Challenge: Summarize Election Results
Election coverage often requires you to quickly analyze and summarize large amounts of voting data. As a journalist, you need to identify winners, trends, and turnout patterns to inform your audience. By using Python and pandas, you can efficiently process raw election results and generate clear, accurate reports that highlight the most important outcomes.
123456789101112131415161718192021222324import pandas as pd # Example DataFrame: Election results by precinct data = { "precinct": ["A", "A", "B", "B", "C", "C"], "candidate": ["Smith", "Jones", "Smith", "Jones", "Smith", "Jones"], "votes": [120, 80, 150, 100, 130, 90], "turnout": [200, 200, 250, 250, 220, 220] } df = pd.DataFrame(data) # Calculate total votes for each candidate total_votes = df.groupby("candidate")["votes"].sum() print("Total votes for each candidate:") print(total_votes) # Determine the candidate with the most votes winner = total_votes.idxmax() winner_votes = total_votes.max() print(f"Winner: {winner} with {winner_votes} votes") # Calculate average voter turnout per precinct avg_turnout = df.groupby("precinct")["turnout"].first().mean() print(f"Average voter turnout per precinct: {avg_turnout:.2f}")
These grouping and aggregation techniques are essential tools for journalists. They allow you to condense complex election datasets into clear, digestible summaries. By quickly identifying total votes, winners, and turnout averages, you can report key findings to your audience with confidence and speed, even when working under tight deadlines.
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Write a function that takes a predefined DataFrame and does the following:
- Calculate total votes for each candidate.
- Identify the candidate with the highest votes.
- Calculate the average voter turnout per precinct.
- Print a clear summary report with these results.
Solución
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Can you explain how the code calculates the winner?
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Can you show how to analyze trends or patterns in the voting data?
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Election coverage often requires you to quickly analyze and summarize large amounts of voting data. As a journalist, you need to identify winners, trends, and turnout patterns to inform your audience. By using Python and pandas, you can efficiently process raw election results and generate clear, accurate reports that highlight the most important outcomes.
123456789101112131415161718192021222324import pandas as pd # Example DataFrame: Election results by precinct data = { "precinct": ["A", "A", "B", "B", "C", "C"], "candidate": ["Smith", "Jones", "Smith", "Jones", "Smith", "Jones"], "votes": [120, 80, 150, 100, 130, 90], "turnout": [200, 200, 250, 250, 220, 220] } df = pd.DataFrame(data) # Calculate total votes for each candidate total_votes = df.groupby("candidate")["votes"].sum() print("Total votes for each candidate:") print(total_votes) # Determine the candidate with the most votes winner = total_votes.idxmax() winner_votes = total_votes.max() print(f"Winner: {winner} with {winner_votes} votes") # Calculate average voter turnout per precinct avg_turnout = df.groupby("precinct")["turnout"].first().mean() print(f"Average voter turnout per precinct: {avg_turnout:.2f}")
These grouping and aggregation techniques are essential tools for journalists. They allow you to condense complex election datasets into clear, digestible summaries. By quickly identifying total votes, winners, and turnout averages, you can report key findings to your audience with confidence and speed, even when working under tight deadlines.
Swipe to start coding
Write a function that takes a predefined DataFrame and does the following:
- Calculate total votes for each candidate.
- Identify the candidate with the highest votes.
- Calculate the average voter turnout per precinct.
- Print a clear summary report with these results.
Solución
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