Data management datasets

Datasets to use in Pandas class. Convert large datasets to more manageable ones and save as dictionaries that we can turn into dataframes.

This notebook was written by Dave Backus for the NYU Stern course Data Bootcamp.



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import pandas as pd
%matplotlib inline

Datasets

We take these examples from the data input chapter:

Penn World Table
World Economic Outlook
Fama-French
UN Population Projections

All of them come in an unfriendly form; our goal is to fix them. Here we extract small subsets to work with so that we can follow all the steps.

Penn World Table

This one comes with countries stacked on top of each others.



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%%time
url = 'http://www.rug.nl/research/ggdc/data/pwt/v81/pwt81.xlsx'
pwt = pd.read_excel(url, sheetname='Data')
pwt.head()



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pwt.shape



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variables = [0, 1, 3, 4, 6]
countries = ['CHN', 'FRA', 'USA']
years     = [1990, 2000, 2010]

pwt = pwt[variables]



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# one country 
pwt1 = pwt[(pwt['countrycode'] == 'CHN') & (pwt['year'].isin(years))]



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# three countries 
pwt3 = pwt[(pwt['countrycode'].isin(countries)) & (pwt['year'].isin(years))]
pwt3 = pwt3[pwt3['country']==]
pwt3.shape



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pwt3 = pwt3.replace(to_replace=["China, People's Republic of"], value=['China']) 
pwt3.reset_index()
pwt3



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pw3d = pwt3.to_dict(orient='list')
pw3d



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