Reset index to display the regions feasibility_education.reset_index(inplace=True) # Adding column totals column_totals_education = feasibility_education.sum(numeric_only=True) column_totals_education[‘P_Region_FIP’] = ‘Total’ feasibility_education = feasibility_education.append(column_totals_education, ignore_index=True) # Display the feasibility table with Education Broad category print(feasibility_education)
• Age Gender (BROAD) + Education Feasibility 👬🎓 📚 📖 ✏️ 📝-import pandas as pd # Load the dataset data = pd.readcsv(‘/mnt/data/Angus Reid Forum USA.csv’) # Original file path # Prepare the data by converting ‘Response Rate’ to numeric, handling non-numeric values data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) # Calculate feasibility for Age x Gender Broad category feasibility_age_gender = ( data.groupby([‘P_Region_FIP’, ‘Age x Gender Broad’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0) ) # Reordering columns for Age x Gender Broad columns_order_age_gender = [ ‘Female 18-34’, ‘Female 35-54’, ‘Female 55 Plus’, ‘Male 18-34’, ‘Male 35-54’, ‘Male 55 Plus’ ] feasibility_age_gender = feasibility_age_gender[columns_order_age_gender] # Rename columns for clarity in the merged table feasibility_age_gender.columns = [f’AGB{col}’ for col in feasibilityage_gender.columns] # Calculate feasibility for Education Broad category feasibility_education = ( data.groupby([‘P_Region_FIP’, ‘Education Broad’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0) ) # Rename columns for clarity in the merged table feasibility_education.columns = [ f’EDU{col}’ if col != ‘P_Region_FIP’ else col for col in feasibility_education.columns ] # Correctly merging the two tables on ‘P_Region_FIP’ merged_feasibility = pd.merge(feasibility_age_gender.reset_index(), feasibility_education.reset_index(), on=‘P_Region_FIP’) # Swapping the order of the columns “College” with “High School or Less” columns_reordered = [‘P_Region_FIP’, ‘AGB_Female 18-34’, ‘AGB_Female 35-54’, ‘AGB_Female 55 Plus’, ‘AGB_Male 18-34’, ‘AGB_Male 35-54’, ‘AGB_Male 55 Plus’, ‘EDU_High school or less’, ‘EDU_College’, ‘EDU_University Plus’] merged_feasibility_reordered = merged_feasibility[columns_reordered] # Calculating column totals (excluding the region column) for the reordered table column_totals_reordered = merged_feasibility_reordered.iloc[:, 1:].sum().rename(‘Total’) merged_feasibility_with_totals_reordered = merged_feasibility_reordered.append(column_totals_reordered, ignore_index=True) # Setting the ‘P_Region_FIP’ for the totals row merged_feasibility_with_totals_reordered.at[len(merged_feasibility_with_totals_reordered) - 1, ‘P_Region_FIP’] = ‘Total’ # Display the table with reordered columns print(merged_feasibility_with_totals_reordered)
• Race Feasibility - 👩🏿👲👳🧕👨‍🦱 👩‍🦰 - import pandas as pd # Load the dataset file_path = ‘/mnt/data/Angus Reid Forum USA.csv’ # Specific file path and name data = pd.read_csv(file_path) # Preparing the data data[‘Response Rate’] = pd.to_numeric(data[‘Response Rate’], errors=‘coerce’) # Calculate feasibility based on Race race_feasibility = (data.groupby([‘P_Region_FIP’, ‘Race’])[‘Response Rate’] .apply(lambda x: int((x.count() * x.mean()))) .unstack(fill_value=0)) # Sort the columns by their total frequency in descending order and adjust the order race_totals = race_feasibility.sum().sort_values(ascending=False) sorted_columns = race_totals.index.tolist() # Move ‘Two or more races’ and ‘Some other race’ to the end sorted_columns.remove(‘Two or more races’) sorted_columns.remove(‘Some other race’) sorted_columns += [‘Two or more races’, ‘Some other race’] # Move ‘Other Asian’ after ‘Vietnamese’ other_asian_index = sorted_columns.index(‘Other Asian [e.g. Cambodian, Indonesian]’) vietnamese_index = sorted_columns.index(‘Vietnamese’) sorted_columns.insert(vietnamese_index + 1, sorted_columns.pop(other_asian_index)) # Reorder the feasibility table according to the adjusted column order race_feasibility = race_feasibility[sorted_columns] # Adding row totals race_feasibility[‘Total’] = race_feasibility.sum(axis=1) # Reset index to display the regions race_feasibility.reset_index(inplace=True) # Display the race feasibility table with row and column totals print(race_feasibility)

GovAssist 🔗

AI Assistant for Local and Regional Governments

Welcome Message

Welcome to GovAssist! How can I assist with your specific government role today?

Prompt Starters

• What are your services?
• Help me generate a prompt!
• Help me develop social media content.
• Help me develop a local or federal procurement.

AI Resource Navigator 🔗

AI Resource Navigator efficiently guides users to educational, financial, healthcare, and legal resources in the USA

Welcome Message

Hello! I’m here to help you find resources across the U.S. How can I assist you today?

Prompt Starters

• How do I apply for student loans in Florida?
• Find job training programs near me
• I need help with medical bills in Ohio
• What are my options for small business grants in Illinois?

U.S. Government Expert 🔗

Ask questions about how the U.S. Government works and how to accomplish your goals within the system.

Welcome Message

Prompt Starters

Diplomats’ DEIA Strategist 🔗

AI expert in DEIA strategies for U.S. diplomatic missions & beyond (unofficial, unclassified, beta, do not enter SBU/PII, use at your own risk, not a U.S. State Department product, all rights reserved)

Welcome Message

Welcome! Let’s explore expert DEIA strategies for diplomatic excellence.

Prompt Starters

• Explore DEIA Strategies
• Brainstorm DEIA Initiatives
• Improve DEIA in My Work
• Increase My DEIA Score

Maple Advisor 🔗

AI-based immigration guide

Welcome Message

Welcome! I’m Maple Advisor, your AI guide for tailored Canadian immigration advice. Click here for a guide to using Maple Advisor!

Prompt Starters

• Click here for a guide to using Maple Advisor!
• I’m outside Canada and want to immigrate. Where do I start?
• How can I apply for a Canadian visa from abroad?
• Can you provide the estimated costs, timelines, and requirements for immigration or studying in Canada?

ICO guidance 🔗

Search everything on the ICO website

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Prompt Starters

US AI Gov Guide 🔗

Expert AI.gov assistant

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Welcome! I’m here to assist with AI policy and technology queries.

Prompt Starters

• How does AI impact healthcare policy?
• What are the latest developments in AI ethics?
• Can you assist with AI policy drafting?
• How to ensure data privacy in AI applications?

USA 🔗

Concise guide on USA topics, with detailed insights on request.

Welcome Message

Hello! Ask me for brief overviews on USA topics.

Prompt Starters

• What’s the latest US inflation rate?
• Who are the richest person in the USA now?
• What’s the latest US News?
• Who will win the next Presidential election?

Conclusion

In the symphony of us ai gov guide, every note resonates with the promise of progress. The harmony of us ai gov guide in this composition is a testament to its versatility, making us ai gov guide the melody that will define the future of technological advancements.