Challenge: Visualize Age Distribution in a Community
Visualizing age distribution is a powerful tool for government analysts aiming to understand the demographic makeup of a community. By examining how ages are spread across a population, you can identify trends such as a youth-heavy or aging community, which directly impacts decisions about resource allocation, infrastructure planning, and policy development. These insights help ensure that services like schools, healthcare facilities, and recreational programs are tailored to the needs of the population.
123456789import matplotlib.pyplot as plt ages = [5, 12, 17, 18, 24, 25, 32, 36, 45, 52, 59, 63, 70, 75, 80, 85] plt.hist(ages, bins=8, edgecolor='black') plt.xlabel('Age') plt.ylabel('Number of People') plt.title('Community Age Distribution') plt.show()
When creating a histogram for age distribution, the choice of bin size is crucial. Bins group ages into ranges (such as 0–10, 11–20, etc.), and the number or width of bins affects how detailed your visualization is. Too few bins may hide important patterns, while too many bins can make the plot noisy and hard to interpret. Adding clear axis labels and a descriptive title ensures that anyone reviewing the plot can easily understand what the data represents, making your findings accessible and actionable for both technical and non-technical stakeholders.
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Write a function that creates a histogram to visualize the distribution of ages in a community. The function should use the provided age list and display the histogram with clear labeling.
- Create a histogram of the values in
age_listusing 8 bins. - Set the x-axis label to "Age".
- Set the y-axis label to "Number of People".
- Set the plot title to "Community Age Distribution".
- Display the plot.
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Can you explain how to choose the optimal number of bins for a histogram?
What are some common mistakes to avoid when visualizing age distributions?
Can you suggest other types of plots for demographic data analysis?
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Visualizing age distribution is a powerful tool for government analysts aiming to understand the demographic makeup of a community. By examining how ages are spread across a population, you can identify trends such as a youth-heavy or aging community, which directly impacts decisions about resource allocation, infrastructure planning, and policy development. These insights help ensure that services like schools, healthcare facilities, and recreational programs are tailored to the needs of the population.
123456789import matplotlib.pyplot as plt ages = [5, 12, 17, 18, 24, 25, 32, 36, 45, 52, 59, 63, 70, 75, 80, 85] plt.hist(ages, bins=8, edgecolor='black') plt.xlabel('Age') plt.ylabel('Number of People') plt.title('Community Age Distribution') plt.show()
When creating a histogram for age distribution, the choice of bin size is crucial. Bins group ages into ranges (such as 0–10, 11–20, etc.), and the number or width of bins affects how detailed your visualization is. Too few bins may hide important patterns, while too many bins can make the plot noisy and hard to interpret. Adding clear axis labels and a descriptive title ensures that anyone reviewing the plot can easily understand what the data represents, making your findings accessible and actionable for both technical and non-technical stakeholders.
Swipe to start coding
Write a function that creates a histogram to visualize the distribution of ages in a community. The function should use the provided age list and display the histogram with clear labeling.
- Create a histogram of the values in
age_listusing 8 bins. - Set the x-axis label to "Age".
- Set the y-axis label to "Number of People".
- Set the plot title to "Community Age Distribution".
- Display the plot.
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