{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scientist Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_json(\"https://raw.githubusercontent.com/dariusk/corpora/master/data/humans/scientists.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>scientists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>List of particularly famous scientists</td>\n",
       "      <td>Aage Bohr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>List of particularly famous scientists</td>\n",
       "      <td>Abdul Qadeer Khan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>List of particularly famous scientists</td>\n",
       "      <td>Abu Nasr Al-Farabi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>List of particularly famous scientists</td>\n",
       "      <td>Ada Lovelace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>List of particularly famous scientists</td>\n",
       "      <td>Adalbert Czerny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              description          scientists\n",
       "0  List of particularly famous scientists           Aage Bohr\n",
       "1  List of particularly famous scientists   Abdul Qadeer Khan\n",
       "2  List of particularly famous scientists  Abu Nasr Al-Farabi\n",
       "3  List of particularly famous scientists        Ada Lovelace\n",
       "4  List of particularly famous scientists     Adalbert Czerny"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_raw[ ['scientists'] ].copy()\n",
    "df['first_name'] = df.scientists.apply( lambda x: x.split(' ')[0] )\n",
    "df['last_name'] = df.scientists.apply( lambda x: x.split(' ')[-1] )\n",
    "df['len_diff'] = df.apply( lambda x: len(x.first_name) - len(x.last_name), axis=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scientists</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>len_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aage Bohr</td>\n",
       "      <td>Aage</td>\n",
       "      <td>Bohr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abdul Qadeer Khan</td>\n",
       "      <td>Abdul</td>\n",
       "      <td>Khan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abu Nasr Al-Farabi</td>\n",
       "      <td>Abu</td>\n",
       "      <td>Al-Farabi</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ada Lovelace</td>\n",
       "      <td>Ada</td>\n",
       "      <td>Lovelace</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adalbert Czerny</td>\n",
       "      <td>Adalbert</td>\n",
       "      <td>Czerny</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           scientists first_name  last_name  len_diff\n",
       "0           Aage Bohr       Aage       Bohr         0\n",
       "1   Abdul Qadeer Khan      Abdul       Khan         1\n",
       "2  Abu Nasr Al-Farabi        Abu  Al-Farabi        -6\n",
       "3        Ada Lovelace        Ada   Lovelace        -5\n",
       "4     Adalbert Czerny   Adalbert     Czerny         2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119e2ed30>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x444.96 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots( figsize=(10,6.18) )\n",
    "df.plot.hist(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "vscode": {
   "interpreter": {
    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}