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FIX: FutureWarning in heavy_tails #581

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Merged
merged 5 commits into from
Mar 28, 2025
Merged

FIX: FutureWarning in heavy_tails #581

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b318cb2
FIX: FutureWarning in heavy_tails
mmcky Mar 28, 2025
5807435
Update lectures/heavy_tails.md
mmcky Mar 28, 2025
7f70a05
minor typo
mmcky Mar 28, 2025
7758078
fix install of wbgapi
mmcky Mar 28, 2025
75a0cd4
fix cntry_mapper
mmcky Mar 28, 2025
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33 changes: 15 additions & 18 deletions lectures/heavy_tails.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ jupytext:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.16.1
jupytext_version: 1.16.7
kernelspec:
display_name: Python 3 (ipykernel)
language: python
Expand All @@ -19,7 +19,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
```{code-cell} ipython3
:tags: [hide-output]

!pip install --upgrade yfinance pandas_datareader
!pip install --upgrade yfinance wbgapi
```

We use the following imports.
Expand All @@ -31,7 +31,7 @@ import yfinance as yf
import pandas as pd
import statsmodels.api as sm

from pandas_datareader import wb
import wbgapi as wb
from scipy.stats import norm, cauchy
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
Expand Down Expand Up @@ -790,24 +790,21 @@ def empirical_ccdf(data,
:tags: [hide-input]

def extract_wb(varlist=['NY.GDP.MKTP.CD'],
c='all_countries',
c='all',
s=1900,
e=2021,
varnames=None):
if c == "all_countries":
# Keep countries only (no aggregated regions)
countries = wb.get_countries()
countries_name = countries[countries['region'] != 'Aggregates']['name'].values
c = "all"

df = wb.download(indicator=varlist, country=c, start=s, end=e).stack().unstack(0).reset_index()
df = df.drop(['level_1'], axis=1).transpose()
df = wb.data.DataFrame(varlist, economy=c, time=range(s, e+1, 1), skipAggs=True)
df.index.name = 'country'

if varnames is not None:
df.columns = varnames
df = df[1:]
df.columns = variable_names

cntry_mapper = pd.DataFrame(wb.economy.info().items)[['id','value']].set_index('id').to_dict()['value']
df.index = df.index.map(lambda x: cntry_mapper[x]) #map iso3c to name values

df1 =df[df.index.isin(countries_name)]
return df1
return df
```

### Firm size
Expand Down Expand Up @@ -914,9 +911,9 @@ variable_code = ['NY.GDP.MKTP.CD', 'NY.GDP.PCAP.CD']
variable_names = ['GDP', 'GDP per capita']

df_gdp1 = extract_wb(varlist=variable_code,
c="all_countries",
s="2021",
e="2021",
c="all",
s=2021,
e=2021,
varnames=variable_names)
df_gdp1.dropna(inplace=True)
```
Expand Down
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