{
 "cells": [
  {
   "source": [
    "The Nobel Prize has been among the most prestigious international awards since 1901. Each year, awards are bestowed in chemistry, literature, physics, physiology or medicine, economics, and peace. In addition to the honor, prestige, and substantial prize money, the recipient also gets a gold medal with an image of Alfred Nobel (1833 - 1896), who established the prize.\n",
    "\n",
    "The Nobel Foundation has made a dataset available of all prize winners from the outset of the awards from 1901 to 2023. The dataset used in this project is from the Nobel Prize API and is available in the `nobel.csv` file in the `data` folder.\n",
    "\n",
    "In this project, you'll get a chance to explore and answer several questions related to this prizewinning data. And we encourage you then to explore further questions that you're interested in!"
   ],
   "metadata": {},
   "id": "db5bd2ce-918a-4f7d-a927-a3ea74c4b456",
   "cell_type": "markdown"
  },
  {
   "source": [
    "# Loading in required libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Start coding here!\n",
    "nobel = pd.read_csv(\"../datasets/nobel.csv\")\n",
    "nobel"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 73,
    "lastExecutedAt": 1703989796447,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Loading in required libraries\nimport pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Start coding here!\nnobel = pd.read_csv(\"data/nobel.csv\")\nnobel",
    "outputsMetadata": {
     "0": {
      "height": 333,
      "type": "dataFrame"
     }
    },
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:37.979218400Z",
     "start_time": "2023-12-31T04:25:37.879408200Z"
    }
   },
   "id": "11fd5946-7bd1-495c-aa7f-ff6e7e4a350b",
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "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   \n..    ...         ...                                                ...   \n995  2023   Chemistry                  The Nobel Prize in Chemistry 2023   \n996  2023   Chemistry                  The Nobel Prize in Chemistry 2023   \n997  2023  Literature                 The Nobel Prize in Literature 2023   \n998  2023       Peace                         The Nobel Peace Prize 2023   \n999  2023   Economics  The Sveriges Riksbank Prize in Economic Scienc...   \n\n                                            motivation prize_share  \\\n0    \"in recognition of the extraordinary services ...         1/1   \n1    \"in special recognition of his poetic composit...         1/1   \n2    \"for his work on serum therapy, especially its...         1/1   \n3                                                  NaN         1/2   \n4                                                  NaN         1/2   \n..                                                 ...         ...   \n995  \"for the discovery and synthesis of quantum dots\"         1/3   \n996  \"for the discovery and synthesis of quantum dots\"         1/3   \n997  \"for his innovative plays and prose which give...         1/1   \n998  \"for her fight against the oppression of women...         1/1   \n999  \"for having advanced our understanding of wome...         1/1   \n\n     laureate_id laureate_type                     full_name  birth_date  \\\n0            160    Individual  Jacobus Henricus van 't Hoff  1852-08-30   \n1            569    Individual               Sully Prudhomme  1839-03-16   \n2            293    Individual        Emil Adolf von Behring  1854-03-15   \n3            462    Individual             Jean Henry Dunant  1828-05-08   \n4            463    Individual                Frédéric Passy  1822-05-20   \n..           ...           ...                           ...         ...   \n995         1030    Individual                    Louis Brus  1943-00-00   \n996         1031    Individual               Aleksey Yekimov  1945-00-00   \n997         1032    Individual                     Jon Fosse  1959-09-29   \n998         1033    Individual              Narges Mohammadi  1972-04-21   \n999         1034    Individual                Claudia Goldin  1946-00-00   \n\n            birth_city             birth_country     sex  \\\n0            Rotterdam               Netherlands    Male   \n1                Paris                    France    Male   \n2    Hansdorf (Lawice)          Prussia (Poland)    Male   \n3               Geneva               Switzerland    Male   \n4                Paris                    France    Male   \n..                 ...                       ...     ...   \n995      Cleveland, OH  United States of America    Male   \n996                NaN         USSR (now Russia)    Male   \n997          Haugesund                    Norway    Male   \n998             Zanjan                      Iran  Female   \n999       New York, NY  United States of America  Female   \n\n                organization_name organization_city      organization_country  \\\n0               Berlin University            Berlin                   Germany   \n1                             NaN               NaN                       NaN   \n2              Marburg University           Marburg                   Germany   \n3                             NaN               NaN                       NaN   \n4                             NaN               NaN                       NaN   \n..                            ...               ...                       ...   \n995           Columbia University      New York, NY  United States of America   \n996  Nanocrystals Technology Inc.      New York, NY  United States of America   \n997                           NaN               NaN                       NaN   \n998                           NaN               NaN                       NaN   \n999            Harvard University     Cambridge, MA  United States of America   \n\n     death_date death_city death_country  \n0    1911-03-01     Berlin       Germany  \n1    1907-09-07   Châtenay        France  \n2    1917-03-31    Marburg       Germany  \n3    1910-10-30     Heiden   Switzerland  \n4    1912-06-12      Paris        France  \n..          ...        ...           ...  \n995         NaN        NaN           NaN  \n996         NaN        NaN           NaN  \n997         NaN        NaN           NaN  \n998         NaN        NaN           NaN  \n999         NaN        NaN           NaN  \n\n[1000 rows x 18 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>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>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>2023</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 2023</td>\n      <td>\"for the discovery and synthesis of quantum dots\"</td>\n      <td>1/3</td>\n      <td>1030</td>\n      <td>Individual</td>\n      <td>Louis Brus</td>\n      <td>1943-00-00</td>\n      <td>Cleveland, OH</td>\n      <td>United States of America</td>\n      <td>Male</td>\n      <td>Columbia University</td>\n      <td>New York, NY</td>\n      <td>United States of America</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>2023</td>\n      <td>Chemistry</td>\n      <td>The Nobel Prize in Chemistry 2023</td>\n      <td>\"for the discovery and synthesis of quantum dots\"</td>\n      <td>1/3</td>\n      <td>1031</td>\n      <td>Individual</td>\n      <td>Aleksey Yekimov</td>\n      <td>1945-00-00</td>\n      <td>NaN</td>\n      <td>USSR (now Russia)</td>\n      <td>Male</td>\n      <td>Nanocrystals Technology Inc.</td>\n      <td>New York, NY</td>\n      <td>United States of America</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>2023</td>\n      <td>Literature</td>\n      <td>The Nobel Prize in Literature 2023</td>\n      <td>\"for his innovative plays and prose which give...</td>\n      <td>1/1</td>\n      <td>1032</td>\n      <td>Individual</td>\n      <td>Jon Fosse</td>\n      <td>1959-09-29</td>\n      <td>Haugesund</td>\n      <td>Norway</td>\n      <td>Male</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    </tr>\n    <tr>\n      <th>998</th>\n      <td>2023</td>\n      <td>Peace</td>\n      <td>The Nobel Peace Prize 2023</td>\n      <td>\"for her fight against the oppression of women...</td>\n      <td>1/1</td>\n      <td>1033</td>\n      <td>Individual</td>\n      <td>Narges Mohammadi</td>\n      <td>1972-04-21</td>\n      <td>Zanjan</td>\n      <td>Iran</td>\n      <td>Female</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    </tr>\n    <tr>\n      <th>999</th>\n      <td>2023</td>\n      <td>Economics</td>\n      <td>The Sveriges Riksbank Prize in Economic Scienc...</td>\n      <td>\"for having advanced our understanding of wome...</td>\n      <td>1/1</td>\n      <td>1034</td>\n      <td>Individual</td>\n      <td>Claudia Goldin</td>\n      <td>1946-00-00</td>\n      <td>New York, NY</td>\n      <td>United States of America</td>\n      <td>Female</td>\n      <td>Harvard University</td>\n      <td>Cambridge, MA</td>\n      <td>United States of America</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 18 columns</p>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "source": [
    "**Question 1:** What is the most commonly awarded gender and birth country?"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "f4947351-85ed-4341-b00a-86b8296b0fc7"
  },
  {
   "source": [
    "Solution with Pandas"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "fc8ee690-bc8d-40d6-8a24-4e5e388500cc"
  },
  {
   "source": [
    "genders = nobel.value_counts(\"sex\").sort_values(ascending=False)\n",
    "genders"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 50,
    "lastExecutedAt": 1703989796497,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "genders = nobel.value_counts(\"sex\").sort_values(ascending=False)\ngenders",
    "visualizeDataframe": false,
    "chartConfig": {
     "bar": {
      "hasRoundedCorners": true,
      "stacked": false
     },
     "type": "bar",
     "version": "v1"
    },
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.008535600Z",
     "start_time": "2023-12-31T04:25:37.963905100Z"
    }
   },
   "cell_type": "code",
   "id": "e7ea86da-241e-4ea5-baf6-dde7c9e81fbf",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "sex\nMale      905\nFemale     65\nName: count, dtype: int64"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "source": [
    "countries = nobel.value_counts(\"birth_country\").sort_values(ascending=False)\n",
    "countries.head(5)"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 53,
    "lastExecutedAt": 1703989796550,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "countries = nobel.value_counts(\"birth_country\").sort_values(ascending=False)\ncountries.head(5)",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.073367700Z",
     "start_time": "2023-12-31T04:25:37.985236200Z"
    }
   },
   "cell_type": "code",
   "id": "2a919d4e-a356-40e4-9c7a-dc7a1e9aee9c",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "birth_country\nUnited States of America    291\nUnited Kingdom               91\nGermany                      67\nFrance                       58\nSweden                       30\nName: count, dtype: int64"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "source": [
    "Solution with Seaborn"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "373b0fd7-8e87-4dc3-9e73-d6958e8c0544"
  },
  {
   "source": [
    "sns.countplot(x='sex',data=nobel)"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 115,
    "lastExecutedAt": 1703989796666,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "sns.countplot(x='sex',data=nobel)",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.148478800Z",
     "start_time": "2023-12-31T04:25:38.013820400Z"
    }
   },
   "cell_type": "code",
   "id": "c130b129-085b-4f60-91a5-52eb2d661803",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='sex', ylabel='count'>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "source": [
    "top_gender = 'Male'\n",
    "top_country = 'United States of America'"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1703989796717,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "top_gender = 'Male'\ntop_country = 'United States of America'",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.150141300Z",
     "start_time": "2023-12-31T04:25:38.120300700Z"
    }
   },
   "cell_type": "code",
   "id": "495c4d8d-23f3-4721-b70f-f072552e7d21",
   "execution_count": 30,
   "outputs": []
  },
  {
   "source": [
    "**Question 2:** What decade had te highest propotion of US-born winners?"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "04888ce4-206c-434d-a582-d04ebe979205"
  },
  {
   "source": [
    "nobel[\"decade\"] = nobel[\"year\"].apply(lambda x: x // 10 * 10)\n",
    "nobel[\"is_usa\"] = nobel[\"birth_country\"].apply(lambda x: 1 if x == 'United States of America' else 0)\n",
    "nobels_proportion_usa = nobel.groupby(\"decade\", as_index=False)[\"is_usa\"].mean()\n",
    "nobels_proportion_usa"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1703989796770,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "nobel[\"decade\"] = nobel[\"year\"].apply(lambda x: x // 10 * 10)\nnobel[\"is_usa\"] = nobel[\"birth_country\"].apply(lambda x: 1 if x == 'United States of America' else 0)\nnobels_proportion_usa = nobel.groupby(\"decade\", as_index=False)[\"is_usa\"].mean()\nnobels_proportion_usa",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.161449400Z",
     "start_time": "2023-12-31T04:25:38.128845Z"
    }
   },
   "cell_type": "code",
   "id": "cf7d78ff-d604-49cc-b228-4346a0d6adda",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "    decade    is_usa\n0     1900  0.017544\n1     1910  0.075000\n2     1920  0.074074\n3     1930  0.250000\n4     1940  0.302326\n5     1950  0.291667\n6     1960  0.265823\n7     1970  0.317308\n8     1980  0.319588\n9     1990  0.403846\n10    2000  0.422764\n11    2010  0.314050\n12    2020  0.360000",
      "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>decade</th>\n      <th>is_usa</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1900</td>\n      <td>0.017544</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1910</td>\n      <td>0.075000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1920</td>\n      <td>0.074074</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1930</td>\n      <td>0.250000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1940</td>\n      <td>0.302326</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1950</td>\n      <td>0.291667</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1960</td>\n      <td>0.265823</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1970</td>\n      <td>0.317308</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1980</td>\n      <td>0.319588</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1990</td>\n      <td>0.403846</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2000</td>\n      <td>0.422764</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2010</td>\n      <td>0.314050</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2020</td>\n      <td>0.360000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "source": [
    "ax = sns.barplot(data=nobels_proportion_usa,x='decade',y='is_usa')\n",
    "ax.set_title(\"Proportion of USA nobels per decade\")\n",
    "ax.set_ylabel(\"Proportion of Nobel prizes earned\")\n",
    "ax.set_xlabel(\"Decade\");"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 265,
    "lastExecutedAt": 1703989797035,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "ax = sns.barplot(data=nobels_proportion_usa,x='decade',y='is_usa')\nax.set_title(\"Proportion of USA nobels per decade\")\nax.set_ylabel(\"Proportion of Nobel prizes earned\")\nax.set_xlabel(\"Decade\");",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.451524100Z",
     "start_time": "2023-12-31T04:25:38.156055800Z"
    }
   },
   "cell_type": "code",
   "id": "18fedcf2-23be-44ae-8033-2218228d6ea3",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "source": [
    "max_decade_usa = 2000"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 46,
    "lastExecutedAt": 1703989797081,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "max_decade_usa = 2000",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:38.458094Z",
     "start_time": "2023-12-31T04:25:38.453558700Z"
    }
   },
   "cell_type": "code",
   "id": "2b962617-c44b-4512-a3c6-b83ea1f08424",
   "execution_count": 33,
   "outputs": []
  },
  {
   "source": [
    "**Question 3:** What decade and category pair had the highest proportion of female laureates?"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "e32a35cc-30a8-4e23-8a89-b0319a8d08c9"
  },
  {
   "source": [
    "nobel[\"is_female\"] = nobel[\"sex\"].apply(lambda x: 1 if x == 'Female' else 0)\n",
    "nobels_proportion_female = nobel.groupby([\"decade\",\"category\"],as_index=False)[\"is_female\"].mean()\n",
    "sns.catplot(kind='bar', \n",
    "            x='decade', \n",
    "            hue='category',\n",
    "            y='is_female',\n",
    "            data=nobels_proportion_female);"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 595,
    "lastExecutedAt": 1703989797677,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "nobel[\"is_female\"] = nobel[\"sex\"].apply(lambda x: 1 if x == 'Female' else 0)\nnobels_proportion_female = nobel.groupby([\"decade\",\"category\"],as_index=False)[\"is_female\"].mean()\nsns.catplot(kind='bar', \n            x='decade', \n            hue='category',\n            y='is_female',\n            data=nobels_proportion_female);",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:39.164982700Z",
     "start_time": "2023-12-31T04:25:38.458094Z"
    }
   },
   "cell_type": "code",
   "id": "70ba9d9f-bfa0-4df0-9454-a8916425db08",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 617.75x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "source": [
    "max_female_dict = {2020:\"Literature\"}"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 47,
    "lastExecutedAt": 1703989797725,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "max_female_dict = {2020:\"Literature\"}",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:39.172328Z",
     "start_time": "2023-12-31T04:25:39.163307500Z"
    }
   },
   "cell_type": "code",
   "id": "cb94fd05-e6cf-4fa5-a070-8abbff4e7e5d",
   "execution_count": 35,
   "outputs": []
  },
  {
   "source": [
    "**Question 4:** Who was the first woman to receive a Nobel Prize, and in what category?"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "0b426ebb-a665-47a1-9c2c-2aec444cbb99"
  },
  {
   "source": [
    "nobel_first_woman = nobel[nobel[\"sex\"]==\"Female\"]\\\n",
    "                    .sort_values([\"year\"], ascending=True)\\\n",
    "                    .iloc[0]\n",
    "nobel_first_woman"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1703989797777,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "nobel_first_woman = nobels_female.sort_values([\"year\"], ascending=True).iloc[0]\nnobel_first_woman",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:39.180164400Z",
     "start_time": "2023-12-31T04:25:39.168477200Z"
    }
   },
   "cell_type": "code",
   "id": "4f74637a-ce90-45f3-bfdf-a857e8195af1",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "year                                                                 1903\ncategory                                                          Physics\nprize                                     The Nobel Prize in Physics 1903\nmotivation              \"in recognition of the extraordinary services ...\nprize_share                                                           1/4\nlaureate_id                                                             6\nlaureate_type                                                  Individual\nfull_name                                     Marie Curie, née Sklodowska\nbirth_date                                                     1867-11-07\nbirth_city                                                         Warsaw\nbirth_country                                     Russian Empire (Poland)\nsex                                                                Female\norganization_name                                                     NaN\norganization_city                                                     NaN\norganization_country                                                  NaN\ndeath_date                                                     1934-07-04\ndeath_city                                                     Sallanches\ndeath_country                                                      France\ndecade                                                               1900\nis_usa                                                                  0\nis_female                                                               1\nName: 19, dtype: object"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "source": [
    "first_woman_name = \"Marie Curie, née Sklodowska\"\n",
    "first_woman_category = \"Physics\""
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 48,
    "lastExecutedAt": 1703989797825,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "first_woman_name = \"Marie Curie, née Sklodowska\"\nfirst_woman_category = \"Physics\"",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:39.185653900Z",
     "start_time": "2023-12-31T04:25:39.178577700Z"
    }
   },
   "cell_type": "code",
   "id": "f4842259-2db2-4470-b954-9e07cccde387",
   "execution_count": 37,
   "outputs": []
  },
  {
   "source": [
    "**Quesiton 5** Which individuals or organizations have won multiple Nobel Prizes throughout the years?"
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "f3fad5f5-859c-494c-86cd-0e896d1b5acf"
  },
  {
   "source": [
    "amount_nobels = nobel.value_counts(\"full_name\")\n",
    "multiple_nobels = amount_nobels[amount_nobels > 1]\n",
    "repeat_list = multiple_nobels.index.tolist()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1703989797877,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "amount_nobels = nobel.value_counts(\"full_name\")\nmultiple_nobels = amount_nobels[amount_nobels > 1]\nrepeat_list = multiple_nobels.index.tolist()",
    "ExecuteTime": {
     "end_time": "2023-12-31T04:25:39.245234700Z",
     "start_time": "2023-12-31T04:25:39.183660200Z"
    }
   },
   "cell_type": "code",
   "id": "40c234de-4aa3-46cd-9217-a0d3c5ef51a4",
   "execution_count": 38,
   "outputs": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "version": "3.8.10",
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   "codemirror_mode": {
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    "version": 3
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   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "editor": "DataCamp Workspace"
 },
 "nbformat": 4,
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}