01_dataexplore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis in Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keyboard shortcuts available in Edit and Command modes\n",
    "* `Enter` key to enter Edit mode (`Escape` to enter Command mode)\n",
    "* `Ctrl`-`Enter`: run the cell\n",
    "* `Shift`-`Enter`: run the cell and select the cell below\n",
    "* `Alt`-`Enter`: run the cell and insert a new cell below\n",
    "* `Ctrl`-`s`: save the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful keyboard shortcuts in **Command mode**\n",
    " - `Tab` key for code completion or indentation\n",
    " - `m` and `y` to toggle between Markdown and Code cells\n",
    " - `d-d` to delete a cell\n",
    " - `z` to undo deleting\n",
    " - `a/b` to insert cells above/below current cell\n",
    " - `x/c/v` to cut/copy/paste cells\n",
    " - `Up/Down` or `k/j` to select previous/next cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Some of the following material has been adapted from an example in the [IPython Cookbook](http://ipython-books.github.io/), by Cyrille Rossant, Packt Publishing, 2014.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with three important Python packages\n",
    "- `matplotlib` is the standard Python package for plotting, \"grandfather of Python visualization packages\"\n",
    "- `numpy` is the fundamental package for scientific computing with Python\n",
    "- `pandas` is a more recently developed package for data manipulation and analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will download and process a dataset about attendance on Montreal's bicycle tracks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to import the python modules that will be used\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location of dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = \"https://github.com/ipython-books/cookbook-data/raw/master/bikes.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas defines a `read_csv` function that can read any CSV file. By giving the URL to the file, Pandas will automatically download and parse the file, and return a `DataFrame` object. We need to specify a few options to make sure the dates are parsed correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df = pd.read_csv(url, index_col='Date', parse_dates=True, dayfirst=True)\n",
    "df = pd.read_csv(\"data/bikes.csv\", index_col='Date', parse_dates=True, dayfirst=True) # in case of internet problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `df` variable now contains a `DataFrame` object, a Pandas data structure that contains 2D tabular data. The `head(n)` method displays the first `n` rows of this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "strip_output": [
     0,
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Berri1</th>\n",
       "      <th>CSC</th>\n",
       "      <th>Mais1</th>\n",
       "      <th>Mais2</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>Totem_Laurier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>00:00</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>00:00</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Unnamed: 1  Berri1  CSC  Mais1  Mais2  Parc  PierDup  Rachel1  \\\n",
       "Date                                                                       \n",
       "2013-01-01      00:00       0    0      1      0     6        0        1   \n",
       "2013-01-02      00:00      69    0     13      0    18        0        2   \n",
       "2013-01-03      00:00      69    2     21      6    22        1        0   \n",
       "2013-01-04      00:00      80    0     22      4    16        0      125   \n",
       "2013-01-05      00:00      52    4     19      3    12        0       97   \n",
       "\n",
       "            Totem_Laurier  \n",
       "Date                       \n",
       "2013-01-01              0  \n",
       "2013-01-02              0  \n",
       "2013-01-03              0  \n",
       "2013-01-04              0  \n",
       "2013-01-05              0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every row contains the number of bicycles on every track of the city, for every day of the year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get some summary statistics of the table with the `describe` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "strip_output": [
     0,
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri1</th>\n",
       "      <th>CSC</th>\n",
       "      <th>Mais1</th>\n",
       "      <th>Mais2</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>Totem_Laurier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2743.390805</td>\n",
       "      <td>1221.858238</td>\n",
       "      <td>1757.590038</td>\n",
       "      <td>3224.130268</td>\n",
       "      <td>1669.425287</td>\n",
       "      <td>1152.885057</td>\n",
       "      <td>3084.425287</td>\n",
       "      <td>1858.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2247.957848</td>\n",
       "      <td>1070.037364</td>\n",
       "      <td>1458.793882</td>\n",
       "      <td>2589.514354</td>\n",
       "      <td>1363.738862</td>\n",
       "      <td>1208.848429</td>\n",
       "      <td>2380.255540</td>\n",
       "      <td>1434.899574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>392.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>516.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2771.000000</td>\n",
       "      <td>1184.000000</td>\n",
       "      <td>1706.000000</td>\n",
       "      <td>3178.000000</td>\n",
       "      <td>1584.000000</td>\n",
       "      <td>818.000000</td>\n",
       "      <td>3111.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4767.000000</td>\n",
       "      <td>2168.000000</td>\n",
       "      <td>3158.000000</td>\n",
       "      <td>5812.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>2104.000000</td>\n",
       "      <td>5338.000000</td>\n",
       "      <td>3168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6803.000000</td>\n",
       "      <td>3330.000000</td>\n",
       "      <td>4716.000000</td>\n",
       "      <td>7684.000000</td>\n",
       "      <td>4103.000000</td>\n",
       "      <td>4841.000000</td>\n",
       "      <td>8555.000000</td>\n",
       "      <td>4293.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri1          CSC        Mais1        Mais2         Parc  \\\n",
       "count   261.000000   261.000000   261.000000   261.000000   261.000000   \n",
       "mean   2743.390805  1221.858238  1757.590038  3224.130268  1669.425287   \n",
       "std    2247.957848  1070.037364  1458.793882  2589.514354  1363.738862   \n",
       "min       0.000000     0.000000     1.000000     0.000000     6.000000   \n",
       "25%     392.000000    12.000000   236.000000   516.000000   222.000000   \n",
       "50%    2771.000000  1184.000000  1706.000000  3178.000000  1584.000000   \n",
       "75%    4767.000000  2168.000000  3158.000000  5812.000000  3068.000000   \n",
       "max    6803.000000  3330.000000  4716.000000  7684.000000  4103.000000   \n",
       "\n",
       "           PierDup      Rachel1  Totem_Laurier  \n",
       "count   261.000000   261.000000     261.000000  \n",
       "mean   1152.885057  3084.425287    1858.793103  \n",
       "std    1208.848429  2380.255540    1434.899574  \n",
       "min       0.000000     0.000000       0.000000  \n",
       "25%      12.000000   451.000000     340.000000  \n",
       "50%     818.000000  3111.000000    2087.000000  \n",
       "75%    2104.000000  5338.000000    3168.000000  \n",
       "max    4841.000000  8555.000000    4293.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has plotting capabilities through a layer over `matplotlib`  \n",
    "Let's plot the daily attendance of two tracks. First, we select the two columns `'Berri1'` and `'PierDup'`. Then, we call the `plot` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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M3t5corajqJub2X5mn2tLi1xRi46LzPoWE8kAe9a3Mcmxulo+kXBqfRuJmtd1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\nglVkY3E6AaKxwaCh34bN9aj5htpb6FunA6oaqpARmYHk8GROUESv0QM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RUaFG5sIFWtFd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\n27nXY8YAg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wfmmFkFsB04twDx7a0+WV3fC2LaJcCnZnYC8A3gUknD47qJwBXAsUC5pDwT\ndibGgMeAafGCaBSwLGv9u8BkMxsP3ADcnLVuLHCOmZ2SVLDtcDZQaWZrgY8ljY3p+b7/vsDrsWfk\nteTDzamj++Y+4GKAWHkfaGarE4u6/YzWW6FbYxnvBn6ZTEj7rK0ypV2+46dFmeLf5t3A98xsIjA4\nV74UeB44PF6k3ynpW5J6AnOAc2PsfwVuytqmj5mNJZwzHkg64DRU1AJ+J2kVoUU2VNKhcd36rBPL\nCmBEAeLbW59ldX1nLihOA6bH1tkyYBCQGbrqDTPbEIeOexSY3PIjk2Vmawjf8TTgWZr2EgwA5kta\nDdwKHJe1brGZbU8qznaaRqjkAB4HLozL+b7/BuCJZENsWwf3zXzgzHhL4qfAg0nF20mejD9XEOYL\ncJ0v3/GTyzHAuqzbfqmciN3M6oFxwKXAR4TyXQZUAIvj+fp6YGjWZo/GbZcCB0vql2TMhR6ZTMCP\ngEOAsWbWKGk9kBnDa3dW3oas9K5CwFVmtrhJonQSLa8003LluRD4PXAy8LWs9N8CL5rZObFXIGv+\nMOqTC2/vSRpI6KWpUBhEp4TwPT+bI3vm+99p6X0Vol37xsx2SloMTAHOA8YnG267fUHYRxnNj/fM\n+aCBwp+79lZbZUqtVo6fp8hfpi4xdVw8xl8BXokXuFcAa8zsxHybZC2LhM/XaWhR9yN0aTVKOoWm\nV8pdYqdHuWJdBFweu1WQNFJSZuTqE+J9+B7AVODVHNsnKRP/A8BvzKym2fr+wMa4fHFiUe2b84CH\nzOwIMzvSzIYD64FvAhObff9L4zZp/Jvbl31zP3A7oQchrb0eEE58G4DjJB0gaQBhYKSurKuXKd/x\nUwIcm6NM7wFH6Ks3XqYmH3LbJB0lqTwraQzwDjBY4UEzJPWUlN1rODWmTybczkz0LYWCXZXG7rhd\nwN+AZ2LX93LC/baMtLZscskV632E7spqSQK2Elo3EEZlu4NwH/5FM3syx/ZJyswvupEQV3OzgbmS\nfk3uFmkaTQVuaZb2BPAz4E2afv9PxfVp/Jvr8L4xs2pJtYR7bqkUzwW7zWyjpHnAGkKFUJ2VLY37\nJa8iKVOu42dBTJ8H1ADvE8tkZrskXQ4skrSDcIylsYylwBxJ/Qk9HmsJ3eD3ZqWXAH8iVOAAuyRV\nE+rMxBsqBRvwRNJo4B4zS9OTta4biLcerjGzswodS2eTNJRwIXJMoWPJpxjPBcVYpr0hqW+8B4yk\nO4H/mNltBQ5rn0h6iXC+qG4zcycpSNe3pMsILenrC/H/O9cdSPox8DpwXaFjyacYzwXFWKZ2mBHf\nfKkh3Na8p9AB7QcF7xXwIUSdc865FEvDw2TOOeecyyORilrSMEkvSqqRtFrSrJg+UNLzkt6TtCje\nxEfSoJi/TtLtzT7rudi1slrSXfEhLeecc64oJdL1LakMKDOzlZJKCQMWnE14eu5/ZjZb0q+AgWZ2\nraSDCI/MVwAVZjYr67NKzWxHXJ4PzDOzeZ1eCOecc64AEmlRm9kWM1sZl3cQXsEaRqis58Zsc4mv\nLpnZZ3H4xt05PitTSR8A9CIFN/qdc865zpL4PWpJIwit5X8BQ8zsQwiVOXBo/i2bfEYlsAWoJQyT\n6JxzzhWlRCvq2O09H7g6tow7NIymmZ1OmAHlQMIQd84551xRSqyijsNozgceNrOnY/KHWVOJlRFG\n7torZvY5Yezjs/d3rM4551xaJNmifgB4p9koNQuBn8Tli4Cnm29E1tjLkvrGCj1T8Z8J/LtTonXO\nOedSIKmnvk8kzFSymq/mZ72OMN71PODrhMHrzzezT+M264GDCQ+MfUqYMnIb8ExM60GYJejnZtbY\n6YVwzjnnCsBHJnPOOedSzEcmc84551LMK2rnnHMuxbyids4551LMK2rnnHMuxbyids4551LMK2rn\nnHMuxbyidq7ISWqQVC1pTZwi9hdtTQ8rabikaUnF6JzLzytq54pfvZmNM7MK4FTg+8ANbWxzBHBh\np0fmnGuTV9TOdSNm9jFwKXAlfNlyfkXS8vhvUsx6MzA5tsSvltRD0mxJyyStlDSjUGVwrrvxkcmc\nK3KSas2sX7O0bcDRQB3QaGafSyoHHjWziZJOAq4xs7Ni/hnAYDO7SVIv4J/AD8xsQ7Klca776Vno\nAJxzBZG5R90LuEPSGKABGJkn/2nA8ZLOi7/3i3m9onauk3lF7Vw3I+lI4Asz+0jSDcAWMxslqQTY\nmW8z4CozW5xYoM45wO9RO9cdZE8VOxj4MzAnJvUHNsfl6UBJXK4jzF6XsQi4PE4vi6SRkvp0ZtDO\nucBb1M4Vv96Sqgnd3HuAh8zs1rjuLmCBpOlAJVAf098GGiW9BTxoZrdJGgFUx1e7tgJTEiyDc92W\nP0zmnHPOpZh3fTvnnHMp5hW1c845l2JeUTvnnHMp5hW1c845l2JeUTvnnHMp5hW1c845l2JeUTvn\nnHMp5hW1c845l2L/BxCfe2ejNRo6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113199890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Berri1', 'PierDup']].plot(figsize=(8,4),\n",
    "                               style=['-', '--']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now look at the attendance of all tracks as a function of the weekday. We can get the week day easily with Pandas: the `index` attribute of the `DataFrame` contains the dates of all rows in the table. This index has a few date-related attributes, including `weekday`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2], dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we would like to have names (Monday, Tuesday, etc.) instead of numbers between 0 and 6. First, we create an array `days` with all weekday names. Then, we index it by `df.index.weekday`. This operation replaces every integer in the index by the corresponding name in `days`. The first element, `Monday`, has index 0, so every 0 in `df.index.weekday` is replaced by `Monday`, and so on. We assign this new index to a new column `Weekday` in the `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "days = np.array(['Monday', 'Tuesday', 'Wednesday', \n",
    "                 'Thursday', 'Friday', 'Saturday', \n",
    "                 'Sunday'])\n",
    "df['Weekday'] = days[df.index.weekday]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Berri1</th>\n",
       "      <th>CSC</th>\n",
       "      <th>Mais1</th>\n",
       "      <th>Mais2</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>Totem_Laurier</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>00:00</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>00:00</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Unnamed: 1  Berri1  CSC  Mais1  Mais2  Parc  PierDup  Rachel1  \\\n",
       "Date                                                                       \n",
       "2013-01-01      00:00       0    0      1      0     6        0        1   \n",
       "2013-01-02      00:00      69    0     13      0    18        0        2   \n",
       "2013-01-03      00:00      69    2     21      6    22        1        0   \n",
       "2013-01-04      00:00      80    0     22      4    16        0      125   \n",
       "2013-01-05      00:00      52    4     19      3    12        0       97   \n",
       "\n",
       "            Totem_Laurier    Weekday  \n",
       "Date                                  \n",
       "2013-01-01              0    Tuesday  \n",
       "2013-01-02              0  Wednesday  \n",
       "2013-01-03              0   Thursday  \n",
       "2013-01-04              0     Friday  \n",
       "2013-01-05              0   Saturday  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the attendance as a function of the weekday, we need to group the table by the weekday. The `groupby` method lets us do just that. Once grouped, we can sum all rows in every group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.groupby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_week = df.groupby('Weekday',sort=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "strip_output": [
     0,
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri1</th>\n",
       "      <th>CSC</th>\n",
       "      <th>Mais1</th>\n",
       "      <th>Mais2</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>Totem_Laurier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>117244</td>\n",
       "      <td>54656</td>\n",
       "      <td>76974</td>\n",
       "      <td>141217</td>\n",
       "      <td>74299</td>\n",
       "      <td>40679</td>\n",
       "      <td>123533</td>\n",
       "      <td>76559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>120434</td>\n",
       "      <td>59604</td>\n",
       "      <td>79033</td>\n",
       "      <td>145860</td>\n",
       "      <td>80437</td>\n",
       "      <td>42564</td>\n",
       "      <td>125173</td>\n",
       "      <td>79501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>115193</td>\n",
       "      <td>52340</td>\n",
       "      <td>76273</td>\n",
       "      <td>141424</td>\n",
       "      <td>73668</td>\n",
       "      <td>36349</td>\n",
       "      <td>120684</td>\n",
       "      <td>74540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>105701</td>\n",
       "      <td>44252</td>\n",
       "      <td>71605</td>\n",
       "      <td>127526</td>\n",
       "      <td>64385</td>\n",
       "      <td>36850</td>\n",
       "      <td>118556</td>\n",
       "      <td>71426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>75754</td>\n",
       "      <td>27226</td>\n",
       "      <td>45947</td>\n",
       "      <td>79743</td>\n",
       "      <td>35544</td>\n",
       "      <td>46149</td>\n",
       "      <td>97143</td>\n",
       "      <td>56438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>74873</td>\n",
       "      <td>29181</td>\n",
       "      <td>40812</td>\n",
       "      <td>75746</td>\n",
       "      <td>37620</td>\n",
       "      <td>53812</td>\n",
       "      <td>100735</td>\n",
       "      <td>53798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>106826</td>\n",
       "      <td>51646</td>\n",
       "      <td>68087</td>\n",
       "      <td>129982</td>\n",
       "      <td>69767</td>\n",
       "      <td>44500</td>\n",
       "      <td>119211</td>\n",
       "      <td>72883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Berri1    CSC  Mais1   Mais2   Parc  PierDup  Rachel1  \\\n",
       "Weekday                                                            \n",
       "Tuesday    117244  54656  76974  141217  74299    40679   123533   \n",
       "Wednesday  120434  59604  79033  145860  80437    42564   125173   \n",
       "Thursday   115193  52340  76273  141424  73668    36349   120684   \n",
       "Friday     105701  44252  71605  127526  64385    36850   118556   \n",
       "Saturday    75754  27226  45947   79743  35544    46149    97143   \n",
       "Sunday      74873  29181  40812   75746  37620    53812   100735   \n",
       "Monday     106826  51646  68087  129982  69767    44500   119211   \n",
       "\n",
       "           Totem_Laurier  \n",
       "Weekday                   \n",
       "Tuesday            76559  \n",
       "Wednesday          79501  \n",
       "Thursday           74540  \n",
       "Friday             71426  \n",
       "Saturday           56438  \n",
       "Sunday             53798  \n",
       "Monday             72883  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can now display this information in a figure. We first need to reorder the table by the weekday using `ix` (indexing operation). Then, we plot the table, specifying the line width and the figure size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jsGcPjB8PO3eW23r0gM8/N2afbgiOFRdz/qZNpJoVPHsHB7NuyBAiXOZE+/HH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\n+aJfP3zN/1Nb8vOZtGcPDv1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aorNzp/tL6dBa3WgRQqOUigBmA/0xJqGdDOwHFgGdgSPA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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c00cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_week.ix[days].plot(lw=3, figsize=(6,4));\n",
    "plt.ylim(0);  # Set the bottom axis to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Finally, let's illustrate interactive capabilities through `widgets`. We plot a *smoothed* version of the track attendance as a function of time (**rolling mean**). The idea is to compute the mean value in the neighborhood of any day. The larger the neighborhood, the smoother the curve. We will create an interactive slider in the notebook to vary this parameter in real-time in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ktw/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=15,center=False).mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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aFzoAEQnjx0OXLjXPTJUmG24IAwfC7rvDokXF0xIgUmxqrVGb2bpm9oaZjTez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\n6qFuhgQjLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115fb4290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "#from IPython.html.widgets import interact # IPython < 4.x\n",
    "@interact\n",
    "def plot(n=(1, 30)):\n",
    "    plt.figure(figsize=(8,4));\n",
    "    pd.rolling_mean(df['Berri1'], n).dropna().plot();\n",
    "    plt.ylim(0, 8000);\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### <font color=\"red\"> *Exercise* </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a widget that computes the square of integers between 0 and 10!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- while `matplotlib` is extremely powerful, it can also be complex. \n",
    "- sometimes much effort needed to get good-looking graphs\n",
    "- `seaborn` is a higher-level visualization package based on `matplotlib`\n",
    "- default visualization much more appealing compared to `matplotlib`!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"blue\"> Demo: Seaborn </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first load the `seaborn` module and import some data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Tuesday', u'Wednesday', u'Thursday', u'Friday', u'Saturday',\n",
       "       u'Sunday', u'Monday'],\n",
       "      dtype='object', name=u'Weekday')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EpNSoxUJERMRCWnkzl1osREREpNSoxUJERMRCWscilwoLERERCzmorjCosBAREbGQWixy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\nAACeoVgAAADP/A1pYoQsGIp1KwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113189b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(df_week.ix[days],linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"red\"> *Exercise* </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Annotate each cell with the numeric value using integer formatting!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nobel prizes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import as packages we need, and load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>firstname</th>\n",
       "      <th>surname</th>\n",
       "      <th>born</th>\n",
       "      <th>died</th>\n",
       "      <th>bornCountry</th>\n",
       "      <th>bornCountryCode</th>\n",
       "      <th>bornCity</th>\n",
       "      <th>diedCountry</th>\n",
       "      <th>diedCountryCode</th>\n",
       "      <th>diedCity</th>\n",
       "      <th>gender</th>\n",
       "      <th>year</th>\n",
       "      <th>category</th>\n",
       "      <th>overallMotivation</th>\n",
       "      <th>share</th>\n",
       "      <th>motivation</th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Wilhelm Conrad</td>\n",
       "      <td>Röntgen</td>\n",
       "      <td>1845-03-27</td>\n",
       "      <td>1923-02-10</td>\n",
       "      <td>Prussia (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Lennep (now Remscheid)</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Munich</td>\n",
       "      <td>male</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>Munich University</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hendrik Antoon</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>1853-07-18</td>\n",
       "      <td>1928-02-04</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Arnhem</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"in recognition of the extraordinary service t...</td>\n",
       "      <td>Leiden University</td>\n",
       "      <td>Leiden</td>\n",
       "      <td>the Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Pieter</td>\n",
       "      <td>Zeeman</td>\n",
       "      <td>1865-05-25</td>\n",
       "      <td>1943-10-09</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Zonnemaire</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>male</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"in recognition of the extraordinary service t...</td>\n",
       "      <td>Amsterdam University</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>the Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Antoine Henri</td>\n",
       "      <td>Becquerel</td>\n",
       "      <td>1852-12-15</td>\n",
       "      <td>1908-08-25</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>École Polytechnique</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pierre</td>\n",
       "      <td>Curie</td>\n",
       "      <td>1859-05-15</td>\n",
       "      <td>1906-04-19</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Paris</td>\n",
       "      <td>male</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>École municipale de physique et de chimie indu...</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Marie</td>\n",
       "      <td>Curie, née Sklodowska</td>\n",
       "      <td>1867-11-07</td>\n",
       "      <td>1934-07-04</td>\n",
       "      <td>Russian Empire (now Poland)</td>\n",
       "      <td>PL</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Sallanches</td>\n",
       "      <td>female</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Marie</td>\n",
       "      <td>Curie, née Sklodowska</td>\n",
       "      <td>1867-11-07</td>\n",
       "      <td>1934-07-04</td>\n",
       "      <td>Russian Empire (now Poland)</td>\n",
       "      <td>PL</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Sallanches</td>\n",
       "      <td>female</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"in recognition of her services to the advance...</td>\n",
       "      <td>Sorbonne University</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Lord Rayleigh</td>\n",
       "      <td>(John William Strutt)</td>\n",
       "      <td>1842-11-12</td>\n",
       "      <td>1919-06-30</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Langford Grove, Maldon, Essex</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his investigations of the densities of th...</td>\n",
       "      <td>Royal Institution of Great Britain</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Philipp Eduard Anton</td>\n",
       "      <td>von Lenard</td>\n",
       "      <td>1862-06-07</td>\n",
       "      <td>1947-05-20</td>\n",
       "      <td>Hungary (now Slovakia)</td>\n",
       "      <td>SK</td>\n",
       "      <td>Pressburg (now Bratislava)</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Messelhausen</td>\n",
       "      <td>male</td>\n",
       "      <td>1905.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his work on cathode rays\"</td>\n",
       "      <td>Kiel University</td>\n",
       "      <td>Kiel</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Joseph John</td>\n",
       "      <td>Thomson</td>\n",
       "      <td>1856-12-18</td>\n",
       "      <td>1940-08-30</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Cheetham Hill, near Manchester</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>male</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"in recognition of the great merits of his the...</td>\n",
       "      <td>University of Cambridge</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Albert Abraham</td>\n",
       "      <td>Michelson</td>\n",
       "      <td>1852-12-19</td>\n",
       "      <td>1931-05-09</td>\n",
       "      <td>Prussia (now Poland)</td>\n",
       "      <td>PL</td>\n",
       "      <td>Strelno (now Strzelno)</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Pasadena, CA</td>\n",
       "      <td>male</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his optical precision instruments and the...</td>\n",
       "      <td>University of Chicago</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Gabriel</td>\n",
       "      <td>Lippmann</td>\n",
       "      <td>1845-08-16</td>\n",
       "      <td>1921-07-13</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>LU</td>\n",
       "      <td>Hollerich</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>1908.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his method of reproducing colours photogr...</td>\n",
       "      <td>Sorbonne University</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Guglielmo</td>\n",
       "      <td>Marconi</td>\n",
       "      <td>1874-04-25</td>\n",
       "      <td>1937-07-20</td>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td>Rome</td>\n",
       "      <td>male</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"in recognition of their contributions to the ...</td>\n",
       "      <td>Marconi Wireless Telegraph Co. Ltd.</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Karl Ferdinand</td>\n",
       "      <td>Braun</td>\n",
       "      <td>1850-06-06</td>\n",
       "      <td>1918-04-20</td>\n",
       "      <td>Hesse-Kassel (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Fulda</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Brooklyn, NY</td>\n",
       "      <td>male</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"in recognition of their contributions to the ...</td>\n",
       "      <td>Strasbourg University</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>Alsace (then Germany, now France)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Johannes Diderik</td>\n",
       "      <td>van der Waals</td>\n",
       "      <td>1837-11-23</td>\n",
       "      <td>1923-03-08</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Leiden</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>male</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his work on the equation of state for gas...</td>\n",
       "      <td>Amsterdam University</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>the Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Wilhelm</td>\n",
       "      <td>Wien</td>\n",
       "      <td>1864-01-13</td>\n",
       "      <td>1928-08-30</td>\n",
       "      <td>Prussia (now Russia)</td>\n",
       "      <td>RU</td>\n",
       "      <td>Gaffken (now Parusnoye)</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Munich</td>\n",
       "      <td>male</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his discoveries regarding the laws govern...</td>\n",
       "      <td>Würzburg University</td>\n",
       "      <td>Würzburg</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Nils Gustaf</td>\n",
       "      <td>Dalén</td>\n",
       "      <td>1869-11-30</td>\n",
       "      <td>1937-12-09</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stenstorp</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>male</td>\n",
       "      <td>1912.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his invention of automatic regulators for...</td>\n",
       "      <td>Swedish Gas-Accumulator Co.</td>\n",
       "      <td>Lidingö-Stockholm</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Heike</td>\n",
       "      <td>Kamerlingh Onnes</td>\n",
       "      <td>1853-09-21</td>\n",
       "      <td>1926-02-21</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Groningen</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Leiden</td>\n",
       "      <td>male</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his investigations on the properties of m...</td>\n",
       "      <td>Leiden University</td>\n",
       "      <td>Leiden</td>\n",
       "      <td>the Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Max</td>\n",
       "      <td>von Laue</td>\n",
       "      <td>1879-10-09</td>\n",
       "      <td>1960-04-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Pfaffendorf</td>\n",
       "      <td>West Germany (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>male</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his discovery of the diffraction of X-ray...</td>\n",
       "      <td>Frankfurt-on-the-Main University</td>\n",
       "      <td>Frankfurt-on-the-Main</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Sir William Henry</td>\n",
       "      <td>Bragg</td>\n",
       "      <td>1862-07-02</td>\n",
       "      <td>1942-03-12</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Wigton</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>male</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their services in the analysis of crystal...</td>\n",
       "      <td>University College</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>William Lawrence</td>\n",
       "      <td>Bragg</td>\n",
       "      <td>1890-03-31</td>\n",
       "      <td>1971-07-01</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Ipswich</td>\n",
       "      <td>male</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their services in the analysis of crystal...</td>\n",
       "      <td>Victoria University</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Charles Glover</td>\n",
       "      <td>Barkla</td>\n",
       "      <td>1877-06-07</td>\n",
       "      <td>1944-10-23</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Widnes</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>GB</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>male</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his discovery of the characteristic R&amp;oum...</td>\n",
       "      <td>Edinburgh University</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Max Karl Ernst Ludwig</td>\n",
       "      <td>Planck</td>\n",
       "      <td>1858-04-23</td>\n",
       "      <td>1947-10-04</td>\n",
       "      <td>Schleswig (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Kiel</td>\n",
       "      <td>West Germany (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Göttingen</td>\n",
       "      <td>male</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"in recognition of the services he rendered to...</td>\n",
       "      <td>Berlin University</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Johannes</td>\n",
       "      <td>Stark</td>\n",
       "      <td>1874-04-15</td>\n",
       "      <td>1957-06-21</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Schickenhof</td>\n",
       "      <td>West Germany (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Traunstein</td>\n",
       "      <td>male</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his discovery of the Doppler effect in ca...</td>\n",
       "      <td>Greifswald University</td>\n",
       "      <td>Greifswald</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Charles Edouard</td>\n",
       "      <td>Guillaume</td>\n",
       "      <td>1861-02-15</td>\n",
       "      <td>1938-06-13</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CH</td>\n",
       "      <td>Fleurier</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Sèvres</td>\n",
       "      <td>male</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"in recognition of the service he has rendered...</td>\n",
       "      <td>Bureau International des Poids et Mesures (Int...</td>\n",
       "      <td>Sèvres</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Einstein</td>\n",
       "      <td>1879-03-14</td>\n",
       "      <td>1955-04-18</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Ulm</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Princeton, NJ</td>\n",
       "      <td>male</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his services to Theoretical Physics, and ...</td>\n",
       "      <td>Kaiser-Wilhelm-Institut (now Max-Planck-Instit...</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Niels Henrik David</td>\n",
       "      <td>Bohr</td>\n",
       "      <td>1885-10-07</td>\n",
       "      <td>1962-11-18</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DK</td>\n",
       "      <td>Copenhagen</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DK</td>\n",
       "      <td>Copenhagen</td>\n",
       "      <td>male</td>\n",
       "      <td>1922.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his services in the investigation of the ...</td>\n",
       "      <td>Copenhagen University</td>\n",
       "      <td>Copenhagen</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Robert Andrews</td>\n",
       "      <td>Millikan</td>\n",
       "      <td>1868-03-22</td>\n",
       "      <td>1953-12-19</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Morrison, IL</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>San Marino, CA</td>\n",
       "      <td>male</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his work on the elementary charge of elec...</td>\n",
       "      <td>California Institute of Technology (Caltech)</td>\n",
       "      <td>Pasadena, CA</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Karl Manne Georg</td>\n",
       "      <td>Siegbahn</td>\n",
       "      <td>1886-12-03</td>\n",
       "      <td>1978-09-26</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Lund</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ängelholm</td>\n",
       "      <td>male</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his discoveries and research in the field...</td>\n",
       "      <td>Uppsala University</td>\n",
       "      <td>Uppsala</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>James</td>\n",
       "      <td>Franck</td>\n",
       "      <td>1882-08-26</td>\n",
       "      <td>1964-05-21</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>West Germany (now Germany)</td>\n",
       "      <td>DE</td>\n",
       "      <td>Göttingen</td>\n",
       "      <td>male</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their discovery of the laws governing the...</td>\n",
       "      <td>Goettingen University</td>\n",
       "      <td>Göttingen</td>\n",
       "      <td>Germany</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>910</td>\n",
       "      <td>Stefan W.</td>\n",
       "      <td>Hell</td>\n",
       "      <td>1962-12-23</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Romania</td>\n",
       "      <td>RO</td>\n",
       "      <td>Arad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for the development of super-resolved fluores...</td>\n",
       "      <td>German Cancer Research Center</td>\n",
       "      <td>Heidelberg</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>911</td>\n",
       "      <td>William E.</td>\n",
       "      <td>Moerner</td>\n",
       "      <td>1953-06-24</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Pleasanton, CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for the development of super-resolved fluores...</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>Stanford, CA</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>912</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Modiano</td>\n",
       "      <td>1945-07-30</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>literature</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for the art of memory with which he has evoke...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>913</td>\n",
       "      <td>Kailash</td>\n",
       "      <td>Satyarthi</td>\n",
       "      <td>1954-01-11</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Vidisha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>peace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their struggle against the suppression of...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>914</td>\n",
       "      <td>Malala</td>\n",
       "      <td>Yousafzai</td>\n",
       "      <td>1997-07-12</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>PK</td>\n",
       "      <td>Mingora</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>peace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their struggle against the suppression of...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>915</td>\n",
       "      <td>Jean</td>\n",
       "      <td>Tirole</td>\n",
       "      <td>1953-08-09</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Troyes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>economics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his analysis of market power and regulation\"</td>\n",
       "      <td>Toulouse School of Economics (TSE)</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>916</td>\n",
       "      <td>William C.</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>1930-06-28</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>IE</td>\n",
       "      <td>Ramelton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>medicine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>\"for their discoveries concerning a novel ther...</td>\n",
       "      <td>Drew University</td>\n",
       "      <td>Madison, NJ</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>917</td>\n",
       "      <td>Satoshi</td>\n",
       "      <td>Ōmura</td>\n",
       "      <td>1935-07-12</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JP</td>\n",
       "      <td>Yamanashi Prefecture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>medicine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>\"for their discoveries concerning a novel ther...</td>\n",
       "      <td>Kitasato University</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>918</td>\n",
       "      <td>Youyou</td>\n",
       "      <td>Tu</td>\n",
       "      <td>1930-12-30</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>China</td>\n",
       "      <td>CN</td>\n",
       "      <td>Zhejiang Ningbo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>medicine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for her discoveries concerning a novel therap...</td>\n",
       "      <td>China Academy of Traditional Chinese Medicine</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>919</td>\n",
       "      <td>Takaaki</td>\n",
       "      <td>Kajita</td>\n",
       "      <td>1959-03-09</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JP</td>\n",
       "      <td>Higashimatsuyama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for the discovery of neutrino oscillations, w...</td>\n",
       "      <td>University of Tokyo</td>\n",
       "      <td>Kashiwa</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>920</td>\n",
       "      <td>Arthur B.</td>\n",
       "      <td>McDonald</td>\n",
       "      <td>1943-08-29</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for the discovery of neutrino oscillations, w...</td>\n",
       "      <td>Queen's University</td>\n",
       "      <td>Kingston</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>921</td>\n",
       "      <td>Tomas</td>\n",
       "      <td>Lindahl</td>\n",
       "      <td>1938-01-28</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for mechanistic studies of DNA repair\"</td>\n",
       "      <td>Francis Crick Institute</td>\n",
       "      <td>Hertfordshire</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>921</td>\n",
       "      <td>Tomas</td>\n",
       "      <td>Lindahl</td>\n",
       "      <td>1938-01-28</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for mechanistic studies of DNA repair\"</td>\n",
       "      <td>Clare Hall Laboratory</td>\n",
       "      <td>Hertfordshire</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>922</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Modrich</td>\n",
       "      <td>1946-06-13</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Raton, NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for mechanistic studies of DNA repair\"</td>\n",
       "      <td>Howard Hughes Medical Institute</td>\n",
       "      <td>Durham, NC</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>922</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Modrich</td>\n",
       "      <td>1946-06-13</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Raton, NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for mechanistic studies of DNA repair\"</td>\n",
       "      <td>Duke University School of Medicine</td>\n",
       "      <td>Durham, NC</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>923</td>\n",
       "      <td>Aziz</td>\n",
       "      <td>Sancar</td>\n",
       "      <td>1946-09-08</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>TR</td>\n",
       "      <td>Savur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for mechanistic studies of DNA repair\"</td>\n",
       "      <td>University of North Carolina</td>\n",
       "      <td>Chapel Hill, NC</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>924</td>\n",
       "      <td>Svetlana</td>\n",
       "      <td>Alexievich</td>\n",
       "      <td>1948-05-31</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>UA</td>\n",
       "      <td>Ivano-Frankivsk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>literature</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for her polyphonic writings, a monument to su...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>925</td>\n",
       "      <td>National Dialogue Quartet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</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>org</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>peace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for its decisive contribution to the building...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>926</td>\n",
       "      <td>Angus</td>\n",
       "      <td>Deaton</td>\n",
       "      <td>1945-10-19</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>economics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his analysis of consumption, poverty, and...</td>\n",
       "      <td>Princeton University</td>\n",
       "      <td>Princeton, NJ</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>927</td>\n",
       "      <td>Yoshinori</td>\n",
       "      <td>Ohsumi</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JP</td>\n",
       "      <td>Fukuoka</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>medicine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his discoveries of mechanisms for autophagy\"</td>\n",
       "      <td>Tokyo Institute of Technology</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>928</td>\n",
       "      <td>David J.</td>\n",
       "      <td>Thouless</td>\n",
       "      <td>1934-09-21</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Bearsden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for theoretical discoveries of topological ph...</td>\n",
       "      <td>University of Washington</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>929</td>\n",
       "      <td>F. Duncan M.</td>\n",
       "      <td>Haldane</td>\n",
       "      <td>1951-09-14</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>\"for theoretical discoveries of topological ph...</td>\n",
       "      <td>Princeton University</td>\n",
       "      <td>Princeton, NJ</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>930</td>\n",
       "      <td>J. Michael</td>\n",
       "      <td>Kosterlitz</td>\n",
       "      <td>1943-06-22</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>physics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>\"for theoretical discoveries of topological ph...</td>\n",
       "      <td>Brown University</td>\n",
       "      <td>Providence, RI</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>931</td>\n",
       "      <td>Jean-Pierre</td>\n",
       "      <td>Sauvage</td>\n",
       "      <td>1944-10-21</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for the design and synthesis of molecular mac...</td>\n",
       "      <td>University of Strasbourg</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>932</td>\n",
       "      <td>Sir J. Fraser</td>\n",
       "      <td>Stoddart</td>\n",
       "      <td>1942-05-24</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for the design and synthesis of molecular mac...</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>Evanston, IL</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>933</td>\n",
       "      <td>Bernard L.</td>\n",
       "      <td>Feringa</td>\n",
       "      <td>1951-05-18</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Barger-Compascuum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>chemistry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>\"for the design and synthesis of molecular mac...</td>\n",
       "      <td>University of Groningen</td>\n",
       "      <td>Groningen</td>\n",
       "      <td>the Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>934</td>\n",
       "      <td>Juan Manuel</td>\n",
       "      <td>Santos</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>CO</td>\n",
       "      <td>Bogotá</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>peace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for his resolute efforts to bring the country...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>935</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>Hart</td>\n",
       "      <td>1948-10-09</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>economics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their contributions to contract theory\"</td>\n",
       "      <td>Harvard University</td>\n",
       "      <td>Cambridge, MA</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>936</td>\n",
       "      <td>Bengt</td>\n",
       "      <td>Holmström</td>\n",
       "      <td>1949-04-18</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FI</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>economics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>\"for their contributions to contract theory\"</td>\n",
       "      <td>Massachusetts Institute of Technology (MIT)</td>\n",
       "      <td>Cambridge, MA</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>937</td>\n",
       "      <td>Bob</td>\n",
       "      <td>Dylan</td>\n",
       "      <td>1941-05-24</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>Duluth, MN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>literature</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>\"for having created new poetic expressions wit...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>975 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                  firstname                surname        born  \\\n",
       "0      1             Wilhelm Conrad                Röntgen  1845-03-27   \n",
       "1      2             Hendrik Antoon                Lorentz  1853-07-18   \n",
       "2      3                     Pieter                 Zeeman  1865-05-25   \n",
       "3      4              Antoine Henri              Becquerel  1852-12-15   \n",
       "4      5                     Pierre                  Curie  1859-05-15   \n",
       "5      6                      Marie  Curie, née Sklodowska  1867-11-07   \n",
       "6      6                      Marie  Curie, née Sklodowska  1867-11-07   \n",
       "7      8              Lord Rayleigh  (John William Strutt)  1842-11-12   \n",
       "8      9       Philipp Eduard Anton             von Lenard  1862-06-07   \n",
       "9     10                Joseph John                Thomson  1856-12-18   \n",
       "10    11             Albert Abraham              Michelson  1852-12-19   \n",
       "11    12                    Gabriel               Lippmann  1845-08-16   \n",
       "12    13                  Guglielmo                Marconi  1874-04-25   \n",
       "13    14             Karl Ferdinand                  Braun  1850-06-06   \n",
       "14    15           Johannes Diderik          van der Waals  1837-11-23   \n",
       "15    16                    Wilhelm                   Wien  1864-01-13   \n",
       "16    17                Nils Gustaf                  Dalén  1869-11-30   \n",
       "17    18                      Heike       Kamerlingh Onnes  1853-09-21   \n",
       "18    19                        Max               von Laue  1879-10-09   \n",
       "19    20          Sir William Henry                  Bragg  1862-07-02   \n",
       "20    21           William Lawrence                  Bragg  1890-03-31   \n",
       "21    22             Charles Glover                 Barkla  1877-06-07   \n",
       "22    23      Max Karl Ernst Ludwig                 Planck  1858-04-23   \n",
       "23    24                   Johannes                  Stark  1874-04-15   \n",
       "24    25            Charles Edouard              Guillaume  1861-02-15   \n",
       "25    26                     Albert               Einstein  1879-03-14   \n",
       "26    27         Niels Henrik David                   Bohr  1885-10-07   \n",
       "27    28             Robert Andrews               Millikan  1868-03-22   \n",
       "28    29           Karl Manne Georg               Siegbahn  1886-12-03   \n",
       "29    30                      James                 Franck  1882-08-26   \n",
       "..   ...                        ...                    ...         ...   \n",
       "945  910                  Stefan W.                   Hell  1962-12-23   \n",
       "946  911                 William E.                Moerner  1953-06-24   \n",
       "947  912                    Patrick                Modiano  1945-07-30   \n",
       "948  913                    Kailash              Satyarthi  1954-01-11   \n",
       "949  914                     Malala              Yousafzai  1997-07-12   \n",
       "950  915                       Jean                 Tirole  1953-08-09   \n",
       "951  916                 William C.               Campbell  1930-06-28   \n",
       "952  917                    Satoshi                  Ōmura  1935-07-12   \n",
       "953  918                     Youyou                     Tu  1930-12-30   \n",
       "954  919                    Takaaki                 Kajita  1959-03-09   \n",
       "955  920                  Arthur B.               McDonald  1943-08-29   \n",
       "956  921                      Tomas                Lindahl  1938-01-28   \n",
       "957  921                      Tomas                Lindahl  1938-01-28   \n",
       "958  922                       Paul                Modrich  1946-06-13   \n",
       "959  922                       Paul                Modrich  1946-06-13   \n",
       "960  923                       Aziz                 Sancar  1946-09-08   \n",
       "961  924                   Svetlana             Alexievich  1948-05-31   \n",
       "962  925  National Dialogue Quartet                    NaN  0000-00-00   \n",
       "963  926                      Angus                 Deaton  1945-10-19   \n",
       "964  927                  Yoshinori                 Ohsumi  0000-00-00   \n",
       "965  928                   David J.               Thouless  1934-09-21   \n",
       "966  929               F. Duncan M.                Haldane  1951-09-14   \n",
       "967  930                 J. Michael             Kosterlitz  1943-06-22   \n",
       "968  931                Jean-Pierre                Sauvage  1944-10-21   \n",
       "969  932              Sir J. Fraser               Stoddart  1942-05-24   \n",
       "970  933                 Bernard L.                Feringa  1951-05-18   \n",
       "971  934                Juan Manuel                 Santos  0000-00-00   \n",
       "972  935                     Oliver                   Hart  1948-10-09   \n",
       "973  936                      Bengt              Holmström  1949-04-18   \n",
       "974  937                        Bob                  Dylan  1941-05-24   \n",
       "\n",
       "           died                  bornCountry bornCountryCode  \\\n",
       "0    1923-02-10        Prussia (now Germany)              DE   \n",
       "1    1928-02-04              the Netherlands              NL   \n",
       "2    1943-10-09              the Netherlands              NL   \n",
       "3    1908-08-25                       France              FR   \n",
       "4    1906-04-19                       France              FR   \n",
       "5    1934-07-04  Russian Empire (now Poland)              PL   \n",
       "6    1934-07-04  Russian Empire (now Poland)              PL   \n",
       "7    1919-06-30               United Kingdom              GB   \n",
       "8    1947-05-20       Hungary (now Slovakia)              SK   \n",
       "9    1940-08-30               United Kingdom              GB   \n",
       "10   1931-05-09         Prussia (now Poland)              PL   \n",
       "11   1921-07-13                   Luxembourg              LU   \n",
       "12   1937-07-20                        Italy              IT   \n",
       "13   1918-04-20   Hesse-Kassel (now Germany)              DE   \n",
       "14   1923-03-08              the Netherlands              NL   \n",
       "15   1928-08-30         Prussia (now Russia)              RU   \n",
       "16   1937-12-09                       Sweden              SE   \n",
       "17   1926-02-21              the Netherlands              NL   \n",
       "18   1960-04-23                      Germany              DE   \n",
       "19   1942-03-12               United Kingdom              GB   \n",
       "20   1971-07-01                    Australia              AU   \n",
       "21   1944-10-23               United Kingdom              GB   \n",
       "22   1947-10-04      Schleswig (now Germany)              DE   \n",
       "23   1957-06-21                      Germany              DE   \n",
       "24   1938-06-13                  Switzerland              CH   \n",
       "25   1955-04-18                      Germany              DE   \n",
       "26   1962-11-18                      Denmark              DK   \n",
       "27   1953-12-19                          USA              US   \n",
       "28   1978-09-26                       Sweden              SE   \n",
       "29   1964-05-21                      Germany              DE   \n",
       "..          ...                          ...             ...   \n",
       "945  0000-00-00                      Romania              RO   \n",
       "946  0000-00-00                          USA              US   \n",
       "947  0000-00-00                       France              FR   \n",
       "948  0000-00-00                        India              IN   \n",
       "949  0000-00-00                     Pakistan              PK   \n",
       "950  0000-00-00                       France              FR   \n",
       "951  0000-00-00                      Ireland              IE   \n",
       "952  0000-00-00                        Japan              JP   \n",
       "953  0000-00-00                        China              CN   \n",
       "954  0000-00-00                        Japan              JP   \n",
       "955  0000-00-00                       Canada              CA   \n",
       "956  0000-00-00                       Sweden              SE   \n",
       "957  0000-00-00                       Sweden              SE   \n",
       "958  0000-00-00                          USA              US   \n",
       "959  0000-00-00                          USA              US   \n",
       "960  0000-00-00                       Turkey              TR   \n",
       "961  0000-00-00                      Ukraine              UA   \n",
       "962  0000-00-00                          NaN             NaN   \n",
       "963  0000-00-00               United Kingdom              GB   \n",
       "964  0000-00-00                        Japan              JP   \n",
       "965  0000-00-00               United Kingdom              GB   \n",
       "966  0000-00-00               United Kingdom              GB   \n",
       "967  0000-00-00               United Kingdom              GB   \n",
       "968  0000-00-00                       France              FR   \n",
       "969  0000-00-00               United Kingdom              GB   \n",
       "970  0000-00-00              the Netherlands              NL   \n",
       "971  0000-00-00                     Colombia              CO   \n",
       "972  0000-00-00               United Kingdom              GB   \n",
       "973  0000-00-00                      Finland              FI   \n",
       "974  0000-00-00                          USA              US   \n",
       "\n",
       "                           bornCity                 diedCountry  \\\n",
       "0            Lennep (now Remscheid)                     Germany   \n",
       "1                            Arnhem             the Netherlands   \n",
       "2                        Zonnemaire             the Netherlands   \n",
       "3                             Paris                      France   \n",
       "4                             Paris                      France   \n",
       "5                            Warsaw                      France   \n",
       "6                            Warsaw                      France   \n",
       "7     Langford Grove, Maldon, Essex              United Kingdom   \n",
       "8        Pressburg (now Bratislava)                     Germany   \n",
       "9    Cheetham Hill, near Manchester              United Kingdom   \n",
       "10           Strelno (now Strzelno)                         USA   \n",
       "11                        Hollerich                         NaN   \n",
       "12                          Bologna                       Italy   \n",
       "13                            Fulda                         USA   \n",
       "14                           Leiden             the Netherlands   \n",
       "15          Gaffken (now Parusnoye)                     Germany   \n",
       "16                        Stenstorp                      Sweden   \n",
       "17                        Groningen             the Netherlands   \n",
       "18                      Pfaffendorf  West Germany (now Germany)   \n",
       "19                           Wigton              United Kingdom   \n",
       "20                         Adelaide              United Kingdom   \n",
       "21                           Widnes                    Scotland   \n",
       "22                             Kiel  West Germany (now Germany)   \n",
       "23                      Schickenhof  West Germany (now Germany)   \n",
       "24                         Fleurier                      France   \n",
       "25                              Ulm                         USA   \n",
       "26                       Copenhagen                     Denmark   \n",
       "27                     Morrison, IL                         USA   \n",
       "28                             Lund                      Sweden   \n",
       "29                          Hamburg  West Germany (now Germany)   \n",
       "..                              ...                         ...   \n",
       "945                            Arad                         NaN   \n",
       "946                  Pleasanton, CA                         NaN   \n",
       "947                           Paris                         NaN   \n",
       "948                         Vidisha                         NaN   \n",
       "949                         Mingora                         NaN   \n",
       "950                          Troyes                         NaN   \n",
       "951                        Ramelton                         NaN   \n",
       "952            Yamanashi Prefecture                         NaN   \n",
       "953                 Zhejiang Ningbo                         NaN   \n",
       "954                Higashimatsuyama                         NaN   \n",
       "955                          Sydney                         NaN   \n",
       "956                       Stockholm                         NaN   \n",
       "957                       Stockholm                         NaN   \n",
       "958                       Raton, NM                         NaN   \n",
       "959                       Raton, NM                         NaN   \n",
       "960                           Savur                         NaN   \n",
       "961                 Ivano-Frankivsk                         NaN   \n",
       "962                             NaN                         NaN   \n",
       "963                       Edinburgh                         NaN   \n",
       "964                         Fukuoka                         NaN   \n",
       "965                        Bearsden                         NaN   \n",
       "966                          London                         NaN   \n",
       "967                        Aberdeen                         NaN   \n",
       "968                           Paris                         NaN   \n",
       "969                       Edinburgh                         NaN   \n",
       "970               Barger-Compascuum                         NaN   \n",
       "971                          Bogotá                         NaN   \n",
       "972                          London                         NaN   \n",
       "973                        Helsinki                         NaN   \n",
       "974                      Duluth, MN                         NaN   \n",
       "\n",
       "    diedCountryCode        diedCity  gender    year    category  \\\n",
       "0                DE          Munich    male  1901.0     physics   \n",
       "1                NL             NaN    male  1902.0     physics   \n",
       "2                NL       Amsterdam    male  1902.0     physics   \n",
       "3                FR             NaN    male  1903.0     physics   \n",
       "4                FR           Paris    male  1903.0     physics   \n",
       "5                FR      Sallanches  female  1903.0     physics   \n",
       "6                FR      Sallanches  female  1911.0   chemistry   \n",
       "7                GB             NaN    male  1904.0     physics   \n",
       "8                DE    Messelhausen    male  1905.0     physics   \n",
       "9                GB       Cambridge    male  1906.0     physics   \n",
       "10               US    Pasadena, CA    male  1907.0     physics   \n",
       "11              NaN             NaN    male  1908.0     physics   \n",
       "12               IT            Rome    male  1909.0     physics   \n",
       "13               US    Brooklyn, NY    male  1909.0     physics   \n",
       "14               NL       Amsterdam    male  1910.0     physics   \n",
       "15               DE          Munich    male  1911.0     physics   \n",
       "16               SE       Stockholm    male  1912.0     physics   \n",
       "17               NL          Leiden    male  1913.0     physics   \n",
       "18               DE          Berlin    male  1914.0     physics   \n",
       "19               GB          London    male  1915.0     physics   \n",
       "20               GB         Ipswich    male  1915.0     physics   \n",
       "21               GB       Edinburgh    male  1917.0     physics   \n",
       "22               DE       Göttingen    male  1918.0     physics   \n",
       "23               DE      Traunstein    male  1919.0     physics   \n",
       "24               FR          Sèvres    male  1920.0     physics   \n",
       "25               US   Princeton, NJ    male  1921.0     physics   \n",
       "26               DK      Copenhagen    male  1922.0     physics   \n",
       "27               US  San Marino, CA    male  1923.0     physics   \n",
       "28               SE       Ängelholm    male  1924.0     physics   \n",
       "29               DE       Göttingen    male  1925.0     physics   \n",
       "..              ...             ...     ...     ...         ...   \n",
       "945             NaN             NaN    male  2014.0   chemistry   \n",
       "946             NaN             NaN    male  2014.0   chemistry   \n",
       "947             NaN             NaN    male  2014.0  literature   \n",
       "948             NaN             NaN    male  2014.0       peace   \n",
       "949             NaN             NaN  female  2014.0       peace   \n",
       "950             NaN             NaN    male  2014.0   economics   \n",
       "951             NaN             NaN    male  2015.0    medicine   \n",
       "952             NaN             NaN    male  2015.0    medicine   \n",
       "953             NaN             NaN  female  2015.0    medicine   \n",
       "954             NaN             NaN    male  2015.0     physics   \n",
       "955             NaN             NaN    male  2015.0     physics   \n",
       "956             NaN             NaN    male  2015.0   chemistry   \n",
       "957             NaN             NaN    male  2015.0   chemistry   \n",
       "958             NaN             NaN    male  2015.0   chemistry   \n",
       "959             NaN             NaN    male  2015.0   chemistry   \n",
       "960             NaN             NaN    male  2015.0   chemistry   \n",
       "961             NaN             NaN  female  2015.0  literature   \n",
       "962             NaN             NaN     org  2015.0       peace   \n",
       "963             NaN             NaN    male  2015.0   economics   \n",
       "964             NaN             NaN    male  2016.0    medicine   \n",
       "965             NaN             NaN    male  2016.0     physics   \n",
       "966             NaN             NaN    male  2016.0     physics   \n",
       "967             NaN             NaN    male  2016.0     physics   \n",
       "968             NaN             NaN    male  2016.0   chemistry   \n",
       "969             NaN             NaN    male  2016.0   chemistry   \n",
       "970             NaN             NaN    male  2016.0   chemistry   \n",
       "971             NaN             NaN    male  2016.0       peace   \n",
       "972             NaN             NaN    male  2016.0   economics   \n",
       "973             NaN             NaN    male  2016.0   economics   \n",
       "974             NaN             NaN    male  2016.0  literature   \n",
       "\n",
       "    overallMotivation  share  \\\n",
       "0                 NaN    1.0   \n",
       "1                 NaN    2.0   \n",
       "2                 NaN    2.0   \n",
       "3                 NaN    2.0   \n",
       "4                 NaN    4.0   \n",
       "5                 NaN    4.0   \n",
       "6                 NaN    1.0   \n",
       "7                 NaN    1.0   \n",
       "8                 NaN    1.0   \n",
       "9                 NaN    1.0   \n",
       "10                NaN    1.0   \n",
       "11                NaN    1.0   \n",
       "12                NaN    2.0   \n",
       "13                NaN    2.0   \n",
       "14                NaN    1.0   \n",
       "15                NaN    1.0   \n",
       "16                NaN    1.0   \n",
       "17                NaN    1.0   \n",
       "18                NaN    1.0   \n",
       "19                NaN    2.0   \n",
       "20                NaN    2.0   \n",
       "21                NaN    1.0   \n",
       "22                NaN    1.0   \n",
       "23                NaN    1.0   \n",
       "24                NaN    1.0   \n",
       "25                NaN    1.0   \n",
       "26                NaN    1.0   \n",
       "27                NaN    1.0   \n",
       "28                NaN    1.0   \n",
       "29                NaN    2.0   \n",
       "..                ...    ...   \n",
       "945               NaN    3.0   \n",
       "946               NaN    3.0   \n",
       "947               NaN    1.0   \n",
       "948               NaN    2.0   \n",
       "949               NaN    2.0   \n",
       "950               NaN    1.0   \n",
       "951               NaN    4.0   \n",
       "952               NaN    4.0   \n",
       "953               NaN    2.0   \n",
       "954               NaN    2.0   \n",
       "955               NaN    2.0   \n",
       "956               NaN    3.0   \n",
       "957               NaN    3.0   \n",
       "958               NaN    3.0   \n",
       "959               NaN    3.0   \n",
       "960               NaN    3.0   \n",
       "961               NaN    1.0   \n",
       "962               NaN    1.0   \n",
       "963               NaN    1.0   \n",
       "964               NaN    1.0   \n",
       "965               NaN    2.0   \n",
       "966               NaN    4.0   \n",
       "967               NaN    4.0   \n",
       "968               NaN    3.0   \n",
       "969               NaN    3.0   \n",
       "970               NaN    3.0   \n",
       "971               NaN    1.0   \n",
       "972               NaN    2.0   \n",
       "973               NaN    2.0   \n",
       "974               NaN    1.0   \n",
       "\n",
       "                                            motivation  \\\n",
       "0    \"in recognition of the extraordinary services ...   \n",
       "1    \"in recognition of the extraordinary service t...   \n",
       "2    \"in recognition of the extraordinary service t...   \n",
       "3    \"in recognition of the extraordinary services ...   \n",
       "4    \"in recognition of the extraordinary services ...   \n",
       "5    \"in recognition of the extraordinary services ...   \n",
       "6    \"in recognition of her services to the advance...   \n",
       "7    \"for his investigations of the densities of th...   \n",
       "8                       \"for his work on cathode rays\"   \n",
       "9    \"in recognition of the great merits of his the...   \n",
       "10   \"for his optical precision instruments and the...   \n",
       "11   \"for his method of reproducing colours photogr...   \n",
       "12   \"in recognition of their contributions to the ...   \n",
       "13   \"in recognition of their contributions to the ...   \n",
       "14   \"for his work on the equation of state for gas...   \n",
       "15   \"for his discoveries regarding the laws govern...   \n",
       "16   \"for his invention of automatic regulators for...   \n",
       "17   \"for his investigations on the properties of m...   \n",
       "18   \"for his discovery of the diffraction of X-ray...   \n",
       "19   \"for their services in the analysis of crystal...   \n",
       "20   \"for their services in the analysis of crystal...   \n",
       "21   \"for his discovery of the characteristic R&oum...   \n",
       "22   \"in recognition of the services he rendered to...   \n",
       "23   \"for his discovery of the Doppler effect in ca...   \n",
       "24   \"in recognition of the service he has rendered...   \n",
       "25   \"for his services to Theoretical Physics, and ...   \n",
       "26   \"for his services in the investigation of the ...   \n",
       "27   \"for his work on the elementary charge of elec...   \n",
       "28   \"for his discoveries and research in the field...   \n",
       "29   \"for their discovery of the laws governing the...   \n",
       "..                                                 ...   \n",
       "945  \"for the development of super-resolved fluores...   \n",
       "946  \"for the development of super-resolved fluores...   \n",
       "947  \"for the art of memory with which he has evoke...   \n",
       "948  \"for their struggle against the suppression of...   \n",
       "949  \"for their struggle against the suppression of...   \n",
       "950  \"for his analysis of market power and regulation\"   \n",
       "951  \"for their discoveries concerning a novel ther...   \n",
       "952  \"for their discoveries concerning a novel ther...   \n",
       "953  \"for her discoveries concerning a novel therap...   \n",
       "954  \"for the discovery of neutrino oscillations, w...   \n",
       "955  \"for the discovery of neutrino oscillations, w...   \n",
       "956            \"for mechanistic studies of DNA repair\"   \n",
       "957            \"for mechanistic studies of DNA repair\"   \n",
       "958            \"for mechanistic studies of DNA repair\"   \n",
       "959            \"for mechanistic studies of DNA repair\"   \n",
       "960            \"for mechanistic studies of DNA repair\"   \n",
       "961  \"for her polyphonic writings, a monument to su...   \n",
       "962  \"for its decisive contribution to the building...   \n",
       "963  \"for his analysis of consumption, poverty, and...   \n",
       "964  \"for his discoveries of mechanisms for autophagy\"   \n",
       "965  \"for theoretical discoveries of topological ph...   \n",
       "966  \"for theoretical discoveries of topological ph...   \n",
       "967  \"for theoretical discoveries of topological ph...   \n",
       "968  \"for the design and synthesis of molecular mac...   \n",
       "969  \"for the design and synthesis of molecular mac...   \n",
       "970  \"for the design and synthesis of molecular mac...   \n",
       "971  \"for his resolute efforts to bring the country...   \n",
       "972       \"for their contributions to contract theory\"   \n",
       "973       \"for their contributions to contract theory\"   \n",
       "974  \"for having created new poetic expressions wit...   \n",
       "\n",
       "                                                  name                   city  \\\n",
       "0                                    Munich University                 Munich   \n",
       "1                                    Leiden University                 Leiden   \n",
       "2                                 Amsterdam University              Amsterdam   \n",
       "3                                  École Polytechnique                  Paris   \n",
       "4    École municipale de physique et de chimie indu...                  Paris   \n",
       "5                                                  NaN                    NaN   \n",
       "6                                  Sorbonne University                  Paris   \n",
       "7                   Royal Institution of Great Britain                 London   \n",
       "8                                      Kiel University                   Kiel   \n",
       "9                              University of Cambridge              Cambridge   \n",
       "10                               University of Chicago            Chicago, IL   \n",
       "11                                 Sorbonne University                  Paris   \n",
       "12                 Marconi Wireless Telegraph Co. Ltd.                 London   \n",
       "13                               Strasbourg University             Strasbourg   \n",
       "14                                Amsterdam University              Amsterdam   \n",
       "15                                 Würzburg University               Würzburg   \n",
       "16                         Swedish Gas-Accumulator Co.      Lidingö-Stockholm   \n",
       "17                                   Leiden University                 Leiden   \n",
       "18                    Frankfurt-on-the-Main University  Frankfurt-on-the-Main   \n",
       "19                                  University College                 London   \n",
       "20                                 Victoria University             Manchester   \n",
       "21                                Edinburgh University              Edinburgh   \n",
       "22                                   Berlin University                 Berlin   \n",
       "23                               Greifswald University             Greifswald   \n",
       "24   Bureau International des Poids et Mesures (Int...                 Sèvres   \n",
       "25   Kaiser-Wilhelm-Institut (now Max-Planck-Instit...                 Berlin   \n",
       "26                               Copenhagen University             Copenhagen   \n",
       "27        California Institute of Technology (Caltech)           Pasadena, CA   \n",
       "28                                  Uppsala University                Uppsala   \n",
       "29                               Goettingen University              Göttingen   \n",
       "..                                                 ...                    ...   \n",
       "945                      German Cancer Research Center             Heidelberg   \n",
       "946                                Stanford University           Stanford, CA   \n",
       "947                                                NaN                    NaN   \n",
       "948                                                NaN                    NaN   \n",
       "949                                                NaN                    NaN   \n",
       "950                 Toulouse School of Economics (TSE)               Toulouse   \n",
       "951                                    Drew University            Madison, NJ   \n",
       "952                                Kitasato University                  Tokyo   \n",
       "953      China Academy of Traditional Chinese Medicine                Beijing   \n",
       "954                                University of Tokyo                Kashiwa   \n",
       "955                                 Queen's University               Kingston   \n",
       "956                            Francis Crick Institute          Hertfordshire   \n",
       "957                              Clare Hall Laboratory          Hertfordshire   \n",
       "958                    Howard Hughes Medical Institute             Durham, NC   \n",
       "959                 Duke University School of Medicine             Durham, NC   \n",
       "960                       University of North Carolina        Chapel Hill, NC   \n",
       "961                                                NaN                    NaN   \n",
       "962                                                NaN                    NaN   \n",
       "963                               Princeton University          Princeton, NJ   \n",
       "964                      Tokyo Institute of Technology                  Tokyo   \n",
       "965                           University of Washington            Seattle, WA   \n",
       "966                               Princeton University          Princeton, NJ   \n",
       "967                                   Brown University         Providence, RI   \n",
       "968                           University of Strasbourg             Strasbourg   \n",
       "969                            Northwestern University           Evanston, IL   \n",
       "970                            University of Groningen              Groningen   \n",
       "971                                                NaN                    NaN   \n",
       "972                                 Harvard University          Cambridge, MA   \n",
       "973        Massachusetts Institute of Technology (MIT)          Cambridge, MA   \n",
       "974                                                NaN                    NaN   \n",
       "\n",
       "                               country  \n",
       "0                              Germany  \n",
       "1                      the Netherlands  \n",
       "2                      the Netherlands  \n",
       "3                               France  \n",
       "4                               France  \n",
       "5                                  NaN  \n",
       "6                               France  \n",
       "7                       United Kingdom  \n",
       "8                              Germany  \n",
       "9                       United Kingdom  \n",
       "10                                 USA  \n",
       "11                              France  \n",
       "12                      United Kingdom  \n",
       "13   Alsace (then Germany, now France)  \n",
       "14                     the Netherlands  \n",
       "15                             Germany  \n",
       "16                              Sweden  \n",
       "17                     the Netherlands  \n",
       "18                             Germany  \n",
       "19                      United Kingdom  \n",
       "20                      United Kingdom  \n",
       "21                      United Kingdom  \n",
       "22                             Germany  \n",
       "23                             Germany  \n",
       "24                              France  \n",
       "25                             Germany  \n",
       "26                             Denmark  \n",
       "27                                 USA  \n",
       "28                              Sweden  \n",
       "29                             Germany  \n",
       "..                                 ...  \n",
       "945                            Germany  \n",
       "946                                USA  \n",
       "947                                NaN  \n",
       "948                                NaN  \n",
       "949                                NaN  \n",
       "950                             France  \n",
       "951                                USA  \n",
       "952                              Japan  \n",
       "953                              China  \n",
       "954                              Japan  \n",
       "955                             Canada  \n",
       "956                     United Kingdom  \n",
       "957                     United Kingdom  \n",
       "958                                USA  \n",
       "959                                USA  \n",
       "960                                USA  \n",
       "961                                NaN  \n",
       "962                                NaN  \n",
       "963                                USA  \n",
       "964                              Japan  \n",
       "965                                USA  \n",
       "966                                USA  \n",
       "967                                USA  \n",
       "968                             France  \n",
       "969                                USA  \n",
       "970                    the Netherlands  \n",
       "971                                NaN  \n",
       "972                                USA  \n",
       "973                                USA  \n",
       "974                                NaN  \n",
       "\n",
       "[975 rows x 20 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "# dataset from http://oppnadata.se/en/dataset/nobel-prizes/resource/f3da8ba9-a17f-4911-9003-4bcef93619cc\n",
    "nobel = pd.read_csv(\"data/nobels.csv\")\n",
    "nobel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add column with ones (one Nobel prize per laureate...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nobel[\"number\"]=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"red\"> *Exercise*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use the groupby method and `sum()` to extract total numbers of Nobel prizes by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract four countries with the highest number of prizes, and generate some plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries = np.array([\"Germany\", \"France\", \"USA\", \"United Kingdom\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nobel2 = nobel.loc[nobel['country'].isin(countries)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KodYAuj9x8RjBhk4gnMIlHaiqqmTPnl0U2OyWiAXAWLuDIarK5s0f0draYokN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\n3boojLT1SmNje/wM74Wammp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2hh4AI8SVV15Hbm6e1eb0Tnr+GixFVVVmzpzD1q2buW1I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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c46590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(y=\"year\", x=\"country\",inner=\"stick\", data=nobel2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use multiple conditions. Let's extract only physics prizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S+UiShFLko+Ai57ffwl1jDL7e5tpEerNF162aIwttchMujwnG4cM1nH/+hbaM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\nxhhj8mIBwxhjTF4sYBhjjMnL/wNObLHP74Z4XQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b6b1090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nobel3 = nobel.loc[nobel['country'].isin(countries) & nobel['category'].isin(['physics'])]\n",
    "sns.violinplot(y=\"year\", x=\"country\",inner=\"stick\", data=nobel3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gOvnweTKH7VX1gF8zDOMi8Angc7qunwN+A/iKYRg68FXgNwF0Xb8AfBY4D/x9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mS5pJ5DROQbuMbO+867sTHzRERqVDXrr7J/ClynqmcWu1zm2VtKdYDmAvm+3A8BP7dgYZaAb4pI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\nDGOMMVWxgGGMMaYqFjCMMcZU5T8Bsc4XvvKqMKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115af3590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(y=\"year\", x=\"country\", data=nobel2, alpha=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d3KyUyh5RLqPFbr8Ba8XHfwZSgZBSahBr9cd5Iw5bANRf6rODwZDpcjkn6lSEELOPcelN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bccbad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"country\", y=\"year\", col=\"category\", data=nobel2, kind=\"swarm\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X2/e1JIfNef62JGfseFJVdxsfvivDH4NU1UMznJWdbx3XJXn8MGst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\nAAAAAMDU/f+AwP6Z3/RVmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc6d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"country\", col=\"category\", data=nobel2, kind=\"count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Other types of media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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XFGX2DiXFaVH+VkrA8T0dGVnV9jy2GswziV7VWNW10tsZrNdHEfFbVnGnJpeN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\nPPVnlmfdwopZf5ao+mfwNct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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/j9YpkSX7NNM\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x11846e190>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('j9YpkSX7NNM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source 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\" type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Audio\n",
    "Audio(\"data/GW150914_L1_whitenbp.wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}