Nobel.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"dc":{"key":"3"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 1. The most Nobel of Prizes\n<p><img style=\"float: right;margin:5px 20px 5px 1px; max-width:250px\" src=\"https://assets.datacamp.com/production/project_441/img/Nobel_Prize.png\"></p>\n<p>The Nobel Prize is perhaps the world's most well known scientific award. Except for the honor, prestige and substantial prize money the recipient also gets a gold medal showing Alfred Nobel (1833 - 1896) who established the prize. Every year it's given to scientists and scholars in the categories chemistry, literature, physics, physiology or medicine, economics, and peace. The first Nobel Prize was handed out in 1901, and at that time the Prize was very Eurocentric and male-focused, but nowadays it's not biased in any way whatsoever. Surely. Right?</p>\n<p>Well, we're going to find out! The Nobel Foundation has made a dataset available of all prize winners from the start of the prize, in 1901, to 2016. Let's load it in and take a look.</p>"},{"cell_type":"code","metadata":{"dc":{"key":"3"},"trusted":true,"tags":["sample_code"]},"execution_count":26,"outputs":[{"metadata":{},"execution_count":26,"data":{"text/plain":"   year    category                                           prize  \\\n0  1901   Chemistry               The Nobel Prize in Chemistry 1901   \n1  1901  Literature              The Nobel Prize in Literature 1901   \n2  1901    Medicine  The Nobel Prize in Physiology or Medicine 1901   \n3  1901       Peace                      The Nobel Peace Prize 1901   \n4  1901       Peace                      The Nobel Peace Prize 1901   \n5  1901     Physics                 The Nobel Prize in Physics 1901   \n\n                                          motivation prize_share  laureate_id  \\\n0  \"in recognition of the extraordinary services ...         1/1          160   \n1  \"in special recognition of his poetic composit...         1/1          569   \n2  \"for his work on serum therapy, especially its...         1/1          293   \n3                                                NaN         1/2          462   \n4                                                NaN         1/2          463   \n5  \"in recognition of the extraordinary services ...         1/1            1   \n\n  laureate_type                     full_name  birth_date          birth_city  \\\n0    Individual  Jacobus Henricus van 't Hoff  1852-08-30           Rotterdam   \n1    Individual               Sully Prudhomme  1839-03-16               Paris   \n2    Individual        Emil Adolf von Behring  1854-03-15   Hansdorf (Lawice)   \n3    Individual             Jean Henry Dunant  1828-05-08              Geneva   \n4    Individual                Frédéric Passy  1822-05-20               Paris   \n5    Individual        Wilhelm Conrad Röntgen  1845-03-27  Lennep (Remscheid)   \n\n       birth_country   sex   organization_name organization_city  \\\n0        Netherlands  Male   Berlin University            Berlin   \n1             France  Male                 NaN               NaN   \n2   Prussia (Poland)  Male  Marburg University           Marburg   \n3        Switzerland  Male                 NaN               NaN   \n4             France  Male                 NaN               NaN   \n5  Prussia (Germany)  Male   Munich University            Munich   \n\n  organization_country  death_date death_city death_country  \n0              Germany  1911-03-01     Berlin       Germany  \n1                  NaN  1907-09-07   Châtenay        France  \n2              Germany  1917-03-31    Marburg       Germany  \n3                  NaN  1910-10-30     Heiden   Switzerland  \n4                  NaN  1912-06-12      Paris        France  \n5              Germany  1923-02-10     Munich       Germany  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>category</th>\n      <th>prize</th>\n      <th>motivation</th>\n      <th>prize_share</th>\n      <th>laureate_id</th>\n      <th>laureate_type</th>\n      <th>full_name</th>\n      <th>birth_date</th>\n      <th>birth_city</th>\n      <th>birth_country</th>\n      <th>sex</th>\n      <th>organization_name</th>\n      <th>organization_city</th>\n      <th>organization_country</th>\n      <th>death_date</th>\n      <th>death_city</th>\n      <th>death_country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1901</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 1901</td>\n      <td>\"in recognition of the extraordinary services ...</td>\n      <td>1/1</td>\n      <td>160</td>\n      <td>Individual</td>\n      <td>Jacobus Henricus van 't Hoff</td>\n      <td>1852-08-30</td>\n      <td>Rotterdam</td>\n      <td>Netherlands</td>\n      <td>Male</td>\n      <td>Berlin University</td>\n      <td>Berlin</td>\n      <td>Germany</td>\n      <td>1911-03-01</td>\n      <td>Berlin</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1901</td>\n      <td>Literature</td>\n      <td>The Nobel Prize in Literature 1901</td>\n      <td>\"in special recognition of his poetic composit...</td>\n      <td>1/1</td>\n      <td>569</td>\n      <td>Individual</td>\n      <td>Sully Prudhomme</td>\n      <td>1839-03-16</td>\n      <td>Paris</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1907-09-07</td>\n      <td>Châtenay</td>\n      <td>France</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1901</td>\n      <td>Medicine</td>\n      <td>The Nobel Prize in Physiology or Medicine 1901</td>\n      <td>\"for his work on serum therapy, especially its...</td>\n      <td>1/1</td>\n      <td>293</td>\n      <td>Individual</td>\n      <td>Emil Adolf von Behring</td>\n      <td>1854-03-15</td>\n      <td>Hansdorf (Lawice)</td>\n      <td>Prussia (Poland)</td>\n      <td>Male</td>\n      <td>Marburg University</td>\n      <td>Marburg</td>\n      <td>Germany</td>\n      <td>1917-03-31</td>\n      <td>Marburg</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1901</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1901</td>\n      <td>NaN</td>\n      <td>1/2</td>\n      <td>462</td>\n      <td>Individual</td>\n      <td>Jean Henry Dunant</td>\n      <td>1828-05-08</td>\n      <td>Geneva</td>\n      <td>Switzerland</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1910-10-30</td>\n      <td>Heiden</td>\n      <td>Switzerland</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1901</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1901</td>\n      <td>NaN</td>\n      <td>1/2</td>\n      <td>463</td>\n      <td>Individual</td>\n      <td>Frédéric Passy</td>\n      <td>1822-05-20</td>\n      <td>Paris</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1912-06-12</td>\n      <td>Paris</td>\n      <td>France</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1901</td>\n      <td>Physics</td>\n      <td>The Nobel Prize in Physics 1901</td>\n      <td>\"in recognition of the extraordinary services ...</td>\n      <td>1/1</td>\n      <td>1</td>\n      <td>Individual</td>\n      <td>Wilhelm Conrad Röntgen</td>\n      <td>1845-03-27</td>\n      <td>Lennep (Remscheid)</td>\n      <td>Prussia (Germany)</td>\n      <td>Male</td>\n      <td>Munich University</td>\n      <td>Munich</td>\n      <td>Germany</td>\n      <td>1923-02-10</td>\n      <td>Munich</td>\n      <td>Germany</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"output_type":"execute_result"}],"source":"# Loading in required libraries\n# ... YOUR CODE FOR TASK 1 ...\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n# Reading in the Nobel Prize data\nnobel = pd.read_csv('datasets/nobel.csv')\n\n# Taking a look at the first several winners\n# ... YOUR CODE FOR TASK 1 ...\nnobel.head(6)"},{"cell_type":"markdown","metadata":{"dc":{"key":"10"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 2. So, who gets the Nobel Prize?\n<p>Just looking at the first couple of prize winners, or Nobel laureates as they are also called, we already see a celebrity: Wilhelm Conrad Röntgen, the guy who discovered X-rays. And actually, we see that all of the winners in 1901 were guys that came from Europe. But that was back in 1901, looking at all winners in the dataset, from 1901 to 2016, which sex and which country is the most commonly represented? </p>\n<p>(For <em>country</em>, we will use the <code>birth_country</code> of the winner, as the <code>organization_country</code> is <code>NaN</code> for all shared Nobel Prizes.)</p>"},{"cell_type":"code","metadata":{"dc":{"key":"10"},"trusted":true,"tags":["sample_code"]},"execution_count":28,"outputs":[{"metadata":{},"data":{"text/plain":"911"},"output_type":"display_data"},{"metadata":{},"data":{"text/plain":"Male      836\nFemale     49\nName: sex, dtype: int64"},"output_type":"display_data"},{"metadata":{},"execution_count":28,"data":{"text/plain":"United States of America    259\nUnited Kingdom               85\nGermany                      61\nFrance                       51\nSweden                       29\nJapan                        24\nCanada                       18\nNetherlands                  18\nItaly                        17\nRussia                       17\nName: birth_country, dtype: int64"},"output_type":"execute_result"}],"source":"# Display the number of (possibly shared) Nobel Prizes handed\n# out between 1901 and 2016\n# ... YOUR CODE FOR TASK 2 ...\ndisplay(len(nobel))\n\n# Display the number of prizes won by male and female recipients.\n# ... YOUR CODE FOR TASK 2 ...\ndisplay(nobel.sex.value_counts())\n\n# Display the number of prizes won by the top 10 nationalities.\n# ... YOUR CODE FOR TASK 2 ...\nnobel.birth_country.value_counts().head(10)"},{"cell_type":"markdown","metadata":{"dc":{"key":"17"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 3. USA dominance\n<p>Not so surprising perhaps: the most common Nobel laureate between 1901 and 2016 was a man born in the United States of America. But in 1901 all the winners were European. When did the USA start to dominate the Nobel Prize charts?</p>"},{"cell_type":"code","metadata":{"dc":{"key":"17"},"trusted":true,"tags":["sample_code"]},"execution_count":30,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 911 entries, 0 to 910\nData columns (total 19 columns):\nyear                    911 non-null int64\ncategory                911 non-null object\nprize                   911 non-null object\nmotivation              823 non-null object\nprize_share             911 non-null object\nlaureate_id             911 non-null int64\nlaureate_type           911 non-null object\nfull_name               911 non-null object\nbirth_date              883 non-null object\nbirth_city              883 non-null object\nbirth_country           885 non-null object\nsex                     885 non-null object\norganization_name       665 non-null object\norganization_city       667 non-null object\norganization_country    667 non-null object\ndeath_date              593 non-null object\ndeath_city              576 non-null object\ndeath_country           582 non-null object\nusa_born_winner         911 non-null bool\ndtypes: bool(1), int64(2), object(16)\nmemory usage: 129.1+ KB\n    decade  usa_born_winner\n0     1900            False\n1     1910            False\n2     1920            False\n3     1930            False\n4     1940            False\n5     1950            False\n6     1960            False\n7     1970            False\n8     1980            False\n9     1990            False\n10    2000            False\n11    2010            False\n","output_type":"stream"}],"source":"# Calculating the proportion of USA born winners per decade\nnobel['usa_born_winner'] = True if 'birth_country' == 'United States of America' else False\nnobel.info()\nnobel['decade'] = (np.floor(nobel.year/10)*10).astype(int)\nprop_usa_winners = nobel.groupby(by = 'decade',as_index= False)['usa_born_winner'].mean()\n\n# Display the proportions of USA born winners per decade\n# ... YOUR CODE FOR TASK 3 ...\nprint(prop_usa_winners)"},{"cell_type":"markdown","metadata":{"dc":{"key":"24"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 4. USA dominance, visualized\n<p>A table is OK, but to <em>see</em> when the USA started to dominate the Nobel charts we need a plot!</p>"},{"cell_type":"code","metadata":{"dc":{"key":"24"},"trusted":true,"tags":["sample_code"]},"execution_count":32,"outputs":[{"metadata":{},"data":{"text/plain":"<Figure size 792x504 with 1 Axes>","image/png":"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\n"},"output_type":"display_data"}],"source":"# Setting the plotting theme\nsns.set()\n# and setting the size of all plots.\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.figsize'] = [11, 7]\n\n# Plotting USA born winners \nax = sns.lineplot('decade','usa_born_winner',data = nobel)\n\n# Adding %-formatting to the y-axis\nfrom matplotlib.ticker import PercentFormatter\n# ... YOUR CODE FOR TASK 4 ...\nax.yaxis.set_major_formatter(PercentFormatter())"},{"cell_type":"markdown","metadata":{"dc":{"key":"31"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 5. What is the gender of a typical Nobel Prize winner?\n<p>So the USA became the dominating winner of the Nobel Prize first in the 1930s and had kept the leading position ever since. But one group that was in the lead from the start, and never seems to let go, are <em>men</em>. Maybe it shouldn't come as a shock that there is some imbalance between how many male and female prize winners there are, but how significant is this imbalance? And is it better or worse within specific prize categories like physics, medicine, literature, etc.?</p>"},{"cell_type":"code","metadata":{"dc":{"key":"31"},"trusted":true,"tags":["sample_code"]},"execution_count":34,"outputs":[{"metadata":{},"data":{"text/plain":"<Figure size 792x504 with 1 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\n"},"output_type":"display_data"}],"source":"# Calculating the proportion of female laureates per decade\nnobel['female_winner'] = True if 'sex' == 'Female' else False\nprop_female_winners = nobel.groupby(by = ['decade','category'],as_index= False)['female_winner'].mean()\n\n# Plotting USA born winners with % winners on the y-axis\n# ... YOUR CODE FOR TASK 5 ...\nplt.rcParams['figure.figsize'] = [11, 7]\n\n# Plotting USA born winners \nax = sns.lineplot('decade','female_winner',hue = 'category',data =prop_female_winners)\n\n# Adding %-formatting to the y-axis\nfrom matplotlib.ticker import PercentFormatter\n# ... YOUR CODE FOR TASK 4 ...\nax.yaxis.set_major_formatter(PercentFormatter())"},{"cell_type":"markdown","metadata":{"dc":{"key":"38"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 6. The first woman to win the Nobel Prize\n<p>The plot above is a bit messy as the lines are overplotting. But it does show some interesting trends and patterns. Overall the imbalance is pretty large with physics, economics, and chemistry having the largest imbalance. Medicine has a somewhat positive trend, and since the 1990s the literature prize is also now more balanced. The big outlier is the peace prize during the 2010s, but keep in mind that this just covers the years 2010 to 2016.</p>\n<p>Given this imbalance, who was the first woman to receive a Nobel Prize? And in what category?</p>"},{"cell_type":"code","metadata":{"dc":{"key":"38"},"trusted":true,"tags":["sample_code"]},"execution_count":36,"outputs":[{"metadata":{},"execution_count":36,"data":{"text/plain":"    year category                            prize  \\\n19  1903  Physics  The Nobel Prize in Physics 1903   \n\n                                           motivation prize_share  \\\n19  \"in recognition of the extraordinary services ...         1/4   \n\n    laureate_id laureate_type                    full_name  birth_date  \\\n19            6    Individual  Marie Curie, née Sklodowska  1867-11-07   \n\n   birth_city      ...           sex organization_name organization_city  \\\n19     Warsaw      ...        Female               NaN               NaN   \n\n   organization_country  death_date  death_city death_country usa_born_winner  \\\n19                  NaN  1934-07-04  Sallanches        France           False   \n\n    decade  female_winner  \n19    1900          False  \n\n[1 rows x 21 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>category</th>\n      <th>prize</th>\n      <th>motivation</th>\n      <th>prize_share</th>\n      <th>laureate_id</th>\n      <th>laureate_type</th>\n      <th>full_name</th>\n      <th>birth_date</th>\n      <th>birth_city</th>\n      <th>...</th>\n      <th>sex</th>\n      <th>organization_name</th>\n      <th>organization_city</th>\n      <th>organization_country</th>\n      <th>death_date</th>\n      <th>death_city</th>\n      <th>death_country</th>\n      <th>usa_born_winner</th>\n      <th>decade</th>\n      <th>female_winner</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19</th>\n      <td>1903</td>\n      <td>Physics</td>\n      <td>The Nobel Prize in Physics 1903</td>\n      <td>\"in recognition of the extraordinary services ...</td>\n      <td>1/4</td>\n      <td>6</td>\n      <td>Individual</td>\n      <td>Marie Curie, née Sklodowska</td>\n      <td>1867-11-07</td>\n      <td>Warsaw</td>\n      <td>...</td>\n      <td>Female</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1934-07-04</td>\n      <td>Sallanches</td>\n      <td>France</td>\n      <td>False</td>\n      <td>1900</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 21 columns</p>\n</div>"},"output_type":"execute_result"}],"source":"# Picking out the first woman to win a Nobel Prize\n# ... YOUR CODE FOR TASK 5 ...\nnobel[nobel.sex=='Female'].nsmallest(1,'year')"},{"cell_type":"markdown","metadata":{"dc":{"key":"45"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 7. Repeat laureates\n<p>For most scientists/writers/activists a Nobel Prize would be the crowning achievement of a long career. But for some people, one is just not enough, and few have gotten it more than once. Who are these lucky few? (Having won no Nobel Prize myself, I'll assume it's just about luck.)</p>"},{"cell_type":"code","metadata":{"dc":{"key":"45"},"trusted":true,"tags":["sample_code"]},"execution_count":38,"outputs":[{"metadata":{},"execution_count":38,"data":{"text/plain":"     year   category                              prize  \\\n19   1903    Physics    The Nobel Prize in Physics 1903   \n62   1911  Chemistry  The Nobel Prize in Chemistry 1911   \n89   1917      Peace         The Nobel Peace Prize 1917   \n215  1944      Peace         The Nobel Peace Prize 1944   \n278  1954  Chemistry  The Nobel Prize in Chemistry 1954   \n283  1954      Peace         The Nobel Peace Prize 1954   \n298  1956    Physics    The Nobel Prize in Physics 1956   \n306  1958  Chemistry  The Nobel Prize in Chemistry 1958   \n340  1962      Peace         The Nobel Peace Prize 1962   \n348  1963      Peace         The Nobel Peace Prize 1963   \n424  1972    Physics    The Nobel Prize in Physics 1972   \n505  1980  Chemistry  The Nobel Prize in Chemistry 1980   \n523  1981      Peace         The Nobel Peace Prize 1981   \n\n                                            motivation prize_share  \\\n19   \"in recognition of the extraordinary services ...         1/4   \n62   \"in recognition of her services to the advance...         1/1   \n89                                                 NaN         1/1   \n215                                                NaN         1/1   \n278  \"for his research into the nature of the chemi...         1/1   \n283                                                NaN         1/1   \n298  \"for their researches on semiconductors and th...         1/3   \n306  \"for his work on the structure of proteins, es...         1/1   \n340                                                NaN         1/1   \n348                                                NaN         1/2   \n424  \"for their jointly developed theory of superco...         1/3   \n505  \"for their contributions concerning the determ...         1/4   \n523                                                NaN         1/1   \n\n     laureate_id laureate_type  \\\n19             6    Individual   \n62             6    Individual   \n89           482  Organization   \n215          482  Organization   \n278          217    Individual   \n283          515  Organization   \n298           66    Individual   \n306          222    Individual   \n340          217    Individual   \n348          482  Organization   \n424           66    Individual   \n505          222    Individual   \n523          515  Organization   \n\n                                             full_name  birth_date  \\\n19                         Marie Curie, née Sklodowska  1867-11-07   \n62                         Marie Curie, née Sklodowska  1867-11-07   \n89   Comité international de la Croix Rouge (Intern...         NaN   \n215  Comité international de la Croix Rouge (Intern...         NaN   \n278                                 Linus Carl Pauling  1901-02-28   \n283  Office of the United Nations High Commissioner...         NaN   \n298                                       John Bardeen  1908-05-23   \n306                                   Frederick Sanger  1918-08-13   \n340                                 Linus Carl Pauling  1901-02-28   \n348  Comité international de la Croix Rouge (Intern...         NaN   \n424                                       John Bardeen  1908-05-23   \n505                                   Frederick Sanger  1918-08-13   \n523  Office of the United Nations High Commissioner...         NaN   \n\n       birth_city      ...           sex  \\\n19         Warsaw      ...        Female   \n62         Warsaw      ...        Female   \n89            NaN      ...           NaN   \n215           NaN      ...           NaN   \n278  Portland, OR      ...          Male   \n283           NaN      ...           NaN   \n298   Madison, WI      ...          Male   \n306     Rendcombe      ...          Male   \n340  Portland, OR      ...          Male   \n348           NaN      ...           NaN   \n424   Madison, WI      ...          Male   \n505     Rendcombe      ...          Male   \n523           NaN      ...           NaN   \n\n                                organization_name organization_city  \\\n19                                            NaN               NaN   \n62                            Sorbonne University             Paris   \n89                                            NaN               NaN   \n215                                           NaN               NaN   \n278  California Institute of Technology (Caltech)      Pasadena, CA   \n283                                           NaN               NaN   \n298                        University of Illinois        Urbana, IL   \n306                       University of Cambridge         Cambridge   \n340  California Institute of Technology (Caltech)      Pasadena, CA   \n348                                           NaN               NaN   \n424                        University of Illinois        Urbana, IL   \n505           MRC Laboratory of Molecular Biology         Cambridge   \n523                                           NaN               NaN   \n\n         organization_country  death_date   death_city  \\\n19                        NaN  1934-07-04   Sallanches   \n62                     France  1934-07-04   Sallanches   \n89                        NaN         NaN          NaN   \n215                       NaN         NaN          NaN   \n278  United States of America  1994-08-19  Big Sur, CA   \n283                       NaN         NaN          NaN   \n298  United States of America  1991-01-30   Boston, MA   \n306            United Kingdom  2013-11-19    Cambridge   \n340  United States of America  1994-08-19  Big Sur, CA   \n348                       NaN         NaN          NaN   \n424  United States of America  1991-01-30   Boston, MA   \n505            United Kingdom  2013-11-19    Cambridge   \n523                       NaN         NaN          NaN   \n\n                death_country usa_born_winner  decade  female_winner  \n19                     France           False    1900          False  \n62                     France           False    1910          False  \n89                        NaN           False    1910          False  \n215                       NaN           False    1940          False  \n278  United States of America           False    1950          False  \n283                       NaN           False    1950          False  \n298  United States of America           False    1950          False  \n306            United Kingdom           False    1950          False  \n340  United States of America           False    1960          False  \n348                       NaN           False    1960          False  \n424  United States of America           False    1970          False  \n505            United Kingdom           False    1980          False  \n523                       NaN           False    1980          False  \n\n[13 rows x 21 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>category</th>\n      <th>prize</th>\n      <th>motivation</th>\n      <th>prize_share</th>\n      <th>laureate_id</th>\n      <th>laureate_type</th>\n      <th>full_name</th>\n      <th>birth_date</th>\n      <th>birth_city</th>\n      <th>...</th>\n      <th>sex</th>\n      <th>organization_name</th>\n      <th>organization_city</th>\n      <th>organization_country</th>\n      <th>death_date</th>\n      <th>death_city</th>\n      <th>death_country</th>\n      <th>usa_born_winner</th>\n      <th>decade</th>\n      <th>female_winner</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19</th>\n      <td>1903</td>\n      <td>Physics</td>\n      <td>The Nobel Prize in Physics 1903</td>\n      <td>\"in recognition of the extraordinary services ...</td>\n      <td>1/4</td>\n      <td>6</td>\n      <td>Individual</td>\n      <td>Marie Curie, née Sklodowska</td>\n      <td>1867-11-07</td>\n      <td>Warsaw</td>\n      <td>...</td>\n      <td>Female</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1934-07-04</td>\n      <td>Sallanches</td>\n      <td>France</td>\n      <td>False</td>\n      <td>1900</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>1911</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 1911</td>\n      <td>\"in recognition of her services to the advance...</td>\n      <td>1/1</td>\n      <td>6</td>\n      <td>Individual</td>\n      <td>Marie Curie, née Sklodowska</td>\n      <td>1867-11-07</td>\n      <td>Warsaw</td>\n      <td>...</td>\n      <td>Female</td>\n      <td>Sorbonne University</td>\n      <td>Paris</td>\n      <td>France</td>\n      <td>1934-07-04</td>\n      <td>Sallanches</td>\n      <td>France</td>\n      <td>False</td>\n      <td>1910</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>1917</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1917</td>\n      <td>NaN</td>\n      <td>1/1</td>\n      <td>482</td>\n      <td>Organization</td>\n      <td>Comité international de la Croix Rouge (Intern...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>1910</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>215</th>\n      <td>1944</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1944</td>\n      <td>NaN</td>\n      <td>1/1</td>\n      <td>482</td>\n      <td>Organization</td>\n      <td>Comité international de la Croix Rouge (Intern...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>1940</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>278</th>\n      <td>1954</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 1954</td>\n      <td>\"for his research into the nature of the chemi...</td>\n      <td>1/1</td>\n      <td>217</td>\n      <td>Individual</td>\n      <td>Linus Carl Pauling</td>\n      <td>1901-02-28</td>\n      <td>Portland, OR</td>\n      <td>...</td>\n      <td>Male</td>\n      <td>California Institute of Technology (Caltech)</td>\n      <td>Pasadena, CA</td>\n      <td>United States of America</td>\n      <td>1994-08-19</td>\n      <td>Big Sur, CA</td>\n      <td>United States of America</td>\n      <td>False</td>\n      <td>1950</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>283</th>\n      <td>1954</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1954</td>\n      <td>NaN</td>\n      <td>1/1</td>\n      <td>515</td>\n      <td>Organization</td>\n      <td>Office of the United Nations High Commissioner...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>1950</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>298</th>\n      <td>1956</td>\n      <td>Physics</td>\n      <td>The Nobel Prize in Physics 1956</td>\n      <td>\"for their researches on semiconductors and th...</td>\n      <td>1/3</td>\n      <td>66</td>\n      <td>Individual</td>\n      <td>John Bardeen</td>\n      <td>1908-05-23</td>\n      <td>Madison, WI</td>\n      <td>...</td>\n      <td>Male</td>\n      <td>University of Illinois</td>\n      <td>Urbana, IL</td>\n      <td>United States of America</td>\n      <td>1991-01-30</td>\n      <td>Boston, MA</td>\n      <td>United States of America</td>\n      <td>False</td>\n      <td>1950</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>306</th>\n      <td>1958</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 1958</td>\n      <td>\"for his work on the structure of proteins, es...</td>\n      <td>1/1</td>\n      <td>222</td>\n      <td>Individual</td>\n      <td>Frederick Sanger</td>\n      <td>1918-08-13</td>\n      <td>Rendcombe</td>\n      <td>...</td>\n      <td>Male</td>\n      <td>University of Cambridge</td>\n      <td>Cambridge</td>\n      <td>United Kingdom</td>\n      <td>2013-11-19</td>\n      <td>Cambridge</td>\n      <td>United Kingdom</td>\n      <td>False</td>\n      <td>1950</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>340</th>\n      <td>1962</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1962</td>\n      <td>NaN</td>\n      <td>1/1</td>\n      <td>217</td>\n      <td>Individual</td>\n      <td>Linus Carl Pauling</td>\n      <td>1901-02-28</td>\n      <td>Portland, OR</td>\n      <td>...</td>\n      <td>Male</td>\n      <td>California Institute of Technology (Caltech)</td>\n      <td>Pasadena, CA</td>\n      <td>United States of America</td>\n      <td>1994-08-19</td>\n      <td>Big Sur, CA</td>\n      <td>United States of America</td>\n      <td>False</td>\n      <td>1960</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>348</th>\n      <td>1963</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1963</td>\n      <td>NaN</td>\n      <td>1/2</td>\n      <td>482</td>\n      <td>Organization</td>\n      <td>Comité international de la Croix Rouge (Intern...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>1960</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>424</th>\n      <td>1972</td>\n      <td>Physics</td>\n      <td>The Nobel Prize in Physics 1972</td>\n      <td>\"for their jointly developed theory of superco...</td>\n      <td>1/3</td>\n      <td>66</td>\n      <td>Individual</td>\n      <td>John Bardeen</td>\n      <td>1908-05-23</td>\n      <td>Madison, WI</td>\n      <td>...</td>\n      <td>Male</td>\n      <td>University of Illinois</td>\n      <td>Urbana, IL</td>\n      <td>United States of America</td>\n      <td>1991-01-30</td>\n      <td>Boston, MA</td>\n      <td>United States of America</td>\n      <td>False</td>\n      <td>1970</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <td>1980</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 1980</td>\n      <td>\"for their contributions concerning the determ...</td>\n      <td>1/4</td>\n      <td>222</td>\n      <td>Individual</td>\n      <td>Frederick Sanger</td>\n      <td>1918-08-13</td>\n      <td>Rendcombe</td>\n      <td>...</td>\n      <td>Male</td>\n      <td>MRC Laboratory of Molecular Biology</td>\n      <td>Cambridge</td>\n      <td>United Kingdom</td>\n      <td>2013-11-19</td>\n      <td>Cambridge</td>\n      <td>United Kingdom</td>\n      <td>False</td>\n      <td>1980</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>523</th>\n      <td>1981</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 1981</td>\n      <td>NaN</td>\n      <td>1/1</td>\n      <td>515</td>\n      <td>Organization</td>\n      <td>Office of the United Nations High Commissioner...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>1980</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>13 rows × 21 columns</p>\n</div>"},"output_type":"execute_result"}],"source":"# Selecting the laureates that have received 2 or more prizes.\n# ... YOUR CODE FOR TASK 5 ...\nnobel.groupby('full_name').filter(lambda x: len(x)>1)"},{"cell_type":"markdown","metadata":{"dc":{"key":"52"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 8. How old are you when you get the prize?\n<p>The list of repeat winners contains some illustrious names! We again meet Marie Curie, who got the prize in physics for discovering radiation and in chemistry for isolating radium and polonium. John Bardeen got it twice in physics for transistors and superconductivity, Frederick Sanger got it twice in chemistry, and Linus Carl Pauling got it first in chemistry and later in peace for his work in promoting nuclear disarmament. We also learn that organizations also get the prize as both the Red Cross and the UNHCR have gotten it twice.</p>\n<p>But how old are you generally when you get the prize?</p>"},{"cell_type":"code","metadata":{"dc":{"key":"52"},"trusted":true,"tags":["sample_code"]},"execution_count":40,"outputs":[{"metadata":{},"execution_count":40,"data":{"text/plain":"<seaborn.axisgrid.FacetGrid at 0x7f8ef0807d30>"},"output_type":"execute_result"},{"metadata":{},"data":{"text/plain":"<Figure size 720x360 with 1 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\n"},"output_type":"display_data"}],"source":"# Converting birth_date from String to datetime\nnobel['birth_date'] = pd.to_datetime(nobel['birth_date'])\n\n# Calculating the age of Nobel Prize winners\nnobel['age'] = nobel.year-nobel['birth_date'].dt.year\n\n# Plotting the age of Nobel Prize winners\nsns.lmplot(x = 'year', y = 'age',data = nobel, lowess=True, aspect=2, line_kws={'color' : 'black'})"},{"cell_type":"markdown","metadata":{"dc":{"key":"59"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 9. Age differences between prize categories\n<p>The plot above shows us a lot! We see that people use to be around 55 when they received the price, but nowadays the average is closer to 65. But there is a large spread in the laureates' ages, and while most are 50+, some are very young.</p>\n<p>We also see that the density of points is much high nowadays than in the early 1900s -- nowadays many more of the prizes are shared, and so there are many more winners. We also see that there was a disruption in awarded prizes around the Second World War (1939 - 1945). </p>\n<p>Let's look at age trends within different prize categories.</p>"},{"cell_type":"code","metadata":{"dc":{"key":"59"},"trusted":true,"tags":["sample_code"]},"execution_count":42,"outputs":[{"metadata":{},"execution_count":42,"data":{"text/plain":"<seaborn.axisgrid.FacetGrid at 0x7f8ef076e9b0>"},"output_type":"execute_result"},{"metadata":{},"data":{"text/plain":"<Figure size 720x2160 with 6 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\n"},"output_type":"display_data"}],"source":"# Same plot as above, but separate plots for each type of Nobel Prize\n# ... YOUR CODE FOR TASK 9 ...\nsns.lmplot(x = 'year', y = 'age',row = 'category',data = nobel, lowess=True, aspect=2, line_kws={'color' : 'black'})"},{"cell_type":"markdown","metadata":{"dc":{"key":"66"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 10. Oldest and youngest winners\n<p>More plots with lots of exciting stuff going on! We see that both winners of the chemistry, medicine, and physics prize have gotten older over time. The trend is strongest for physics: the average age used to be below 50, and now it's almost 70. Literature and economics are more stable. We also see that economics is a newer category. But peace shows an opposite trend where winners are getting younger! </p>\n<p>In the peace category we also a winner around 2010 that seems exceptionally young. This begs the questions, who are the oldest and youngest people ever to have won a Nobel Prize?</p>"},{"cell_type":"code","metadata":{"dc":{"key":"66"},"trusted":true,"tags":["sample_code"]},"execution_count":44,"outputs":[{"metadata":{},"execution_count":44,"data":{"text/plain":"     year category                       prize  \\\n885  2014    Peace  The Nobel Peace Prize 2014   \n\n                                            motivation prize_share  \\\n885  \"for their struggle against the suppression of...         1/2   \n\n     laureate_id laureate_type         full_name birth_date birth_city  ...   \\\n885          914    Individual  Malala Yousafzai 1997-07-12    Mingora  ...    \n\n    organization_name organization_city organization_country death_date  \\\n885               NaN               NaN                  NaN        NaN   \n\n    death_city death_country usa_born_winner decade  female_winner   age  \n885        NaN           NaN           False   2010          False  17.0  \n\n[1 rows x 22 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>category</th>\n      <th>prize</th>\n      <th>motivation</th>\n      <th>prize_share</th>\n      <th>laureate_id</th>\n      <th>laureate_type</th>\n      <th>full_name</th>\n      <th>birth_date</th>\n      <th>birth_city</th>\n      <th>...</th>\n      <th>organization_name</th>\n      <th>organization_city</th>\n      <th>organization_country</th>\n      <th>death_date</th>\n      <th>death_city</th>\n      <th>death_country</th>\n      <th>usa_born_winner</th>\n      <th>decade</th>\n      <th>female_winner</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>885</th>\n      <td>2014</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 2014</td>\n      <td>\"for their struggle against the suppression of...</td>\n      <td>1/2</td>\n      <td>914</td>\n      <td>Individual</td>\n      <td>Malala Yousafzai</td>\n      <td>1997-07-12</td>\n      <td>Mingora</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>2010</td>\n      <td>False</td>\n      <td>17.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 22 columns</p>\n</div>"},"output_type":"execute_result"}],"source":"# The oldest winner of a Nobel Prize as of 2016\n# ... YOUR CODE FOR TASK 10 ...\nnobel.nlargest(1,'age')\n\n# The youngest winner of a Nobel Prize as of 2016\n# ... YOUR CODE FOR TASK 10 ...\nnobel.nsmallest(1,'age')"},{"cell_type":"markdown","metadata":{"dc":{"key":"73"},"run_control":{"frozen":true},"deletable":false,"editable":false,"tags":["context"]},"source":"## 11. You get a prize!\n<p><img style=\"float: right;margin:20px 20px 20px 20px; max-width:200px\" src=\"https://assets.datacamp.com/production/project_441/img/paint_nobel_prize.png\"></p>\n<p>Hey! You get a prize for making it to the very end of this notebook! It might not be a Nobel Prize, but I made it myself in paint so it should count for something. But don't despair, Leonid Hurwicz was 90 years old when he got his prize, so it might not be too late for you. Who knows.</p>\n<p>Before you leave, what was again the name of the youngest winner ever who in 2014 got the prize for \"[her] struggle against the suppression of children and young people and for the right of all children to education\"?</p>"},{"cell_type":"code","metadata":{"dc":{"key":"73"},"trusted":true,"collapsed":true,"tags":["sample_code"]},"execution_count":46,"outputs":[],"source":"# The name of the youngest winner of the Nobel Prize as of 2016\nyoungest_winner = nobel.nsmallest(1,'age')['full_name']"}],"metadata":{"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","mimetype":"text/x-python","version":"3.5.2","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python"},"kernelspec":{"name":"python3","language":"python","display_name":"Python 3"}},"nbformat":4,"nbformat_minor":2}