Challenge: Analyze and Visualize Real-World Data
Visualizing real-world data is a fundamental skill for anyone working with mathematics or science. When you collect data—such as daily temperatures, rainfall, or measurements from experiments—it is rarely enough to just look at the raw numbers. Visualizations like line plots and histograms help you quickly spot patterns, trends, and outliers, making the information more accessible and meaningful. Understanding the distribution of your data, through measures like the mean and standard deviation, enables you to summarize large datasets with just a few numbers and supports deeper analysis. By combining summary statistics with clear visualizations, you gain insights that are difficult to achieve by looking at raw data alone.
123456789101112# List of daily temperatures temperatures = [68, 70, 72, 71, 69, 73, 75, 74, 72, 70] # Calculate mean mean_temp = sum(temperatures) / len(temperatures) # Calculate standard deviation squared_diffs = [(x - mean_temp) ** 2 for x in temperatures] std_dev = (sum(squared_diffs) / len(temperatures)) ** 0.5 print("Mean temperature:", mean_temp) print("Standard deviation:", std_dev)
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Write a function that takes a list of daily temperatures and performs the following steps:
- Calculate the mean of the temperatures.
- Calculate the standard deviation of the temperatures.
- Create a line plot showing the temperature for each day, with labeled axes and a title.
- Create a histogram showing the distribution of temperatures, with labeled axes and a title.
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Visualizing real-world data is a fundamental skill for anyone working with mathematics or science. When you collect data—such as daily temperatures, rainfall, or measurements from experiments—it is rarely enough to just look at the raw numbers. Visualizations like line plots and histograms help you quickly spot patterns, trends, and outliers, making the information more accessible and meaningful. Understanding the distribution of your data, through measures like the mean and standard deviation, enables you to summarize large datasets with just a few numbers and supports deeper analysis. By combining summary statistics with clear visualizations, you gain insights that are difficult to achieve by looking at raw data alone.
123456789101112# List of daily temperatures temperatures = [68, 70, 72, 71, 69, 73, 75, 74, 72, 70] # Calculate mean mean_temp = sum(temperatures) / len(temperatures) # Calculate standard deviation squared_diffs = [(x - mean_temp) ** 2 for x in temperatures] std_dev = (sum(squared_diffs) / len(temperatures)) ** 0.5 print("Mean temperature:", mean_temp) print("Standard deviation:", std_dev)
Swipe to start coding
Write a function that takes a list of daily temperatures and performs the following steps:
- Calculate the mean of the temperatures.
- Calculate the standard deviation of the temperatures.
- Create a line plot showing the temperature for each day, with labeled axes and a title.
- Create a histogram showing the distribution of temperatures, with labeled axes and a title.
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