Olimpic Medals DataSets

00-readme.md

Olimpic Medals DataSets

advantage_influence_host_country.py

# Import pandas
import pandas as pd

# Left join editions and ioc_codes: hosts
hosts = pd.merge(editions, ioc_codes, how='left')

# Extract relevant columns and set index: hosts
hosts = hosts[['Edition', 'NOC']].set_index('Edition')

# Fix missing 'NOC' values of hosts
print(hosts.loc[hosts.NOC.isnull()])
hosts.loc[1972, 'NOC'] = 'FRG'
hosts.loc[1980, 'NOC'] = 'URS'
hosts.loc[1988, 'NOC'] = 'KOR'

# Reset Index of hosts: hosts
hosts.reset_index(inplace=True)

# Print hosts
print(hosts)
# =============================================
# Import pandas
import pandas as pd

# Reshape fractions_change: reshaped
reshaped = fractions_change.melt(id_vars='Edition', value_name="Change")

# Print reshaped.shape and fractions_change.shape
print(reshaped.shape, fractions_change.shape)

# Extract rows from reshaped where 'NOC' == 'CHN': chn
chn = reshaped[reshaped['NOC'] == 'CHN']

# Print last 5 rows of chn with .tail()
print(chn.tail())

#=============================================
# Merge reshaped and hosts: merged
merged = pd.merge(reshaped, hosts, how='inner')

# Print first 5 rows of merged
print(merged.head())

# Set Index of merged and sort it: influence
influence = merged.set_index('Edition').sort_index()

# Print first 5 rows of influence
print(influence.head())
#==============================================
# Import pyplot
import matplotlib.pyplot as plt

# Extract influence['Change']: change
change = influence['Change']

# Make bar plot of change: ax
ax = change.plot(kind='bar')

# Customize the plot to improve readability
ax.set_ylabel("% Change of Host Country Medal Count")
ax.set_title("Is there a Host Country Advantage?")
ax.set_xticklabels(editions['City'])

# Display the plot
plt.show()

count_medals_per_country_per_edition

"""
Counting medals by country/edition in a pivot table
Here, you'll start with the concatenated DataFrame medals from the previous exercise.

You can construct a pivot table to see the number of medals each country won in each year. The result is a new DataFrame with the Olympic edition on the Index and with 138 country NOC codes as columns. 
If you want a refresher on pivot tables, it may be useful to refer back to the relevant exercise

"""

# Construct the pivot_table: medal_counts
medal_counts = medals.pivot_table(index='Edition', values='Athlete', columns='NOC', aggfunc='count')

expanding.py

#https://stackoverflow.com/questions/45370666/what-are-pandas-expanding-window-functions
"""

Computing percentage change in fraction of medals won
Here, you'll start with the DataFrames editions, medals, medal_counts, & fractions from prior exercises.

To see if there is a host country advantage, you first want to see how the fraction of medals won changes from edition to edition.

The expanding mean provides a way to see this down each column. 
It is the value of the mean with all the data available up to that point in time. 
If you are interested in learning more about pandas' expanding transformations, 
this section of the pandas documentation has additional information.
"""
#Apply the expanding mean: mean_fractions
mean_fractions = fractions.expanding().mean()

# Compute the percentage change: fractions_change
fractions_change = mean_fractions.pct_change() * 100

# Reset the index of fractions_change: fractions_change
fractions_change = fractions_change.reset_index()

# Print first & last 5 rows of fractions_change
print(fractions_change.head())
print(fractions_change.tail())

fraction_per_country

"""
Computing fraction of medals per Olympic edition
In this exercise, you'll start with the DataFrames editions, medals, & medal_counts from prior exercises.

You can extract a Series with the total number of medals awarded in each Olympic edition.

The DataFrame medal_counts can be divided row-wise by the total number of medals awarded each edition; the method .divide() performs the broadcast as you require.

This gives you a normalized indication of each country's performance in each edition.
"""

# Set Index of editions: totals
totals = editions.set_index('Edition')

# Reassign totals['Grand Total']: totals
totals = totals['Grand Total']

# Divide medal_counts by totals: fractions
fractions = medal_counts.divide(totals, axis='rows')

# Print first & last 5 rows of fractions
print(fractions.head())