Вивчайте Challenge: Visualize Air Quality Trends

In this challenge, you will explore how to visualize air quality trends using a hardcoded pandas DataFrame. Visualizing air quality index (AQI) data over time helps you quickly identify periods of poor air quality and communicate findings effectively. You will learn to create a line plot of AQI values, highlight days with unhealthy air quality, and add clear titles and labels to your plot.

Begin by importing the required libraries. You will use pandas for data handling and matplotlib for plotting. Next, create a DataFrame that represents daily AQI values for a city. This DataFrame will have two columns: one for dates and one for AQI values. After preparing the data, you will plot AQI over time and emphasize days when AQI exceeds a threshold, such as 100, which typically indicates unhealthy air quality.


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import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame with daily AQI values
data = {
    "date": pd.date_range(start="2024-06-01", periods=14, freq="D"),
    "aqi": [75, 82, 95, 110, 120, 99, 85, 102, 130, 88, 92, 105, 97, 115]
}
df = pd.DataFrame(data)

# Set threshold for unhealthy AQI
threshold = 100

# Plot AQI over time
plt.figure(figsize=(10, 6))
plt.plot(df["date"], df["aqi"], label="AQI", color="blue", marker="o")

# Highlight days where AQI exceeds the threshold
exceed = df["aqi"] > threshold
plt.scatter(df["date"][exceed], df["aqi"][exceed], color="red", label="AQI > 100", zorder=5)

# Add titles and labels
plt.title("Daily Air Quality Index (AQI) Trends")
plt.xlabel("Date")
plt.ylabel("AQI Value")
plt.legend()
plt.tight_layout()
plt.show()

Note

The Air Quality Index (AQI) is a standardized indicator for reporting daily air quality. Values above 100 often signal that air pollution may pose health risks, especially for sensitive groups. For more details on AQI categories and their health implications, you can refer to the United States Environmental Protection Agency (EPA) AQI guide.

By practicing this workflow, you gain skills in both data visualization and environmental data interpretation. Being able to highlight critical thresholds in your plots helps stakeholders understand when air quality becomes a concern and supports data-driven decision-making.

Завдання

Swipe to start coding

Use the DataFrame df as shown above.
Plot AQI values over time as a blue line with circle markers.
Highlight days where AQI exceeds 100 using red markers.
Add a title "Daily Air Quality Index (AQI) Trends".
Label the x-axis as "Date" and the y-axis as "AQI Value".
Include a legend distinguishing the line and highlighted points.
Display the plot.

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Секція 1. Розділ 5

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Suggested prompts:

Can you explain how the threshold for unhealthy AQI is determined?

What other ways can I highlight or annotate critical days on the plot?

Can you suggest how to interpret the trends shown in the AQI plot?

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import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame with daily AQI values
data = {
    "date": pd.date_range(start="2024-06-01", periods=14, freq="D"),
    "aqi": [75, 82, 95, 110, 120, 99, 85, 102, 130, 88, 92, 105, 97, 115]
}
df = pd.DataFrame(data)

# Set threshold for unhealthy AQI
threshold = 100

# Plot AQI over time
plt.figure(figsize=(10, 6))
plt.plot(df["date"], df["aqi"], label="AQI", color="blue", marker="o")

# Highlight days where AQI exceeds the threshold
exceed = df["aqi"] > threshold
plt.scatter(df["date"][exceed], df["aqi"][exceed], color="red", label="AQI > 100", zorder=5)

# Add titles and labels
plt.title("Daily Air Quality Index (AQI) Trends")
plt.xlabel("Date")
plt.ylabel("AQI Value")
plt.legend()
plt.tight_layout()
plt.show()