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◅ Olympics: Deep Data Analysis ▻
~ Evan Marie Carr (www.EvanMarie.com)

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A comprehensive project on data import, cleaning, data merging, and statistics as well as advanced data visualization with Seaborn

Section Links:

  1. Datasets
  2. Merging and Organizing
  3. Data Cleaning
  4. Most Successful Countries
  5. Top 50 Countries Aggregated
  6. Ranks Dataframe
  7. Statistics with the Ranks Dataframe
  8. Cross Tabulations and top_50_ranking_countries
  9. Top Competing Countries in a Sport
  10. Top Sports for a Country
  11. Sports and Countries Ranks Dataframe
  12. Effects of Geographical Location
  13. Effects of Culture
  14. National and Traditional Sports
  15. BONUS! The Most Rare Sports

◅ Datasets ▻

JUMP TO: Datasets | Merging | Cleaning | Success | Top 50 | Ranks | Statistics | Cross-tabulations | Top Competitors | Top Sports | Sport & Country Ranks | Geography | Culture | National Traditions | Most Rare





→ summer.csv - all summer medals from 1896 to 2012

→ winter.csv - all winter medals from 1924 to 2014

→ dictionary.csv - country info: country code, population, and GDP per capita

%%capture
!pip install urllib
!pip install yfinance
from urllib.request import urlretrieve
from zipfile import ZipFile
url = 'https://mydatabucky.s3.amazonaws.com/olympics_helpers.py'
urlretrieve(url, "olympics_helpers.py")
url_02 = 'https://mydatabucky.s3.amazonaws.com/olympics_data.zip'
urlretrieve(url_02, "olympics_data.zip")
import pandas as pd
import olympics_helpers as h
from IPython.core.display import HTML
pd.options.display.float_format = '{:,.0f}'.format
bgcolor ='#205373'; text_color = '#F2E6C2'
innerbackcolor = "#C4E1F2"; outerbackcolor = "#205373"; fontcolor = "#F2E6C2"
➣ Importing CSV files