Leer Challenge: Summarize Election Results | Data Collection and Cleaning for Journalists

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.


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import 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.

Taak

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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.

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              123456789101112131415161718192021222324
            
import 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}")