Noble Prize Winners

from IPython.core.display import HTML, Image

Image('obama.jpg')

import pandas as pd

HTML("<h4>we import the dataset which is in csv format</h4>")

data_set = pd.read_csv('archive.csv')

HTML("<h4>lets check the length of the dataset</h4>")

len(data_set)

HTML("<h4>display the first five rows</h4>")

data_set.head()

HTML("<h4>display the last five rows</h4>")

data_set.tail(5)

HTML("<h4> 10 Countries with the most awards</h4>")

data_set['Birth Country'].value_counts().head(10)

top10 = data_set['Birth Country'].value_counts().head(10)

HTML("<h4> 10 Countries with the least awards</h4>")

data_set['Birth Country'].value_counts().tail(10)

HTML("<h4>lets visualize top ten countries with the most awards</h4>")

import matplotlib.pyplot as plt
%matplotlib inline

plt.style.use('ggplot')

def adjust_plot(ax):
    plt.rc('xtick',labelsize=23)
    plt.rc('ytick',labelsize=23)
    # create a list to collect the plt.patches data
    totals = []
    # find the values and append to list
    for i in ax.patches:
        totals.append(i.get_height())
        
    # set individual bar lables using above list
    total = sum(totals)
    
    # set individual bar lables using above list
    for i in ax.patches:
        # get_x pulls left or right; get_height pushes up or down
        ax.text(i.get_x()-.03, i.get_height()+.5, \
            str(round((i.get_height()/total)*100, 2))+'%', fontsize=15,
                color='black')

ax= top10.plot.bar(figsize=(17,6))
adjust_plot(ax)

HTML("<h4>lets see the awards in terms of category</h4>")

data_set.Category.value_counts()

HTML("<h4>lets visualize the awards in terms of category</h4>")

cat = data_set.Category.value_counts()

ax = cat.plot(kind='bar',figsize=(16,6))
adjust_plot(ax)

HTML("<h4>lets count the awards in terms of gender</h4>")

data_set.Sex.value_counts()

HTML("<h4>lets visualize the awards in terms of gender</h4>")

gender = data_set.Sex.value_counts()

ax = gender.plot(kind='bar',figsize=(10,6))
adjust_plot(ax)

HTML("<h4>lets check the number of female that have won it from united states</h4>")

us = data_set[data_set['Birth Country'] == 'United States of America']

us.Sex.value_counts()

us_v = us.Sex.value_counts()

ax = us_v.plot(kind='bar')
adjust_plot(ax)

HTML("<h4>lets analyze the awards in terms of continent</h4>")

contries = data_set['Birth Country'].unique()

contries

n_america = ['United States of America','Canada','Costa Rica','Guatemala','Mexico',]
europe = ['Netherlands', 'France', 'Prussia (Poland)', 'Switzerland',
       'Prussia (Germany)', 'Schleswig (Germany)','Sweden'
        'Norway', 'Faroe Islands (Denmark)', 'United Kingdom',
       'Russian Empire (Poland)', 'Scotland', 'Spain', 'Russia',
       'Poland', 'Germany', 'Austrian Empire (Czech Republic)',
       'Hungary (Slovakia)', 'Tuscany (Italy)', 'Italy', 'Bavaria (Germany)',
       'Austrian Empire (Italy)',
       'East Friesland (Germany)', 'Russian Empire (Ukraine)', 'Denmark',
       'Luxembourg', 'Russian Empire (Latvia)', 'Belgium',
       'Hesse-Kassel (Germany)', 'Germany (Russia)',
       'Mecklenburg (Germany)', 'Austria', 'Prussia (Russia)',
       'Austria-Hungary (Slovenia)', 'Ireland', 
        'Austrian Empire (Austria)','Germany (Poland)', 'Württemberg (Germany)', 
       'Austria-Hungary (Hungary)', 'Austria-Hungary (Austria)',
       'Austria-Hungary (Croatia)', 'Russian Empire (Finland)',
       'Austria-Hungary (Poland)', 'Chile',
       'Austria-Hungary (Czech Republic)', 'Portugal', 
        'Germany (France)', 'Iceland','Guadeloupe Island','Bosnia (Bosnia and Herzegovina)',
       'Hungary', 'Russian Empire (Azerbaijan)',
       'Ottoman Empire (Turkey)', 
       'Union of Soviet Socialist Republics (Russia)',
       'Austria-Hungary (Ukraine)', 
       'Russian Empire (Belarus)', 'Romania',
       'Austria-Hungary (Bosnia and Herzegovina)',
       'Russian Empire (Russia)', 'Northern Ireland',
       'Poland (Lithuania)', 'British West Indies (Saint Lucia)',
       'Crete (Greece)', 'Ottoman Empire (Republic of Macedonia)',
        'Russian Empire (Lithuania)',
       'Poland (Ukraine)', 'Bulgaria', 'Lithuania', 
       'Madagascar', 'German-occupied Poland (Poland)','West Germany (Germany)', 
       'Saint Lucia', 'Poland (Belarus)', 'East Timor',
       'Free City of Danzig (Poland)','Union of Soviet Socialist Republics (Belarus)',
       'British Protectorate of Palestine (Israel)', 'Turkey',
        'Czechoslovakia (Czech Republic)', 'Finland', 'Cyprus',
         'Pakistan', 'Ukraine']
africa = ['Egypt','South Africa','Morocco','Liberia','Southern Rhodesia (Zimbabwe)',
          'Gold Coast (Ghana)', 'Kenya','Nigeria','French Algeria (Algeria)',]
asia = ['Japan','China','India','British Mandate of Palestine (Israel)'
        'British India (Bangladesh)','Korea (South Korea)','Iran','India (Pakistan)',
        'British India (India)','Yemen',"Tibet (People's Republic of China)",
        'Java, Dutch East Indies (Indonesia)','Taiwan','Persia (Iran)','Burma (Myanmar)',
         'Vietnam',]
s_america = ['Trinidad','Venezuela','Brazil', 'Argentina','Peru','Colombia']
oceania = ['New Zealand', 'Australia']

continent = {
    "n_america":n_america,
    "africa":africa,
    "asia":asia,
    "s_america":s_america,
    "europe":europe,
    "oceania":oceania
}

def cont(val):
    if val in continent['n_america']:
        return 'North Anmerica'
    if val in continent['africa']:
        return 'Africa'
    if val in continent['asia']:
        return 'Asia'
    if val in continent['oceania']:
        return 'Oceania'
    if val in continent['s_america']:
        return 'South America'
    return 'Europe' ## this is for europe

data_set['contNumber'] = data_set['Birth Country'].apply(cont)

cotin = data_set.contNumber.value_counts()

ax = cotin.plot(kind='bar',figsize=(15,6))
adjust_plot(ax)