Lernen Challenge: Analyze Daily Temperature Data | Environmental Data Exploration

In this challenge, you will analyze a set of daily temperature readings for a city over the course of one year. You will use a hardcoded pandas DataFrame to perform the analysis. Your objectives are to calculate the average, minimum, and maximum temperature for the year, identify the day or days with the highest and lowest temperatures, and then present these results in a format that is easy to interpret. This exercise will help you practice essential data exploration techniques that are foundational for environmental data analysis.

Start by importing the necessary library and creating a DataFrame containing daily temperature data. For demonstration purposes, a small sample dataset will be used, but the approach remains the same for larger datasets.


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import pandas as pd

# Sample data: daily temperatures for a week (replace with a full year for real analysis)
data = {
    "date": [
        "2023-01-01", "2023-01-02", "2023-01-03",
        "2023-01-04", "2023-01-05", "2023-01-06",
        "2023-01-07"
    ],
    "temperature_C": [5.2, 6.1, 4.8, 7.3, 3.9, 8.0, 2.5]
}

df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])
print(df)

With the data loaded into a DataFrame, you can now calculate the average, minimum, and maximum temperatures for the year by using pandas methods such as mean(), min(), and max() on the temperature_C column. This step provides a quick summary of the temperature distribution over the period.


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# Calculate summary statistics
average_temp = df["temperature_C"].mean()
min_temp = df["temperature_C"].min()
max_temp = df["temperature_C"].max()

print(f"Average temperature: {average_temp:.2f}°C")
print(f"Minimum temperature: {min_temp:.2f}°C")
print(f"Maximum temperature: {max_temp:.2f}°C")

To identify the day or days with the highest and lowest temperatures, use boolean indexing to filter the DataFrame for rows where the temperature matches the minimum or maximum values. This technique helps you pinpoint extreme events, which are often of particular interest in environmental studies.


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# Find days with minimum temperature
coldest_days = df[df["temperature_C"] == min_temp]

# Find days with maximum temperature
hottest_days = df[df["temperature_C"] == max_temp]

print("Coldest day(s):")
print(coldest_days[["date", "temperature_C"]])

print("Hottest day(s):")
print(hottest_days[["date", "temperature_C"]])

Presenting your results in a clear, readable format is crucial for effective communication. Use formatted print statements to summarize your findings, making it easy for others to understand the key insights from the data.


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print(f"Yearly Temperature Summary:")
print(f"- Average: {average_temp:.2f}°C")
print(f"- Minimum: {min_temp:.2f}°C on {', '.join(coldest_days['date'].dt.strftime('%Y-%m-%d'))}")
print(f"- Maximum: {max_temp:.2f}°C on {', '.join(hottest_days['date'].dt.strftime('%Y-%m-%d'))}")

Applying these techniques to a full year of daily temperature data will give you a comprehensive overview of the city's temperature patterns and help you identify notable weather events. This approach is fundamental in environmental science, as it allows you to summarize large datasets and focus on critical values and trends.

Now, put your skills to the test by analyzing a new set of daily temperature data. Your task is to use the provided DataFrame to calculate the average, minimum, and maximum temperature for the year, identify the day(s) with the highest and lowest temperatures, and output your results in a clear, readable format.

Aufgabe

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Calculate the average, minimum, and maximum temperature for the year.
Identify the date(s) with the lowest and highest temperatures.
Print your results in a clear summary.

Lösung

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import pandas as pd

# Sample data: daily temperatures for a week (replace with a full year for real analysis)
data = {
    "date": [
        "2023-01-01", "2023-01-02", "2023-01-03",
        "2023-01-04", "2023-01-05", "2023-01-06",
        "2023-01-07"
    ],
    "temperature_C": [5.2, 6.1, 4.8, 7.3, 3.9, 8.0, 2.5]
}

df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])
print(df)


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# Calculate summary statistics
average_temp = df["temperature_C"].mean()
min_temp = df["temperature_C"].min()
max_temp = df["temperature_C"].max()

print(f"Average temperature: {average_temp:.2f}°C")
print(f"Minimum temperature: {min_temp:.2f}°C")
print(f"Maximum temperature: {max_temp:.2f}°C")


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# Find days with minimum temperature
coldest_days = df[df["temperature_C"] == min_temp]

# Find days with maximum temperature
hottest_days = df[df["temperature_C"] == max_temp]

print("Coldest day(s):")
print(coldest_days[["date", "temperature_C"]])

print("Hottest day(s):")
print(hottest_days[["date", "temperature_C"]])


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print(f"Yearly Temperature Summary:")
print(f"- Average: {average_temp:.2f}°C")
print(f"- Minimum: {min_temp:.2f}°C on {', '.join(coldest_days['date'].dt.strftime('%Y-%m-%d'))}")
print(f"- Maximum: {max_temp:.2f}°C on {', '.join(hottest_days['date'].dt.strftime('%Y-%m-%d'))}")