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@@ -618,7 +618,7 @@ df_income_wealth.year.describe()

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[This notebook](https://github.com/QuantEcon/lecture-python-intro/tree/main/lectures/_static/lecture_specific/inequality/data.ipynb) can be used to compute this information over the full dataset.

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```{code-cell} ipython3

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data_url = 'https://github.com/QuantEcon/lecture-python-intro/raw/main/lectures/_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv'

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data_url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/usa-gini-nwealth-tincome-lincome.csv'

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ginis = pd.read_csv(data_url, index_col='year')

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ginis.head(n=5)

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```

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@@ -408,12 +408,12 @@ Let's consider two economic variables GDP per capita and Life Expectancy.

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<iframe src="https://ourworldindata.org/grapher/life-expectancy-vs-gdp-per-capita" loading="lazy" style="width: 100%; height: 600px; border: 0px none;"></iframe>

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:::

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You can download {download}`a copy of the data here <https://github.com/QuantEcon/lecture-python-intro/raw/main/lectures/_static/lecture_specific/simple_linear_regression/life-expectancy-vs-gdp-per-capita.csv>` if you get stuck

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You can download {download}`a copy of the data here <https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/life-expectancy-vs-gdp-per-capita.csv>` if you get stuck

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**Q3:** Use `pandas` to import the `csv` formatted data and plot a few different countries of interest

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```{code-cell} ipython3

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data_url = "https://github.com/QuantEcon/lecture-python-intro/raw/main/lectures/_static/lecture_specific/simple_linear_regression/life-expectancy-vs-gdp-per-capita.csv"

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data_url = "https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/life-expectancy-vs-gdp-per-capita.csv"

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df = pd.read_csv(data_url, nrows=10)

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```

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Read the original on github.com ↗