Learn 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)

Task

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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].

Solution

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Section 1. Chapter 7

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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)