Add a discussion of cross-sectional distributions in economics by jstac · Pull Request #123 · QuantEcon/lecture-python-intro

jstac · 2023-03-03T06:12:46Z

Fixes #119

netlify · 2023-03-03T06:12:51Z

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shlff · 2023-04-16T05:57:35Z

To do:

embed the figure code into the lecture
shift to lecture
add to _toc
update the data used for those figures: update datasets used in lecture cross_section high_dim_data#3
update the figure imports with the updated data

shlff · 2023-04-16T06:29:26Z

Thanks @jstac . I've embedded the code for figures into the lecture.

This PR will be ready for review once the databases and imports are getting updated.

Looking forward to your comments.

jstac · 2023-04-16T23:33:00Z

Thanks @shlff , please let me know when it's ready to review. Please add to the _toc

mmcky · 2023-04-19T23:02:55Z

@shlff the execution issue is

ModuleNotFoundError: No module named 'interpolation'

and there are missing images

/home/runner/work/lecture-python-intro/lecture-python-intro/lectures/cross_section.md:: WARNING: image file not readable: _static/lecture_specific/heavy_tails/light_heavy_fig1.png

jstac · 2023-04-23T20:47:01Z

@shlff could you please coordinate with @HengchengZhang to get this ready for review.

shlff · 2023-04-24T01:12:29Z

Sure thanks @jstac .

Hi @HengchengZhang , it would be appreciated if we can get your help to get rid of the building issues here.

HengchengZhang · 2023-04-24T01:22:17Z

Sure thanks @jstac .

Hi @HengchengZhang , it would be appreciated if we can get your help to get rid of the building issues here.

Thanks for your nice work @shlff, I will have a look at this today.

github-actions · 2023-04-24T04:07:58Z

🚀 Deployed on https://644b685369be4f5f64eff502--taupe-gaufre-c4e660.netlify.app

shlff · 2023-04-24T04:33:57Z

Thanks @HengchengZhang . I've fixed the building issue.

Hi @jstac please find preview of the lecture here:

https://6446037934418c578f00b88e--taupe-gaufre-c4e660.netlify.app/cross_section.html

jstac · 2023-04-24T05:22:21Z

Thanks @shlff . Is this ready for review? (the label still says in-work).

shlff · 2023-04-24T11:06:06Z

Thanks @shlff . Is this ready for review? (the label still says in-work).

Yep John this lecture is ready for you to review and modify.

Looking forward to your comments and suggestions.

jstac · 2023-04-24T11:48:47Z

Many thanks for your hard work @shlff . It's much appreciated.

Once small thing: each solution should follow its exercise. Would you mind to make that change?

I'm now wondering if it wouldn't be better to combine this lecture with the more advanced one.

Perhaps I'll do that and put the combined one in this series. I might try to do it later this week...

shlff · 2023-04-25T00:52:30Z

Thanks @jstac . I've made the change and it is ready for review:

https://644723aa86a48548c2fa7627--taupe-gaufre-c4e660.netlify.app/cross_section.html

I think it is good to combine this with the advanced lecture:

it will be easier for students to understand if we can explain the heavy tails with theory in a more intuitive way and examples. Very looking forward to it.

jstac · 2023-04-25T23:09:19Z

@shlff Your data for GDP per capita seems to include regions as well as countries:

jstac · 2023-04-25T23:11:39Z

We don't need to pip install statsmodels

jstac · 2023-04-25T23:14:54Z

+### GDP
+
+```{code-cell} ipython3
+df_gdp1 = extract_wb(varlist=['NY.GDP.MKTP.CD']) # gdp for all countries from 1960 to 2022


Can you please make this for just one year -- the latest available?

I don't think it makes sense to pool this data across years.

jstac · 2023-04-25T23:19:53Z

@shlff Could you please address the points above?

Also, could you please try to stay within 80 characters per line, where possible?

shlff · 2023-04-26T05:24:22Z

@shlff Your data for GDP per capita seems to include regions as well as countries:

Thanks @jstac . I will modify the data to eliminate regions and consider the cross-sectional data in the most recent year.

jstac · 2023-04-28T00:28:09Z

@shlff have you had time to address the issues above?

shlff · 2023-04-28T00:31:46Z

@shlff have you had time to address the issues above?

Thanks @jstac . Sorry I forgot to update you.

The commit 22fabf1 addressed the issues you mentioned.

Please find the preview here:

https://6449f917a150993a4c40aa2f--taupe-gaufre-c4e660.netlify.app/cross_section.html

Looking forward to your comments.

jstac · 2023-04-28T00:42:15Z

Thanks @shlff . It's helpful if you can let me know when your changes are ready for my review.

Here you use [48:] twice. Does that remove the first 2 * 48 observations? Is that your intention?

# get gdp and gdp per capita for all regions and countries in 2021
df_gdp1 = extract_wb(varlist=['NY.GDP.MKTP.CD'], s="2021", e="2021")[48:] 
df_gdp2 = extract_wb(varlist=['NY.GDP.PCAP.CD'], s="2021", e="2021")[48:] 

# Keep the data for all countries only
df_gdp1 = df_gdp1[48:] 
df_gdp2 = df_gdp2[48:]

Is there a more robust way to get just the countries rather than the regions?

Is there a way to make it more readable?

shlff · 2023-04-28T01:05:24Z

Sure @jstac . Really sorry about this.

Thanks for spotting the typo. We just want to remove the first 48 observations.

A more readable way to get just countries rather than regions could be

get a list of regions (or all countries),
keep the data for all countries only.

I found other users of pandas_datareader also raised a similar question:

wb.download country: add option to exclude aggregates pydata/pandas-datareader#754

I will modify it and update you soon.

shlff · 2023-04-28T06:26:58Z

+```{code-cell} ipython3
+def extract_wb(varlist=['NY.GDP.MKTP.CD'], 
+               c='all', 
+               s=1900, 
+               e=2021, 
+               varnames=None):
+    if c == "all_countries":
+        #  keep countries only (no aggregated regions)
+        countries = wb.get_countries()
+        countries_code = countries[countries['region'] != 'Aggregates']['iso3c'].values
+
+    df = wb.download(indicator=varlist, country=countries_code, start=s, end=e).stack().unstack(0).reset_index()
+    df = df.drop(['level_1'], axis=1).transpose() # set_index(['year'])
+    if varnames != None:
+        df.columns = varnames
+        df = df[1:]
+    return df
+```


Thanks for your comments @jstac . Please find my solution to get rid of regions for the GDP data and a preview here:

https://644b685369be4f5f64eff502--taupe-gaufre-c4e660.netlify.app/cross_section.html

Since we only use this function once? Should we eliminate the function but use the code inside directly?

Looking forward to your comments and review.

jstac · 2023-04-28T07:25:04Z

Thanks @shlff

misc

4ea4668

github-actions Bot temporarily deployed to commit March 3, 2023 06:15 Inactive

Merge branch 'main' into add_cross_section

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shlff added the writing-in-work label Mar 11, 2023

embed_figure_code_w_fix

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github-actions Bot temporarily deployed to commit April 16, 2023 06:41 Inactive

reorganise_add_toc

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shlff mentioned this pull request Apr 17, 2023

update datasets used in lecture cross_section QuantEcon/high_dim_data#3

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mmcky added 2 commits April 20, 2023 07:19

Merge branch 'main' into add_cross_section

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shlff force-pushed the add_cross_section branch from 287b4a4 to b8b93f5 Compare April 23, 2023 22:49

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shlff force-pushed the add_cross_section branch from b8b93f5 to 116fa63 Compare April 24, 2023 03:50

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shlff force-pushed the add_cross_section branch from ac1773b to b2840dc Compare April 24, 2023 04:16

shlff added ready and removed writing-in-work labels Apr 24, 2023

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jstac commented Apr 25, 2023

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jstac added writing-in-work and removed ready labels Apr 26, 2023

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shlff reviewed Apr 28, 2023

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jstac merged commit 48d4fd0 into main Apr 28, 2023

jstac deleted the add_cross_section branch April 28, 2023 07:25

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