Review Suggestions for GDP Lecture

Thanks everyone for putting together this amazing notebook.

I really love those visualizations!

Below are some minor comments regarding styles, code, and content : )

General Comments

[ ] Only the first letter and proper nouns should be capitalized in graph labels and chapter titles.

[ ] Use graph captions in myst instead of title in matplotlib. For example, the title for the first figure can be embedded as


```{code-cell} ipython3
---
mystnb:
figure:
caption: "Nominal vs. real GDP, United States, 1949 - 2023"
name: real_nominal_gdp
---

start_date = datetime.datetime(1949, 1, 1) end_date = datetime.datetime(2023, 10, 1)

nominal_gdp = web.DataReader('GDP', 'fred', start_date, end_date) real_gdp = web.DataReader('GDPC1', 'fred', start_date, end_date)

plt.figure(figsize=(10, 6)) plt.plot(nominal_gdp.index, nominal_gdp['GDP'], label='Nominal GDP') plt.plot(real_gdp.index, real_gdp['GDPC1'], label='Real GDP', linestyle='--')

plt.xlabel('Year') plt.ylabel('Billions of dollars') plt.legend() plt.show()

[ ] Section numbers can be removed (i.e. ## 1.2 GDP vs. GDP per Capita-> ## GDP vs. GDP per Capita).
[ ] Data sources can be included in the caption (This is related to the second comment in this section above).
[x] Each line should include only one sentence.
[x] The language used in the lecture should be concise. Long sentences should be avoided.

Introduction

[x] Consider adding an introduction with motivation and list out the content of this lecture at the beginning.

What is GDP

[ ] Remove capitalization of 'Real' and 'Nominal' GDP in the sentence.
[ ] Chained dollar is not properly introduced -- and it might be too advanced for an introductory lecture.
[ ] I suggest we state the dollar unit in the legend instead of on the axis and use terms like 2017 dollars:

plt.plot(nominal_gdp.index, nominal_gdp['GDP'], label='Nominal GDP')
plt.plot(real_gdp.index, real_gdp['GDPC1'], label='Real GDP', linestyle='--')

plt.title('Nominal vs. Real GDP of US (1949 - 2023)')
plt.xlabel('Year')
plt.ylabel('Billions of Dollars / Billions of Chained 2017 Dollars')

plt.plot(nominal_gdp.index, nominal_gdp['GDP'], label='Nominal GDP')
plt.plot(real_gdp.index, real_gdp['GDPC1'], label='Real GDP in 2017 dollars', linestyle='--')

plt.xlabel('Year')
plt.ylabel('Billions of dollars')

[ ] The line 'Source: Federal Reserve Economic Data (St. Louis Fed)' can be put into the caption of the figure.
[ ] Remove wb.series.info(q='GDP') as it is not used.
[ ] In ## 1.3 GDP: Ways of Calculation, I suggest we introduce only one way of GDP calculation (perhaps value added method) and refer readers to other methods using links.
[ ] 'User-Selected Country' -> US (or add other countries into the analysis)
[ ] Add short descriptions on graphs in Section 1.4.1 - 1.4.3

GDP: Unique Factors

[x] Split paragraphs so that each paragraph contains only one sentence. Use dot points when needed.

[ ] Merge the following Excel files below into one .xlsx to avoid duplicated read_excel in the code cell below:

data_hdi = pd.read_excel('GDP_HDI.xlsx')
data_gni = pd.read_excel('GDP_GNI.xlsx')
data_lifeexp = pd.read_excel('GDP_LIFEEXP.xlsx')
data_education = pd.read_excel('GDP_SCHOOLING.xlsx')
data_energy = pd.read_excel('GDP_ENERGY.xlsx')
data_mortality = pd.read_excel('GDP_MORTALITY.xlsx')
data_gini = pd.read_excel('GDP_GINI.xlsx')

[ ] I think the graphs are not in the latest version as there are more beautiful graphs demonstrated in the meeting. Update graphs to the latest version.

[ ] The code below (and similar code in this section) can be improved by using Pandas


gdppc_lowincome = []; lifeexp_lowincome = [];
gdppc_highincome = []; lifeexp_highincome = [];

for i in range(len(data_lifeexp.gdppc)): if data_lifeexp.gdppc[i] < 28000: gdppc_lowincome.append(data_lifeexp.gdppc[i]) lifeexp_lowincome.append(data_lifeexp.life_exp[i]) else: gdppc_highincome.append(data_lifeexp.gdppc[i]) lifeexp_highincome.append(data_lifeexp.life_exp[i])

See https://python-programming.quantecon.org/pandas.html#select-data-by-conditions.

## 4. GDP and Environment

- [ ] In the line,

pd.read_excel('Global temp.xlsx', sheet_name='Temperature Anomaly')


`'Global temp.xlsx'` is not found.

- [ ] In the sentence 'The Pearson correlation coefficient for this set of data is 0.9317515940963721 ...' we can round the number to 2 digits. Also, is Pearson correlation a good metric for non-linear correlation like we have shown in the graph? (Hint: search 'linear' in https://en.wikipedia.org/wiki/Correlation)

## When addressing these issues...

When opening the PR addressing a subset of these issues, please add items you addressed in the description. 

It would be a great help for reviewers to see what has been done and what's left on the table.

QuantEcon / reading-group-2024