Learn practical skills, build real-world projects, and advance your career
Updated a year ago
~ Evan Marie Carr (www.EvanMarie.com)
A comprehensive project on data import, cleaning, data merging, and statistics as well as advanced data visualization with Seaborn
Section Links:
- Datasets
- Merging and Organizing
- Data Cleaning
- Most Successful Countries
- Top 50 Countries Aggregated
- Ranks Dataframe
- Statistics with the Ranks Dataframe
- Cross Tabulations and
top_50_ranking_countries
- Top Competing Countries in a Sport
- Top Sports for a Country
- Sports and Countries Ranks Dataframe
- Effects of Geographical Location
- Effects of Culture
- National and Traditional Sports
- BONUS! The Most Rare Sports
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