Impara Challenge: Inflation Rate Visualization | Economic Data Analysis with Python

You are ready to put your skills into practice by visualizing inflation rates for several countries across a decade. This challenge requires you to work with a hardcoded pandas DataFrame containing inflation data, and then use matplotlib to create a clear, informative line plot. Each country's inflation rate should be shown as a distinct line on the same plot, with a legend to identify them and properly labeled axes. This kind of visualization is essential for economists to quickly compare inflation trends across countries and time periods.


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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded inflation data for several countries over 10 years
data = {
    "Year": [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],
    "USA": [2.1, 1.5, 1.6, 0.1, 1.3, 2.1, 2.4, 1.8, 1.2, 4.7],
    "Germany": [2.0, 1.5, 0.9, 0.2, 0.5, 1.5, 1.9, 1.4, 0.5, 3.1],
    "Japan": [0.0, 0.4, 2.7, 0.8, -0.1, 0.5, 1.0, 0.5, 0.0, -0.2],
    "Brazil": [5.4, 6.2, 6.3, 9.0, 8.7, 3.4, 3.7, 3.7, 3.2, 8.7]
}
df = pd.DataFrame(data)

def plot_inflation_rates(df):
    """
    Plots inflation rates for each country over time.
    """
    plt.figure(figsize=(10, 6))
    for country in df.columns:
        if country != "Year":
            plt.plot(df["Year"], df[country], marker="o", label=country)
    plt.xlabel("Year")
    plt.ylabel("Inflation Rate (%)")
    plt.title("Inflation Rates by Country (2012-2021)")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

# Call the function to display the plot
plot_inflation_rates(df)

Compito

Swipe to start coding

Write a function called plot_inflation_rates that takes a pandas DataFrame with a "Year" column and one column per country (containing inflation rates). The function should:

Create a line plot using matplotlib.
Plot each country's inflation rate as a separate line on the same plot.
Add a legend to identify each country.
Label the x-axis as Year and the y-axis as Inflation Rate (%).
Display the plot.

Use the following DataFrame for testing:

Year: [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021].
USA: [2.1, 1.5, 1.6, 0.1, 1.3, 2.1, 2.4, 1.8, 1.2, 4.7].
Germany: [2.0, 1.5, 0.9, 0.2, 0.5, 1.5, 1.9, 1.4, 0.5, 3.1].
Japan: [0.0, 0.4, 2.7, 0.8, -0.1, 0.5, 1.0, 0.5, 0.0, -0.2].
Brazil: [5.4, 6.2, 6.3, 9.0, 8.7, 3.4, 3.7, 3.7, 3.2, 8.7].

Soluzione

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Sezione 1. Capitolo 7

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Suggested prompts:

Can you explain how the plot helps compare inflation trends between countries?

What do the inflation trends suggest about each country's economy during this period?

Can you help me modify the plot to focus on just two countries?

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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded inflation data for several countries over 10 years
data = {
    "Year": [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],
    "USA": [2.1, 1.5, 1.6, 0.1, 1.3, 2.1, 2.4, 1.8, 1.2, 4.7],
    "Germany": [2.0, 1.5, 0.9, 0.2, 0.5, 1.5, 1.9, 1.4, 0.5, 3.1],
    "Japan": [0.0, 0.4, 2.7, 0.8, -0.1, 0.5, 1.0, 0.5, 0.0, -0.2],
    "Brazil": [5.4, 6.2, 6.3, 9.0, 8.7, 3.4, 3.7, 3.7, 3.2, 8.7]
}
df = pd.DataFrame(data)

def plot_inflation_rates(df):
    """
    Plots inflation rates for each country over time.
    """
    plt.figure(figsize=(10, 6))
    for country in df.columns:
        if country != "Year":
            plt.plot(df["Year"], df[country], marker="o", label=country)
    plt.xlabel("Year")
    plt.ylabel("Inflation Rate (%)")
    plt.title("Inflation Rates by Country (2012-2021)")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

# Call the function to display the plot
plot_inflation_rates(df)