1-UN-speeches-initial-data-intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db6d79b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "234e52bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in subsequent iterations\n",
    "df = pd.read_pickle('df_un_general_debates.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeaed947",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c955235e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv\n",
    "df = pd.read_csv(\"un-general-debates-blueprint.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d3d91fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7507 entries, 0 to 7506\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   session       7507 non-null   int64 \n",
      " 1   year          7507 non-null   int64 \n",
      " 2   country       7507 non-null   object\n",
      " 3   country_name  7507 non-null   object\n",
      " 4   speaker       7480 non-null   object\n",
      " 5   position      4502 non-null   object\n",
      " 6   text          7507 non-null   object\n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 410.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b04a650",
   "metadata": {},
   "source": [
    "## Double check variable \"position\" \n",
    "* AKA let's inspect these non-NaN values\n",
    "\n",
    "* There are also a handful of NaN for the variable \"speaker\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4529c92d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session</th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>country_name</th>\n",
       "      <th>speaker</th>\n",
       "      <th>position</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25</td>\n",
       "      <td>1970</td>\n",
       "      <td>CMR</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Mr. AHIDJO</td>\n",
       "      <td>President</td>\n",
       "      <td>: A year ago I came here as the Acting Preside...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25</td>\n",
       "      <td>1970</td>\n",
       "      <td>DOM</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Mr FERNANDEZ G.</td>\n",
       "      <td></td>\n",
       "      <td>\\n\\n\\n Mr. President, it was a source of great...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>1970</td>\n",
       "      <td>GMB</td>\n",
       "      <td>Gambia</td>\n",
       "      <td>Mr. JAWARA</td>\n",
       "      <td>President</td>\n",
       "      <td>2.\\tPresident : President of Gambia . Mr. Pres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>26</td>\n",
       "      <td>1971</td>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Mr. ECHEVERRIA ALVAREZ</td>\n",
       "      <td>President</td>\n",
       "      <td>2.\\t Mr. President, on behalf of the people an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>28</td>\n",
       "      <td>1973</td>\n",
       "      <td>BHS</td>\n",
       "      <td>Bahamas</td>\n",
       "      <td>Pindling</td>\n",
       "      <td>Prime Minister</td>\n",
       "      <td>﻿As this is the first opportunity which the Co...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7502</th>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>Mr. Abdrabuh Mansour Hadi Mansour</td>\n",
       "      <td>President</td>\n",
       "      <td>On behalf of the people and the Government of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7503</th>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>YUG</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>Mr. Tomislav Nikolić</td>\n",
       "      <td>President</td>\n",
       "      <td>\\nSeventy years have passed since the establis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7504</th>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Jacob Zuma</td>\n",
       "      <td>President</td>\n",
       "      <td>I should like to congratulate the President an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7505</th>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Mr. Edgar Chagwa Lungu</td>\n",
       "      <td>President</td>\n",
       "      <td>I would like to begin by thanking the Secretar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7506</th>\n",
       "      <td>70</td>\n",
       "      <td>2015</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Robert Mugabe</td>\n",
       "      <td>President</td>\n",
       "      <td>Allow me at the outset to extend to Mr. Mogens...</td>\n",
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       "<p>4502 rows × 7 columns</p>\n",
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       "      session  year country        country_name  \\\n",
       "9          25  1970     CMR            Cameroon   \n",
       "14         25  1970     DOM  Dominican Republic   \n",
       "21         25  1970     GMB              Gambia   \n",
       "135        26  1971     MEX              Mexico   \n",
       "323        28  1973     BHS             Bahamas   \n",
       "...       ...   ...     ...                 ...   \n",
       "7502       70  2015     YEM               Yemen   \n",
       "7503       70  2015     YUG          Yugoslavia   \n",
       "7504       70  2015     ZAF        South Africa   \n",
       "7505       70  2015     ZMB              Zambia   \n",
       "7506       70  2015     ZWE            Zimbabwe   \n",
       "\n",
       "                                speaker           position  \\\n",
       "9                            Mr. AHIDJO         President    \n",
       "14                      Mr FERNANDEZ G.                      \n",
       "21                           Mr. JAWARA         President    \n",
       "135              Mr. ECHEVERRIA ALVAREZ         President    \n",
       "323                            Pindling  Prime Minister      \n",
       "...                                 ...                ...   \n",
       "7502  Mr. Abdrabuh Mansour Hadi Mansour          President   \n",
       "7503               Mr. Tomislav Nikolić          President   \n",
       "7504                         Jacob Zuma          President   \n",
       "7505             Mr. Edgar Chagwa Lungu          President   \n",
       "7506                      Robert Mugabe          President   \n",
       "\n",
       "                                                   text  \n",
       "9     : A year ago I came here as the Acting Preside...  \n",
       "14    \\n\\n\\n Mr. President, it was a source of great...  \n",
       "21    2.\\tPresident : President of Gambia . Mr. Pres...  \n",
       "135   2.\\t Mr. President, on behalf of the people an...  \n",
       "323   ﻿As this is the first opportunity which the Co...  \n",
       "...                                                 ...  \n",
       "7502  On behalf of the people and the Government of ...  \n",
       "7503  \\nSeventy years have passed since the establis...  \n",
       "7504  I should like to congratulate the President an...  \n",
       "7505  I would like to begin by thanking the Secretar...  \n",
       "7506  Allow me at the outset to extend to Mr. Mogens...  \n",
       "\n",
       "[4502 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.query('position.notnull()', engine='python')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ce45bb",
   "metadata": {},
   "source": [
    "* given this data won't be needed for our topic models, we will just leave it be "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d57283af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4591018f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session            0\n",
       "year               0\n",
       "country            0\n",
       "country_name       0\n",
       "speaker           27\n",
       "position        3005\n",
       "text               0\n",
       "length             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 27 missing speaker values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "28ec3bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as speaker is not overly important to us (looking for trends by country, year and topic of the speech) \n",
    "## can just quickly fill in these missing values with just that \"unknown\"\n",
    "df['speaker'].fillna('unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61778d6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20eb39fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c3fd34b0",
   "metadata": {},
   "source": [
    "## Basic overview of text stats\n",
    "* Get a general feel for the speeches\n",
    "* save the in-depth visuals for notebook #2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09ca7c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as text is \"object\" dtype, first convert to \"string\" then apply len, assign to new col named \"length\"\n",
    "df['length'] = df['text'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1735480",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>top</th>\n",
       "      <th>freq</th>\n",
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       "      <th>country</th>\n",
       "      <td>7507</td>\n",
       "      <td>199</td>\n",
       "      <td>ALB</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speaker</th>\n",
       "      <td>7480</td>\n",
       "      <td>5428</td>\n",
       "      <td>Seyoum Mesfin</td>\n",
       "      <td>12</td>\n",
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      "text/plain": [
       "        count unique            top freq\n",
       "country  7507    199            ALB   46\n",
       "speaker  7480   5428  Seyoum Mesfin   12"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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    }
   ],
   "source": [
    "# inspect summary for non-numerical columns\n",
    "df[['country','speaker']].describe(include='O').T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db16eaa7",
   "metadata": {},
   "source": [
    "* 199 unique countries\n",
    "* 5428 unique speakers seems ... perhaps extreme\n",
    "    * will need to investigate further "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a095cd3d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>session</th>\n",
       "      <td>7507.0</td>\n",
       "      <td>49.610763</td>\n",
       "      <td>12.892155</td>\n",
       "      <td>25.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>7507.0</td>\n",
       "      <td>1994.610763</td>\n",
       "      <td>12.892155</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>7507.0</td>\n",
       "      <td>17967.281604</td>\n",
       "      <td>7860.038463</td>\n",
       "      <td>2362.0</td>\n",
       "      <td>12077.0</td>\n",
       "      <td>16424.0</td>\n",
       "      <td>22479.5</td>\n",
       "      <td>72041.0</td>\n",
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       "          count          mean          std     min      25%      50%      75%  \\\n",
       "session  7507.0     49.610763    12.892155    25.0     39.0     51.0     61.0   \n",
       "year     7507.0   1994.610763    12.892155  1970.0   1984.0   1996.0   2006.0   \n",
       "length   7507.0  17967.281604  7860.038463  2362.0  12077.0  16424.0  22479.5   \n",
       "\n",
       "             max  \n",
       "session     70.0  \n",
       "year      2015.0  \n",
       "length   72041.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# repeat for numerical columns\n",
    "## obviously, mean and std etc. for year/session are non-sensical\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3659587f",
   "metadata": {},
   "source": [
    "* given we know the data is from 25th to the 70th UN generate debate sessions; the years 1970-2015 make sense\n",
    "* Not much to do with the stats about length just yet\n",
    "    * but we can see there is a right-skewed distribution \n",
    "    * Apparent also as the average is greater than the median + max value is far from the average while min is a fair amount closer to the centre of the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0831fdcc",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# just for curiosity, we can confirm visually this right-skew with a simple KDE \n",
    "sns.kdeplot(data=df, x='length');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa0026c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4745424",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3b78fd07",
   "metadata": {},
   "source": [
    "### Also inspect some specifics on length for the permanent members of the UN security council "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f47ed527",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a mask variable for the main DF where the data is in (united states, france, great britan, china, russia) respectively\n",
    "un_perm_members = df['country'].isin(['USA','FRA','GBR','CHN','RUS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7ff1f2be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df[un_perm_members], \n",
    "           x = 'country',\n",
    "           y = 'length', \n",
    "           kind='box');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2f6ec791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mSDq4FHMIRQ2kHLN/WrdqLkUSeXCEx2NmNqE1VKOJiDcBSPoH4M9G2htL0l8D36LoJTadoovyUcCxERGSFgMLJS0HlgMLgd9QdFcmIvokfRlYJGkN8BxwMfAwcHuK+bGkW4CrJX0w7foq4KbU4wyKzgSPAEskfZziXNPFwNUjPcaIoFJptDWxMeXt595XVU9Pj2twZjYsTQ1BExHvb9H+d6O4nmV3ipPpP6RIMrel5RcBU4ErgJnAA8Dc0jU0AGdSjLv29RR7B3By6RoagAXApQz0TrsBOK10PJskvTXt5x5gHUUy+9hID7BSqTBv3ryRbmbYqneazK23t9c3GDOzYWl29OZvDrIoKDoDPA5cV6ox1A+OOGWI5QGcn6bBYioUF1UOemFlRDwHnDDEvp6kGCTUzMxaqNlBNV8A3kkxWsCDFOdVDgB2oqg1HAecI+ktEXHPiEs5Tqz9gwWwXYa7Z0fA5o3F4+0mQa4mrc0b2f57S/Ns28zGrWa/9VZSNC2dFhGbASRtB3wBeJHiXMuXKHqOHdGCco4P202CrsmZNu5h2cysMzWbaE4BXl9NMgARsVnSZcC9EbFQ0heB/2xFIc3GIncEMSs0m2gmUXRnfqxm/isZGFCzgm+IZhOYO4KYFZpNNEuAL6fuyd+hSCgHU3Q//scUcyTwoxGX0MzMxrRmE82ZFFfMf4KBq+5XAX/LwBX9twK3jKh0ZuPEprdvav7Tti0BVDvyd1F0y8lhI3TdONy7f5htrdnraDYBnwM+V73RWe2Fjam7sJlB8UnLkWigGCjJrION+F/f92oxM7NtaWr0Zkm7pdshr5C0UdKm8tTqQpqZ2djVbI3mGmAv4DMUIyu7d5mZbTEaXbth9Lt3u2t3c5pNNEcAb4iI77ewLGY2Tox2124Yne7d7trdnGZvfPYU+fq3mJnZONJsjeYM4POSPhgRP29dccxsvDmHfAMkBcUNqaDofJfj1+96Bq7ZsOY0m2i+BkwDfprusrmhvDAidh5pwcxsfJgCTMnYANKdbctVPgU9UiOp0ZiZmQ2p2Qs2r211QczMbHxqtjMAkl4u6bOS/knSrDTvWEmval3xzMxsrGv2gs0jgYeBQ4B3AzukRa8BPtWaopmZ2XjQbI3m88AnI+IYik4ZVXcCh424VGZmNm40m2heDfxbnfnPALs0XxwzMxtvmk00vwJ2rzP/AOAXTZfGzMzGnWYTzXXAhZJmU3Qy307S64GLGbjxmZmZWdOJ5jzgSYrayw7AI8B/AvcCn21N0czMbDxo9jqaDcACSX8B/AFFwnooIpa3snBmZjb2DTvRSLpkiJBDq8NnR8RZIymUmZmNH43UaA4YZpwHBjIzsy2GnWgi4k05C2JmZuNT00PQmJmZDYcTjZmZZeVEY2ZmWTnRmJlZVk40ZmaWVbN32LRhiij19t60YfDAsaBU/q2Oy8xsG5xoMuvv79/yePuHrmtjSVqrv7+fadOmbTMmIqhUKtnLUt7HaOyvp6eH6sXJZjY0JxrLplKpMG/evFHd5/z587Pvo7e3l6lTp2bfj9l44USTWXd395bHaw94H3RNbmNpRmjThi21svJxWX1bNS9ubF85WqJUfjebWqOcaDLbqomla/LYTjQljTYdXf7GX9HdlecLKgLWby4eT9kOcrRq9W8SH/n2To2tU2o27bqxq8Ulap/hNJualTnR2Kjo7gp6Mn7X5m/I8q94s2Y50ZhlUm5e3PT2TWP707ZxoFbmZlNr1Fj+1zfraFs1L05i3Hza3OPOGuULNs3MLCsnGjMzy8qJxszMsmpropF0rqTvSHpR0mpJ/y5pn5oYSTpf0gpJ6yTdJelVNTHdki6T9KyktZJukLRHTcxMSUsk9aVpiaSdamL2knRj2sazki6VNCXbC2BmNgG0u0ZzJHA5cChwDMXp0lslbV+K+QRwFnAacBCwErhN0vRSzGLgXcDxwBHADsBNksodaq8D5gDHpmkOsKS6MMXeDGyftnE88B5gUSsO1MxsomprP5iIOLb8XNL7gdXAgcC3VXRvOQP4XER8M8WcBKwC3gdcKWkGcApwYkTcnmJOAJ4CjgZ6Je1LkVwOjYgHUsypwH2S9omIR4G5wH7AnhGxIsWcDVwj6byIeCHjS2E2rpRHD1hfzGlXUUZsfemxR0VoTqd1uJyR/j6X/u4NzAZurQZERL+ku4HDgSspktLkmpgVkpalmF7gMKCvmmRSzP2S+lLMoylmWTXJJL1Ad9rHnbWFldSdlldNr40xm4jKoyJc2MZytJpHRWhOu5vOtki1l0uA/4qIZWn27PR3VU34qtKy2cD6iHh+iJjVdXa7uiZmq/2kba4vxdQ6F+grTU8PEmdmNmF1Uo3mi8BrKM6P1Kqtr6rOvFq1MfXim4kpu4AiOVZNx8nGbKvRA84BxnKPmvUM1Mo8KkJzOiLRSLoMeAfwxogof1GvTH9nA78szZ/FQO1jJTBF0syaWs0s4N5SzG51dr1rzXYOqSnXTIpmudoaFVA04wH9pfh6YWYTTvmzMAWYwlj+bAz8zvRnvDnt7t4sSV8E3g28OSKeqAl5giIBHFNaZwpFb7VqEnkQ2FATszuwfynmPmCGpINLMYdQnBMqx+yf1q2aS5FIHhzBYZqZTWjtrtFcTtF7bD7woqTquZC+iFgXESFpMbBQ0nJgObAQ+A1Fd2Uiok/Sl4FFktZQdCS4GHgYuD3F/FjSLcDVkj6Y9nEVcFPqcQZFZ4JHgCWSPg7snLZztXucmVkjyr3TNo7xW7hvbMEt3NudaD6U/t5VM//9wDXp8UUUo8BfAcwEHgDmRsSLpfgzKW7N9PUUewdwckRsKsUsAC5loHfaDRTX5gAQEZskvTXt5x5gHUUy+1jTR2dmE1K5190NP7iijSVprWZ73bX7OpohGzyjSKHnp2mwmApwepoGi3kOOGGIfT0JvG2oMpmZ2fC1u0ZjZjbulHunveO1H2bSGL6z7sZNG7bUyprtdedEY2bWYuXeaZO6JjOpayx38B7QbK+7jrlg08zMxicnGjMzy8qJxszMsnKiMTOzrNwZwGw0bMy03QCqV4t1QbaRXnKV3yYEJxrLpnwVcf+mbQSOAeXyN3N1dNeNXUMHmY1TTjSWTfnq6I98e2YbS9JavieJWWOcaMwy6enpobe3N+s+KpUK8+fPB+D666+np6cn6/6AUdmHjS9ONJZN+Sriy9/4PN1juPWof9NArWy4V0dLYurUqTmLtZWenp5R3Z/ZcDnRWDblq4i7u6BnDCeaMt+TxKwx7t5sZmZZOdGYmVlWTjRmZpaVE42ZmWXlRGNmZlm515mZZbUeKMbKab0Aqne0n0yeEXjWZ9jmRONEY2ZZXdjuArTZxs0bhg5qQkSwaXMxCF3XdpOydbtvRfmdaMzMMrrh+1e0uwht50RjZi03GsPvwOgPwePhd5rjRDOaNmcaaz1iYNvbTYJcV67nKr+NO6M9/A501hA8Hudua040o2j77y1tdxHMbBR4nLutuXuzmZll5RpNZq5Cm9lE50STmavQZjbRuenMzMyycqIxM7OsnGjMzCwrJxozM8vKicbMzLJyojEzs6ycaMzMLCsnGjMzy8oXbNqo6N8kst38KmD95uLxlO3yjClalN/MmuFEY6PiI9/eqd1FMLM2cdOZmZll5RqNZeObX5kZONFYRhP95ldmVnCiMesgEUGlUhl2fDm2kfWqenp6UK47spolTjRmHaRSqTBv3rym1q02Hzait7fXNUDLzp0BzMwsK9dozDpIox0oIoL+/n4Auru7G24Gc8cGGw1trdFIeqOkGyWtkBSS3lmzXJLOT8vXSbpL0qtqYrolXSbpWUlrJd0gaY+amJmSlkjqS9MSSTvVxOyVyrI2betSSVNyHbtZPdUOFMOdpk2bxsyZM5k5cybTpk1raN2pU6f6/IyNinY3nW0P/AA4bZDlnwDOSssPAlYCt0maXopZDLwLOB44AtgBuElSVynmOmAOcGya5gBLqgtT7M2pPEekbb0HWDSCYzMzM9rcdBYR3wK+Bbzkl5WKGWcAn4uIb6Z5JwGrgPcBV0qaAZwCnBgRt6eYE4CngKOBXkn7UiSXQyPigRRzKnCfpH0i4lFgLrAfsGdErEgxZwPXSDovIl7I9yqYmY1v7a7RbMvewGzg1uqMiOgH7gYOT7MOBCbXxKwAlpViDgP6qkkmxdwP9NXELKsmmaQX6E77qCs12+1YnYDpg8WamU1UnZxoZqe/q2rmryotmw2sj4jnh4hZXWf7q2tittpP2ub6Ukw951IkrOr09DZizcwmpE5ONFW1Q/4OZxjg2ph68c3E1LoAmFGa9thGrJnZhNTJiWZl+ltbo5jFQO1jJTBF0swhYnars/1da2K22k/a5mReWqPaIiL6I+KF6gS8OPjhmJlNTJ2caJ6gSADHVGek7sZHAvemWQ8CG2pidgf2L8XcB8yQdHAp5hCKGkg5Zv+0btVcoD/tw8zMmtTWXmeSdgB+rzRrb0lzgOci4klJi4GFkpYDy4GFwG8ouisTEX2SvgwskrQGeA64GHgYuD3F/FjSLcDVkj6Y9nMVcFPqcQZFZ4JHgCWSPg7snLZztXucmZmNTLtHBngdcGfp+SXp77XAycBFwFTgCmAm8AAwNyLKTVRnAhuBr6fYO4CTI2JTKWYBcCkDvdNuoHTtTkRskvTWtJ97gHUUyexjIz5Ca0ijg0qCB5Y063Ttvo7mLooT7oMtD+D8NA0WUwFOT9NgMc8BJwxRlieBt20rxvIbyaCS4IElx7LR/pHhHxijp901GjMzYPR/ZPgHxuhxorGO0sxdOT2wpFlnc6KxjtLsXTmnTZuWoTQ2mkb7R4Z/YIweJxoz6wj+kTF+dfJ1NGZmNg440ZiZWVZONGZmlpUTjZmZZeVEY2ZmWbnXWQdq9AppD8FiNraN96GXVIzyYq2Q7rLZ19fXx4477tj0dtatWzeiK6Qb5SukzdprtD/z0NLP/ZDZyk1nZmaWlWs0LdSqGk2j1ehWDMHipjOz9mmm6ayDPvdDbsTnaDpQM1dI++pos7FrvI+K4KYzMzPLyonGzMyycqIxM7OsnGjMzCwrJxozM8vKicbMzLJyojEzs6ycaMzMLCtfsJnBCy+80O4imJmNihkzZuwIvBjbGGbGQ9C0kKSXAU+3uxxmZqNsRkQM+gvbiaaFVAwc9NvAi23Y/XSKJLdHm/bfbhP5+CfyscPEPv5OOfZt1mjcdNZC6YX+RTv2XRoc78Vt/bIYryby8U/kY4eJffxj5djdGcDMzLJyojEzs6ycaMaPfuBT6e9ENJGPfyIfO0zs4x8Tx+7OAGZmlpVrNGZmlpUTjZmZZeVEY2ZmWTnRmJlZVk40HUzSNZKizvR7Ncs2SnpS0t9JmllnO1MlPS/pOUlT23EszZA0W9IXJD0uqSJplaT/kvR/JU1LMT8vvQ6bJK2Q9OXy6yDpqJrXb42k/5D0+vYd3eAk3SVpcZ3575QU6XGXpHMl/UTSuvTe3i/p/XXWG1Pvf3rfL5P0M0n9kp6SdKOkt6TlP5d0Rp31zpf0/ZrnIelLNXFz0vzfzXwoDRvO5zote2eddRdLuqv0fJakK9M2+iWtlNQr6bDROZoBTjSd7xZg95rpiZplvwv8CfB24Io623gPsAx4BHh33uK2hqT/BTwEzAUWAgcARwN/S3GcR5fC/5LiddgLWAC8Ebi0zmb3SXFHAc8AN0ualecIsjsfOAP4C2A/4E3A1cBLfmgwht7/9OX/IPBm4BPAq4FjgTuBy5vYZAU4RdLvt6qMo2C4n+uhfAN4LXAS8PvAO4C7gJ1bUchGeAiaztcfEStrZ6ahJ8rLnpb0NeDkOts4BfgqoPR4aZ6ittQVwEbgdRGxtjT/YeAbKo29QTH8RvV1+IWkfwSOr7PN1RHxK2ClpM8C/wc4BLix5aXP7+3AFRHxL6V5Pxgkdiy9/1cAARxc877/SNJXmtjeo8BqoPp+jwXD/VwPStJOwBHAURFxd5r9P8B/t6qQjXCNZpxINYBjgQ01818OHAZ8PU2Hp9iOJWkXiprM5TVfNlsMNoBfGkH7bcAD29j+NKDaxLRhsLgOtxJ4s6RdtxU0lt5/STtT/A/Xfd/Tj4Rm/DnwHkkHjaB4bTHY53oYfp2md0rqbnnBGuRE0/neJunXpelf6ixbB/yUognlwpr1PwB8KyKej4jnKKrlHxidojft9yh+fT9aninp2dLrUD7OC0uvw9MUv4jPqrPdpyVVP4BnUjTR3JHlCPI7C9iVonb2Q0lfkvSHdeLG0vtffd9/MozYC2s+F7+maGJ9iYj4HkWS/XzriprVcD7X2xQRGylqQScBv5J0j6S/lvSalpd2GJxoOt+dwJzS9NE6yw4BLgN601+gOGFM8Y/21dI6XwVOSss6XW2t5WCK4/0RUP6V9jdp/muAt6R5N9c5xjcAfwD8EUUzwskRMSZrNBHxCLA/cCjwD8BuwI2S/r4aMwbf/2pz6HCGK6m+5+XpS4NGwyeBN0ia23TpRs82P9fDFRHfoLhtyTvSNo4Cvifp5BaVc9icaDrf2oh4vDT9ss6yH0bERym+fP+qtHwe8DLga6kHy0bgnynuXdHJH7jHKb5sXlmeGRE/i4jHgXU18c+m12F5RPwHxUnywylOkJc9ERGPRcTXKF6nf+uEZoU6XgBm1Jm/U1oGQERsjojvRMTfRsS7KH7BniJp7xQy1t7/5RTv+77DiH225nPxOPDcYMER8VOKzhKfZyChdaqhPtcvMvj/R195RkRUIuK2iPh0RBwOXEMxNtqocqIZXz4FfEzSb6fnp1B8scypmZamZR0pItYAtwGnSdq+iU1sSn+31ZV3CcX//4eb2H5uPwFeV2f+QdQ0J9Z4JP2tvmZj6v1PTXu9wEfqve/pBPdIfJqi91W9jiKdrPZz/ROK/4UtUueYA9n2/wcU/yPNfKZGxIlmHImIuyialRamk8RvB66NiGXlCbgWeMdQJ5Lb7MMUvSK/K+k4SftK2kfSCRQ1nU2l2Onp2ovdJR1M0azyLHDvYBuPiM3AYuDPU+eATnIF8HJJl0t6raTfl/QRiuTwNwCS/lXSmZIOkfQ7ko6i6P77GPCTMfz+fxjoAv5b0nskvSK99x8F7hvJhiNiFXAJWzc/d7zy5zrNupii5npa+t94LfBF4OWkLuCSdlFxrdgJkl4jaW9J76XoMn79aB+DE834cwlwKsUHdi31T3bfSVH9PnEUy9WQ1NRxAHA7cAFF193vAqdTfND+ohT+aeCXwArgJorjPibVjLblK8Bk4LSWFn6EIuLnFOeTXg7cCnyHolns5FJ35l6KRHIjRXK5luKX7tx0IviPGYPvf0Q8QXEe7U5gEcX1P7dRnHv7UAt28TcUnUHGmkuAUyXtGRFfZ+BE/3co/kdeDrwhIv4nxf+aouflmcC3KV7Hz1A0H476/7tvE2BmZlm5RmNmZlk50ZiZWVZONGZmlpUTjZmZZeVEY2ZmWTnRmJlZVk40ZmaWlRONmZll5URjNs5J+t10+9857S6LTUxONGYGgKQp7S6DjU9ONGaZSdpO0jmSHpfUL+lJSeelZa9Ogx+uk7RG0lWSdiite5ekxTXb+3dJ15Se/1zSQklfkfRi2v6fllZ5Iv19KNVs7krrXZO2da6kFcBjkv5S0sN1juFBSZ9u1WtiE4sTjVl+FwDnUAxquB/wPmBVGjX6FuB5imHf3wscTTESb6POphh09ACK0Z//TlL1fj4Hp79HA7sD7y6t9xaK+78cQ3EL7K8A+5Vve5zuyngAxb1MzBo2qd0FMBvPJE0H/gw4LSKuTbN/CvyXpFMp7pnzxxGxNsWfRnGnzHPSsPbD9f8i4oq0jQspRu09imJE52dSzJqIWFmz3lrgTyJifanMvcD7KUYGJj2+OyJ+1kB5zLZwjcYsr30p7pBYb7j+fYEfVJNMcg/F53KfBvfzw+qDKIZkXwnMGsZ6D5eTTHI18EeSeiRNBhZQ1HTMmuIajVletbedLhPFrYvrqc7fzEtvPTy5TvyGOusP54fk2jrzbgT6gXelv93AN4axLbO6XKMxy2s5RbJ5S51ljwBzam5b/HqK5PJYev4MxXkVACR1Afs3WIZqjaVrOMHpxmnXUjSZvR/454j4TYP7NNvCNRqzjCKiks6ZXCRpPUXT2K7Aq4ClFPeDv1bS+Wn+ZcCS0vmZ/wAukfRWinM7ZwI7NViM1RTJ7lhJTwOViOgbYp2/B36cHr++wf2ZbcU1GrP8PkNxW+JPU3x5fw2YlWoJ84CdKU68/yvFuZzyrXa/QlG7+Efgboquync2svNUQ/ko8EGK210Pec/4iFgO3As8GhEPNLI/s1q+lbOZvYQkUfRYuzIiLml3eWxsc9OZmW1F0izgROBlwD+0uTg2DjjRmFmtVcCzwJ9GxPPtLoyNfW46MzOzrNwZwMzMsnKiMTOzrJxozMwsKycaMzPLyonGzMyycqIxM7OsnGjMzCwrJxozM8vKicbMzLL6/2VgPI5nCFtkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plots \n",
    "g = sns.catplot(data=df[un_perm_members], x=\"country\", y=\"length\", kind='box')\n",
    "g.fig.set_size_inches(4, 3)\n",
    "g.fig.set_dpi(100)\n",
    "\n",
    "# violin plots \n",
    "g = sns.catplot(data=df[un_perm_members], x=\"country\", y=\"length\", kind='violin')\n",
    "g.fig.set_size_inches(4, 3) \n",
    "g.fig.set_dpi(100) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4babf1",
   "metadata": {},
   "source": [
    "* Interestingly, russia has the largest dispersion in speech length, with 2 peaks in the lower and upper length ranges\n",
    "* USA mean length is more or less the longest, but the values are no so dispered, so fairly consistent range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "522574f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa9da991",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c05921d2",
   "metadata": {},
   "source": [
    "## Try inspect the large amount of unique speaker values\n",
    "* let's start with a country where we are more familiar with the presidents (aka more recognisable names) \n",
    "    * E.G. the USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e8fad03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barack Obama\n",
      "Bill Clinton\n",
      "Bush\n",
      "Condoleezza Rice\n",
      "GEORGE BUSH\n",
      "George Bush\n",
      "George W. Bush\n",
      "Jimmy Carter\n",
      "Kissinger\n",
      "Madeleine Korbel Albright\n",
      "Mr Haig \n",
      "Mr.  Kissinger\n",
      "Mr. Christopher\n",
      "Mr. George BUSH\n",
      "Mr. George W Bush\n",
      "Mr. George W. Bush\n",
      "Mr. Kissinger\n",
      "Mr. ROGERS\n",
      "Mr. Rogers \n",
      "Mr. Shultz\n",
      "Mr. William Clinton\n",
      "Mr. YOST\n",
      "Muskie\n",
      "President Reagan\n",
      "RONALD REAGAN\n",
      "Reagan\n",
      "Ronald Reagan\n",
      "SHULTZ \n",
      "Vance\n",
      "William Clinton\n",
      "William J. Clinton\n",
      "William Jefferson Clinton\n"
     ]
    }
   ],
   "source": [
    "# let's print out each unique speaker for the USA\n",
    "for i in (np.unique(df.query('country==\"USA\"')['speaker'])):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c5953b",
   "metadata": {},
   "source": [
    "* We can see a few different iterations for Bush, Kissinger, Clintons etc. \n",
    "* There are multiple spellings for multiple different speakers, most likely this is for every country too\n",
    "##### given our analysis is not initially focused on the speaker, we can ignore this for now and rectify it later if we want to dive deeper into the speaker analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "99f08150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "George W. Bush        4\n",
       "Mr. George W. Bush    2\n",
       "Bush                  1\n",
       "George Bush           1\n",
       "Mr. George W Bush     1\n",
       "Name: speaker, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#E.G. all the different spellings containing \"bush\"\n",
    "df[df['speaker'].str.contains('Bush')]['speaker'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86425da9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ee2155",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "def6d64f",
   "metadata": {},
   "source": [
    "## AGGREGATE TRENDS OVER TIME \n",
    "* E.G. Avg speech length across each convention / number of unique countries participating since 1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "057f77e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countries participating by year \n",
    "df.groupby('year').size().plot(title=\"Number of Unique Countries over Time\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9553b162",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9718aca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## AVG speech length (aggregate average) by year\n",
    "df.groupby('year')['length'].mean().plot(title='AVG Speech Length over Time');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd8493a3",
   "metadata": {},
   "source": [
    "* Somewhat makes sense that as more countries join, the time each respective country has to talk has reduced to stop overall runtime from exploding\n",
    "    * An interesting titbit, but nothing groundbreaking yet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1952129",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4811148b",
   "metadata": {},
   "source": [
    "## PICKLE DF FOR NEXT STEPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00ba18cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_pickle('df_un_general_debates.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87d10851",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ab8771b0",
   "metadata": {},
   "source": [
    "## NEXT STEPS: DATA-CLEANING & TOKENIZE\n",
    "* Thus, enabling deeper analysis and visualisations\n",
    "    * See notebook (2)-UN-speeches-data-cleaning-and-tokenisation in github repo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a12256c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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