prenoms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "76097195",
   "metadata": {},
   "source": [
    "# prenoms - cgr v231801.1\n",
    "## analyseur de prénoms base INSEE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a53a39c",
   "metadata": {},
   "source": [
    "### Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52885dae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-18T08:02:57.537316Z",
     "start_time": "2023-04-18T08:02:57.518110Z"
    }
   },
   "outputs": [],
   "source": [
    "# mobile average of value dictionary d in x with window size w\n",
    "def ma(d,x,w): return(np.average([d[i] for i in range(x-w,x+w) if i in d]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08cb53a3",
   "metadata": {},
   "source": [
    "#### Importation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eea420d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-18T08:02:59.840021Z",
     "start_time": "2023-04-18T08:02:57.540013Z"
    }
   },
   "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>sexe</th>\n",
       "      <th>annais</th>\n",
       "      <th>nombre</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preusuel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[1980, 1998, XXXX]</td>\n",
       "      <td>[3, 3, 21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AADAM</th>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
       "      <td>[2009, 2014, 2015, 2016, 2017, 2018, 2020, XXXX]</td>\n",
       "      <td>[4, 3, 3, 4, 4, 3, 3, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AADEL</th>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
       "      <td>[1976, 1978, 1980, 1981, 1982, 1983, 1987, XXXX]</td>\n",
       "      <td>[5, 3, 3, 5, 4, 3, 5, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AADHIRA</th>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>[2020, 2021, XXXX]</td>\n",
       "      <td>[8, 6, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AADIL</th>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1974, 1976, 1977, 1978, 1979, 1980, 1981, 198...</td>\n",
       "      <td>[4, 6, 6, 6, 9, 9, 9, 11, 12, 9, 8, 3, 8, 3, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÖZGE</th>\n",
       "      <td>[2, 2, 2, 2]</td>\n",
       "      <td>[1995, 2010, 2014, XXXX]</td>\n",
       "      <td>[4, 3, 5, 19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÖZGÜR</th>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[2014, 2016, XXXX]</td>\n",
       "      <td>[3, 3, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÖZKAN</th>\n",
       "      <td>[1, 1, 1, 1]</td>\n",
       "      <td>[2014, 2017, 2018, XXXX]</td>\n",
       "      <td>[3, 3, 3, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÖZLEM</th>\n",
       "      <td>[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...</td>\n",
       "      <td>[1976, 1984, 1986, 1988, 1989, 1991, 1992, 199...</td>\n",
       "      <td>[3, 7, 4, 5, 7, 4, 7, 8, 4, 3, 6, 3, 5, 3, 3, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜMMÜ</th>\n",
       "      <td>[2, 2, 2, 2]</td>\n",
       "      <td>[2014, 2015, 2016, XXXX]</td>\n",
       "      <td>[4, 3, 4, 10]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36170 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       sexe  \\\n",
       "preusuel                                                      \n",
       "A                                                 [1, 1, 1]   \n",
       "AADAM                              [1, 1, 1, 1, 1, 1, 1, 1]   \n",
       "AADEL                              [1, 1, 1, 1, 1, 1, 1, 1]   \n",
       "AADHIRA                                           [2, 2, 2]   \n",
       "AADIL     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "...                                                     ...   \n",
       "ÖZGE                                           [2, 2, 2, 2]   \n",
       "ÖZGÜR                                             [1, 1, 1]   \n",
       "ÖZKAN                                          [1, 1, 1, 1]   \n",
       "ÖZLEM     [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...   \n",
       "ÜMMÜ                                           [2, 2, 2, 2]   \n",
       "\n",
       "                                                     annais  \\\n",
       "preusuel                                                      \n",
       "A                                        [1980, 1998, XXXX]   \n",
       "AADAM      [2009, 2014, 2015, 2016, 2017, 2018, 2020, XXXX]   \n",
       "AADEL      [1976, 1978, 1980, 1981, 1982, 1983, 1987, XXXX]   \n",
       "AADHIRA                                  [2020, 2021, XXXX]   \n",
       "AADIL     [1974, 1976, 1977, 1978, 1979, 1980, 1981, 198...   \n",
       "...                                                     ...   \n",
       "ÖZGE                               [1995, 2010, 2014, XXXX]   \n",
       "ÖZGÜR                                    [2014, 2016, XXXX]   \n",
       "ÖZKAN                              [2014, 2017, 2018, XXXX]   \n",
       "ÖZLEM     [1976, 1984, 1986, 1988, 1989, 1991, 1992, 199...   \n",
       "ÜMMÜ                               [2014, 2015, 2016, XXXX]   \n",
       "\n",
       "                                                     nombre  \n",
       "preusuel                                                     \n",
       "A                                                [3, 3, 21]  \n",
       "AADAM                              [4, 3, 3, 4, 4, 3, 3, 8]  \n",
       "AADEL                             [5, 3, 3, 5, 4, 3, 5, 29]  \n",
       "AADHIRA                                           [8, 6, 8]  \n",
       "AADIL     [4, 6, 6, 6, 9, 9, 9, 11, 12, 9, 8, 3, 8, 3, 7...  \n",
       "...                                                     ...  \n",
       "ÖZGE                                          [4, 3, 5, 19]  \n",
       "ÖZGÜR                                            [3, 3, 20]  \n",
       "ÖZKAN                                         [3, 3, 3, 18]  \n",
       "ÖZLEM     [3, 7, 4, 5, 7, 4, 7, 8, 4, 3, 6, 3, 5, 3, 3, ...  \n",
       "ÜMMÜ                                          [4, 3, 4, 10]  \n",
       "\n",
       "[36170 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "prenoms=pd.read_csv('nat2021.csv',delimiter=';').groupby('preusuel').agg(list)\n",
    "prenoms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26da4eae",
   "metadata": {},
   "source": [
    "#### Fonctions d'analyse des tendances annuelles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "301a4a62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-18T08:02:59.847229Z",
     "start_time": "2023-04-18T08:02:59.840920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1900: {1: 1200, 2: 737},\n",
       " 1901: {1: 1276, 2: 675},\n",
       " 1902: {1: 1309, 2: 729},\n",
       " 1903: {1: 1292, 2: 739},\n",
       " 1904: {1: 1433, 2: 740},\n",
       " 1905: {1: 1444, 2: 790},\n",
       " 1906: {1: 1418, 2: 743},\n",
       " 1907: {1: 1453, 2: 760},\n",
       " 1908: {1: 1513, 2: 779},\n",
       " 1909: {1: 1515, 2: 763},\n",
       " 1910: {1: 1594, 2: 728},\n",
       " 1911: {1: 1413, 2: 699},\n",
       " 1912: {1: 1493, 2: 692},\n",
       " 1913: {1: 1422, 2: 632},\n",
       " 1914: {1: 1431, 2: 735},\n",
       " 1915: {1: 873, 2: 489},\n",
       " 1916: {1: 744, 2: 348},\n",
       " 1917: {1: 809, 2: 462},\n",
       " 1918: {1: 906, 2: 464},\n",
       " 1919: {1: 947, 2: 452},\n",
       " 1920: {1: 1517, 2: 680},\n",
       " 1921: {1: 1442, 2: 682},\n",
       " 1922: {1: 1337, 2: 626},\n",
       " 1923: {1: 1277, 2: 561},\n",
       " 1924: {1: 1248, 2: 527},\n",
       " 1925: {1: 1165, 2: 517},\n",
       " 1926: {1: 1103, 2: 488},\n",
       " 1927: {1: 1097, 2: 477},\n",
       " 1928: {1: 1017, 2: 441},\n",
       " 1929: {1: 1021, 2: 404},\n",
       " 1930: {1: 924, 2: 411},\n",
       " 1931: {1: 947, 2: 368},\n",
       " 1932: {1: 822, 2: 349},\n",
       " 1933: {1: 753, 2: 307},\n",
       " 1934: {1: 749, 2: 264},\n",
       " 1935: {1: 682, 2: 273},\n",
       " 1936: {1: 579, 2: 215},\n",
       " 1937: {1: 568, 2: 267},\n",
       " 1938: {1: 522, 2: 204},\n",
       " 1939: {1: 488, 2: 241},\n",
       " 1940: {1: 420, 2: 206},\n",
       " 1941: {1: 363, 2: 147},\n",
       " 1942: {1: 386, 2: 153},\n",
       " 1943: {1: 367, 2: 149},\n",
       " 1944: {1: 344, 2: 143},\n",
       " 1945: {1: 327, 2: 154},\n",
       " 1946: {1: 447, 2: 160},\n",
       " 1947: {1: 420, 2: 147},\n",
       " 1948: {1: 360, 2: 163},\n",
       " 1949: {1: 389, 2: 152},\n",
       " 1950: {1: 308, 2: 139},\n",
       " 1951: {1: 274, 2: 114},\n",
       " 1952: {1: 259, 2: 119},\n",
       " 1953: {1: 267, 2: 123},\n",
       " 1954: {1: 230, 2: 112},\n",
       " 1955: {1: 225, 2: 106},\n",
       " 1956: {1: 202, 2: 87},\n",
       " 1957: {1: 170, 2: 96},\n",
       " 1958: {1: 152, 2: 87},\n",
       " 1959: {1: 163, 2: 80},\n",
       " 1960: {1: 154, 2: 70},\n",
       " 1961: {1: 139, 2: 73},\n",
       " 1962: {1: 125, 2: 102},\n",
       " 1963: {1: 139, 2: 94},\n",
       " 1964: {1: 127, 2: 103},\n",
       " 1965: {1: 125, 2: 108},\n",
       " 1966: {1: 112, 2: 105},\n",
       " 1967: {1: 86, 2: 103},\n",
       " 1968: {1: 75, 2: 91},\n",
       " 1969: {1: 92, 2: 121},\n",
       " 1970: {1: 73, 2: 114},\n",
       " 1971: {1: 85, 2: 145},\n",
       " 1972: {1: 70, 2: 153},\n",
       " 1973: {1: 74, 2: 163},\n",
       " 1974: {1: 64, 2: 223},\n",
       " 1975: {1: 72, 2: 237},\n",
       " 1976: {1: 71, 2: 282},\n",
       " 1977: {1: 108, 2: 395},\n",
       " 1978: {1: 137, 2: 484},\n",
       " 1979: {1: 190, 2: 774},\n",
       " 1980: {1: 222, 2: 979},\n",
       " 1981: {1: 281, 2: 1180},\n",
       " 1982: {1: 284, 2: 1364},\n",
       " 1983: {1: 384, 2: 1384},\n",
       " 1984: {1: 399, 2: 1875},\n",
       " 1985: {1: 481, 2: 2700},\n",
       " 1986: {1: 544, 2: 2770},\n",
       " 1987: {1: 503, 2: 3090},\n",
       " 1988: {1: 575, 2: 3889},\n",
       " 1989: {1: 629, 2: 4845},\n",
       " 1990: {1: 666, 2: 5501},\n",
       " 1991: {1: 665, 2: 5698},\n",
       " 1992: {1: 567, 2: 6219},\n",
       " 1993: {1: 532, 2: 6509},\n",
       " 1994: {1: 502, 2: 7018},\n",
       " 1995: {1: 482, 2: 7335},\n",
       " 1996: {1: 473, 2: 7235},\n",
       " 1997: {1: 508, 2: 7021},\n",
       " 1998: {1: 500, 2: 7462},\n",
       " 1999: {1: 421, 2: 7194},\n",
       " 2000: {1: 400, 2: 7364},\n",
       " 2001: {1: 355, 2: 6582},\n",
       " 2002: {1: 334, 2: 5822},\n",
       " 2003: {1: 341, 2: 5363},\n",
       " 2004: {1: 401, 2: 5292},\n",
       " 2005: {1: 382, 2: 5001},\n",
       " 2006: {1: 472, 2: 5019},\n",
       " 2007: {1: 479, 2: 4610},\n",
       " 2008: {1: 568, 2: 4619},\n",
       " 2009: {1: 573, 2: 4302},\n",
       " 2010: {1: 718, 2: 4159},\n",
       " 2011: {1: 898, 2: 3901},\n",
       " 2012: {1: 891, 2: 3711},\n",
       " 2013: {1: 969, 2: 3453},\n",
       " 2014: {1: 994, 2: 3193},\n",
       " 2015: {1: 1068, 2: 3021},\n",
       " 2016: {1: 1073, 2: 2698},\n",
       " 2017: {1: 1047, 2: 2367},\n",
       " 2018: {1: 1137, 2: 2062},\n",
       " 2019: {1: 1070, 2: 1846},\n",
       " 2020: {1: 1046, 2: 1524},\n",
       " 2021: {1: 1096, 2: 1428}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def maketrend(m):\n",
    "    d={}\n",
    "    for s,a,n in zip(m['sexe'],m['annais'],m['nombre']):\n",
    "        aint=int(a) if a!='XXXX' else a\n",
    "        if aint not in d: d[aint]={} \n",
    "        d[aint][s]=n\n",
    "    return(d)\n",
    "def trendprenom(p):\n",
    "    # tendance d'un prénom i.e., dictionnaire annualisé des naissances\n",
    "    d=maketrend(prenoms.loc[p])\n",
    "    return({y:d[y] for y in d if y!='XXXX'})\n",
    "def trendprenom_fm(prenom,sexe='F'):\n",
    "    # tendance d'un prénom pour un sexe donné\n",
    "    xs={'F':2,'M':1}\n",
    "    tp=trendprenom(prenom)\n",
    "    return({y:tp[y][xs[sexe]] for y in tp if xs[sexe] in tp[y]})\n",
    "trendprenom('CAMILLE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7367a1b",
   "metadata": {},
   "source": [
    "#### Fonctions de tracé des tendances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14102d3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-18T08:03:03.109970Z",
     "start_time": "2023-04-18T08:03:03.104516Z"
    }
   },
   "outputs": [],
   "source": [
    "def plotepi(prenom):\n",
    "    plt.figure(figsize=(1,2.5))\n",
    "    d=trendprenom(prenom)\n",
    "    dx={y:d[y][2]/sum(d[y].values()) for y in d if len(d[y])==2}\n",
    "    plt.axhline(y = 1, color = 'r', linestyle = '-')\n",
    "    plt.scatter(dx.keys(),dx.values(),s=.5,c='black')\n",
    "    plt.yscale('log')\n",
    "    plt.yticks([.1,1,10],['M x10','=','F x10'])\n",
    "    plt.ylim([.1,10])\n",
    "#plotepi('CAMILLE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37df2d0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-18T08:03:03.271659Z",
     "start_time": "2023-04-18T08:03:03.266264Z"
    }
   },
   "outputs": [],
   "source": [
    "def plotprenom(prenom):\n",
    "    plt.figure(figsize=(5,2.5))\n",
    "    for sexe in ['F','M']:\n",
    "        tpfm=trendprenom_fm(prenom.upper(),sexe)\n",
    "        if sum(tpfm.values()): \n",
    "            plt.scatter(tpfm.keys(),tpfm.values(),c={'F':'blue','M':'gray'}[sexe],s=3,label=sexe)\n",
    "    plt.title(prenom)\n",
    "    plt.legend(loc='upper left')\n",
    "#plotprenom('Camille')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "0eb54be7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-30T10:46:50.305125Z",
     "start_time": "2023-04-30T10:46:50.286179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ACACIA', 'ACELIA', 'ACELYA', 'ACHATA', 'ACHIDA', 'ACHLEY', 'ACHOUAK', 'ACHTA', 'ACHWAK', 'ACIA', 'ACILE', 'ACYA', 'ADA', 'ADALGISA', 'ADALINA', 'ADALIND', 'ADALINE', 'ADALYNE', 'ADALYNN', 'ADASSA', 'ADAYA', 'ADDOLORATA', 'ADDYSON', 'ADEA', 'ADELA', 'ADELAIDA', 'ADELAIDE', 'ADELAIS', 'ADELAÏDE', 'ADELE', 'ADELEINE', 'ADELHEID', 'ADELIA', 'ADELICE', 'ADELIE', 'ADELINA', 'ADELINE', 'ADELISA', 'ADELISE', 'ADELLA', 'ADELLE', 'ADELMA', 'ADELPHINE', 'ADELYA', 'ADELYNE', 'ADELYS', 'ADEMA', 'ADENA', 'ADENISE', 'ADENORA', 'ADHIRA', 'ADIA', 'ADIARATOU', 'ADIBA', 'ADIDI', 'ADIDJA', 'ADIJA', 'ADILA', 'ADILIA', 'ADINA', 'ADINE', 'ADISA', 'ADISON', 'ADISSON', 'ADITI', 'ADIXIA', 'ADIZA', 'ADJA', 'ADJARA', 'ADJARATOU', 'ADJIA', 'ADJILA', 'ADJIRA', 'ADLINE', 'ADNA', 'ADOLIE', 'ADOLINE', 'ADOLPHA', 'ADOLPHINE', 'ADONIA', 'ADONIE', 'ADONISE', 'ADORA', 'ADORACION', 'ADORATION', 'ADRA', 'ADRIA', 'ADRIANA', 'ADRIANE', 'ADRIANNA', 'ADRIANNE', 'ADRIELLE', 'ADRIENNE', 'ADRINA', 'ADRYA', 'ADRYANA', 'ADVIKA', 'ADYSON', 'ADYSSON', 'ADÈLA', 'ADÈLE', 'ADÈLE-ROSE', 'ADÉLA', 'ADÉLAIDE', 'ADÉLAÏDE', 'ADÉLAÏS', 'ADÉLE', 'ADÉLIA', 'ADÉLIE', 'ADÉLINA', 'ADÉLINE', 'ADÉLYA', 'ADÉNORA', 'AELA', 'AELAIG', 'AELIA', 'AELINE', 'AELIS', 'AELITA', 'AELLA', 'AELLE', 'AELY', 'AELYA', 'AELYN', 'AELYNE', 'AELYNN', 'AELYS', 'AENA', 'AENAELLE', 'AENOR', 'AENORA', 'AERIS', 'AERITH', 'AERYN', 'AERYNE', 'AERYS', 'AESA', 'AFAF', 'AFAFE', 'AFEF', 'AFENI', 'AFFAF', 'AFFEF', 'AFFOU', 'AFFOUE', 'AFFOUSSIATA', 'AFI', 'AFIA', 'AFIDA', 'AFIFA', 'AFIYA', 'AFNAN', 'AFNANE', 'AFNEN', 'AFNENE', 'AFRA', 'AFRAH', 'AFRICA', 'AFSA', 'AFSANA', 'AFTON', 'AFTONE', 'AGATA', 'AGATE', 'AGATHA', 'AGATHE', 'AGLAE', 'AGLAEE', 'AGLAIA', 'AGLAÉ', 'AGLAÉE', 'AGLAË', 'AGNELA', 'AGNELLA', 'AGNES', 'AGNESA', 'AGNESE', 'AGNIELA', 'AGNIES', 'AGNÈS', 'AGNÈS-MARIE', 'AGOSTINA', 'AGUEDA', 'AGUSTINA', 'AHANA', 'AHAVA', 'AHED', 'AHLAM', 'AHLAME', 'AHLEM', 'AHLEME', 'AHLIMA', 'AHMEL', 'AHNA', 'AHSEN', 'AHYNA', 'AI', 'AIA', 'AIANA', 'AICHA', 'AICHAT', 'AICHATA', 'AICHATOU', 'AICHE', 'AICHETOU', 'AICHOUCHE', 'AIDA', 'AIDEE', 'AIDI', 'AIFA', 'AIGLINE', 'AILA', 'AILEEN', 'AILEY', 'AILINE', 'AILIS', 'AILSA', 'AILYN', 'AILYNE', 'AILYS', 'AIMA', 'AIMEE', 'AIMELINE', 'AIMELYNE', 'AIMI', 'AIMIE', 'AIMMEE', 'AIMY', 'AIMÉE', 'AINARA', 'AINES', 'AINHOA', 'AINI', 'AINOA', 'AINOHA', 'AINTZANE', 'AIRELLE', 'AISATA', 'AISATOU', 'AISE', 'AISETOU', 'AISHA', 'AISHWARYA', 'AISLING', 'AISLINN', 'AISOSA', 'AISSATA', 'AISSATOU', 'AISSE', 'AISSETA', 'AISSETOU', 'AISSIA', 'AISSYA', 'AISSÉ', 'AITA', 'AITANA', 'AIVY', 'AIYA', 'AIYANA', 'AIYANNA', 'AIZA', 'AIZAH', 'AJANA', 'AJAR', 'AJLA', 'AJNA', 'AKANE', 'AKASHA', 'AKASYA', 'AKELA', 'AKEMI', 'AKHEANE', 'AKHESA', 'AKIKO', 'AKILA', 'AKIMA', 'AKINA', 'AKISSI', 'AKSA', 'AKSHA', 'AKSHANA', 'AKSHARA', 'AKSHAYA', 'ALABAMA', 'ALAIA', 'ALAINA', 'ALAINE', 'ALAIS', 'ALAITZ', 'ALANA', 'ALANAH', 'ALANDRA', 'ALANIS', 'ALANNA', 'ALANNAH', 'ALANYS', 'ALARA', 'ALAYAH', 'ALAYNA', 'ALAYS', 'ALAÂ', 'ALAÏA', 'ALAÏNA', 'ALAÏS', 'ALBA', 'ALBANA', 'ALBANE', 'ALBANIE', 'ALBANNE', 'ALBENA', 'ALBERTA', 'ALBERTE', 'ALBERTHE', 'ALBERTINA', 'ALBERTINE', 'ALBINA', 'ALBINE', 'ALCIDA', 'ALCIDIE', 'ALCINA', 'ALCINDA', 'ALCINE', 'ALCYONE', 'ALDA', 'ALDEGONDE', 'ALDINA', 'ALDINE', 'ALDIOUMA', 'ALDJA', 'ALDJIA', 'ALEA', 'ALEANDRA', 'ALECIA', 'ALEENA', 'ALEESHA', 'ALEEYAH', 'ALEEZA', 'ALEGRIA', 'ALEINA', 'ALEJANDRA', 'ALEKSANDRA', 'ALEKSIA', 'ALENA', 'ALENKA', 'ALESIA', 'ALESSA', 'ALESSANDRA', 'ALESSIA', 'ALESSYA', 'ALETH', 'ALETTE', 'ALEV', 'ALEX-ANNE', 'ALEXA', 'ALEXANA', 'ALEXANDRA', 'ALEXANDRIA', 'ALEXANDRIE', 'ALEXANDRINA', 'ALEXANDRINE', 'ALEXANDRY', 'ALEXANE', 'ALEXANNE', 'ALEXE', 'ALEXIA', 'ALEXIANA', 'ALEXIANE', 'ALEXIANNE', 'ALEXIAS', 'ALEXIDE', 'ALEXIE', 'ALEXINA', 'ALEXINE', 'ALEXYA', 'ALEXYANE', 'ALEYA', 'ALEYAH', 'ALEYNA', 'ALEYNA-NUR', 'ALEZIA', 'ALEÏNA', 'ALFIYA', 'ALFREDA', 'ALFREDE', 'ALFREDINE', 'ALFRÉDA', 'ALGIA', 'ALHANA', 'ALHENA', 'ALHYA', 'ALHÉNA', 'ALIA', 'ALIAA', 'ALIAH', 'ALIANA', 'ALICA', 'ALICE', 'ALICE-ANNE', 'ALICE-MARIE', 'ALICEA', 'ALICIA', 'ALICIANE', 'ALICJA', 'ALICYA', 'ALIDA', 'ALIE', 'ALIENOR', 'ALIETTE', 'ALIHA', 'ALIHYA', 'ALIKA', 'ALIMA', 'ALIMATA', 'ALIMATOU', 'ALIME', 'ALINA', 'ALINE', 'ALIONA', 'ALIS', 'ALISA', 'ALISE', 'ALISEA', 'ALISEE', 'ALISHA', 'ALISHBA', 'ALISIA', 'ALISON', 'ALISONE', 'ALISONNE', 'ALISSA', 'ALISSANDRE', 'ALISSAR', 'ALISSE', 'ALISSIA', 'ALISSON', 'ALISSONE', 'ALISSYA', 'ALISYA', 'ALISÉA', 'ALISÉE', 'ALITA', 'ALIX-ANNE', 'ALIXANE', 'ALIXE', 'ALIXIA', 'ALIYA', 'ALIYAAH', 'ALIYAH', 'ALIYANA', 'ALIYE', 'ALIYYAH', 'ALIZA', 'ALIZE', 'ALIZEA', 'ALIZEE', 'ALIZIA', 'ALIZON', 'ALIZÉ', 'ALIZÉA', 'ALIZÉE', 'ALIÉNOR', 'ALIÉNORE', 'ALJIA', 'ALLAA', 'ALLANA', 'ALLEGRA', 'ALLEGRESSE', 'ALLEGRIA', 'ALLIA', 'ALLIAH', 'ALLIANCE', 'ALLICIA', 'ALLIE', 'ALLIETTE', 'ALLINE', 'ALLISON', 'ALLISONE', 'ALLISSA', 'ALLISSIA', 'ALLISSON', 'ALLIX', 'ALLIYA', 'ALLIYAH', 'ALLY', 'ALLYA', 'ALLYAH', 'ALLYANA', 'ALLYCIA', 'ALLYHA', 'ALLYRIANE', 'ALLYSON', 'ALLYSSA', 'ALLYSSIA', 'ALLYSSON', 'ALMA', 'ALMAH', 'ALMAIDE', 'ALMASS', 'ALMASSE', 'ALMAZ', 'ALMEDINA', 'ALMERINDA', 'ALMILA', 'ALMINA', 'ALMIRA', 'ALMUDENA', 'ALNA', 'ALODIE', 'ALOE', 'ALOHA', 'ALOISIA', 'ALONA', 'ALOY', 'ALOYSIA', 'ALOÉ', 'ALOÏSE', 'ALPHENA', 'ALPHONSA', 'ALPHONSIA', 'ALPHONSINE', 'ALSACE', 'ALTEA', 'ALTHEA', 'ALTHÉA', 'ALTINA', 'ALUNA', 'ALVA', 'ALVANA', 'ALVINA', 'ALVIRA', 'ALVYNA', 'ALWEN', 'ALWENA', 'ALWENN', 'ALWENNA', 'ALWINE', 'ALWÉNA', 'ALYA', 'ALYAA', 'ALYAAH', 'ALYAH', 'ALYANA', 'ALYANAH', 'ALYANNA', 'ALYCE', 'ALYCIA', 'ALYETTE', 'ALYHA', 'ALYIA', 'ALYIAH', 'ALYMA', 'ALYNA', 'ALYNE', 'ALYONA', 'ALYS', 'ALYSA', 'ALYSE', 'ALYSEA', 'ALYSEE', 'ALYSHA', 'ALYSIA', 'ALYSON', 'ALYSONE', 'ALYSONN', 'ALYSS', 'ALYSSA', 'ALYSSAH', 'ALYSSANDRE', 'ALYSSE', 'ALYSSIA', 'ALYSSON', 'ALYSSONE', 'ALYSÉA', 'ALYSÉE', 'ALYXE', 'ALYXIA', 'ALYZE', 'ALYZEA', 'ALYZEE', 'ALYZIA', 'ALYZÉ', 'ALYZÉA', 'ALYZÉE', 'ALZINA', 'ALZIRA', 'ALÉA', 'ALÉCIA', 'ALÉNA', 'ALÉSIA', 'ALÉSSIA', 'ALÉXIA', 'ALÉZIA', 'ALÏA', 'AMA', 'AMABILE', 'AMADEA', 'AMAE', 'AMAELLE', 'AMAELYS', 'AMAIA', 'AMAL', 'AMALE', 'AMALIA', 'AMALIE', 'AMALIYA', 'AMALLE', 'AMALYA', 'AMALYAH', 'AMALYS', 'AMANA', 'AMANALLAH', 'AMANCE', 'AMANDA', 'AMANDE', 'AMANDINE', 'AMANDYNE', 'AMANI', 'AMANIE', 'AMANTE', 'AMANTHE', 'AMANTINE', 'AMANY', 'AMANZA', 'AMAPOLA', 'AMARANDE', 'AMARANTE', 'AMARANTHE', 'AMARIA', 'AMARILYS', 'AMARINE', 'AMARIS', 'AMARYA', 'AMARYLLIS', 'AMATALLAH', 'AMATOULLAH', 'AMATULLAH', 'AMAURIE', 'AMAURINE', 'AMAURYNE', 'AMAYA', 'AMAYAH', 'AMAYEL', 'AMAYELLE', 'AMAZONE', 'AMAËLLE', 'AMAÏA', 'AMBAR', 'AMBELINE', 'AMBER', 'AMBERLY', 'AMBRA', 'AMBRE', 'AMBRE-MARIE', 'AMBREEN', 'AMBRINE', 'AMBROISINE', 'AMBROSIA', 'AMBRYNE', 'AMEDEA', 'AMEDINE', 'AMEENA', 'AMEERA', 'AMEL', 'AMELA', 'AMELE', 'AMELI', 'AMELIA', 'AMELIANE', 'AMELIE', 'AMELINA', 'AMELINE', 'AMELIYA', 'AMELL', 'AMELLE', 'AMELLIA', 'AMELLYA', 'AMELY', 'AMELYA', 'AMELYNE', 'AMELYS', 'AMENA', 'AMENATA', 'AMENI', 'AMENIE', 'AMENIS', 'AMERICA', 'AMETHYS', 'AMETHYSTE', 'AMI', 'AMICA', 'AMICIE', 'AMIE', 'AMILA', 'AMILIA', 'AMILYA', 'AMINA', 'AMINAH', 'AMINAT', 'AMINATA', 'AMINATOU', 'AMINTA', 'AMINTHE', 'AMINÉ', 'AMIRA', 'AMIRAH', 'AMLA', 'AMMA', 'AMMARA', 'AMMARIA', 'AMMEL', 'AMNA', 'AMNAH', 'AMPARO', 'AMRA', 'AMRINE', 'AMRITA', 'AMSATOU', 'AMY', 'AMY-ROSE', 'AMYCIE', 'AMYNA', 'AMYNATA', 'AMYRA', 'AMYTIS', 'AMÀLIA', 'AMÈLE', 'AMÉLIA', 'AMÉLIANE', 'AMÉLIE', 'AMÉLIE-ANNE', 'AMÉLINA', 'AMÉLINE', 'AMÉLY', 'AMÉLYA', 'AMÉLYNE', 'AMÉLYS', 'AMÉTHYS', 'AMÉTHYSTE', 'AN-ICHA', 'AN-ICHAT', 'ANA', 'ANA-BELA', 'ANA-BELLA', 'ANA-CHRISTINA', 'ANA-CLARA', 'ANA-CRISTINA', 'ANA-ISABEL', 'ANA-LOU', 'ANA-LUCIA', 'ANA-LUISA', 'ANA-MARIA', 'ANA-MARIE', 'ANA-PAOLA', 'ANA-PAULA', 'ANA-ROSA', 'ANA-ROSE', 'ANA-SOFIA', 'ANABEL', 'ANABELA', 'ANABELE', 'ANABELLA', 'ANABELLE', 'ANABIA', 'ANAE', 'ANAEE', 'ANAELE', 'ANAELI', 'ANAELL', 'ANAELLA', 'ANAELLE', 'ANAH', 'ANAHE', 'ANAHI', 'ANAHID', 'ANAHIDE', 'ANAHIT', 'ANAHITA', 'ANAHY', 'ANAHÉ', 'ANAHË', 'ANAIA', 'ANAICK', 'ANAID', 'ANAIDE', 'ANAIG', 'ANAIK', 'ANAIKA', 'ANAIS', 'ANAISA', 'ANAISE', 'ANAISSA', 'ANAISSE', 'ANAITA', 'ANAIZ', 'ANALIA', 'ANALIE', 'ANALINE', 'ANALOU', 'ANALY', 'ANALYA', 'ANALYNE', 'ANAM', 'ANAMARIA', 'ANAMIKA', 'ANANE', 'ANANYA', 'ANAROSE', 'ANAROZE', 'ANASTACIA', 'ANASTASIA', 'ANASTASIE', 'ANASTASIJA', 'ANASTASIYA', 'ANASTASSIA', 'ANASTASYA', 'ANASTAZIA', 'ANASTAZJA', 'ANASTHASIA', 'ANASTHASIE', 'ANATALIE', 'ANATH', 'ANATHALIE', 'ANATHILDE', 'ANATOLIE', 'ANATOLINE', 'ANAVAI', 'ANAYA', 'ANAYAH', 'ANAYS', 'ANAÉ', 'ANAÉE', 'ANAË', 'ANAËLE', 'ANAËLLE', 'ANAÏA', 'ANAÏDE', 'ANAÏG', 'ANAÏK', 'ANAÏKA', 'ANAÏS', 'ANAÏSE', 'ANAÏSSA', 'ANAÏSSE', 'ANCA', 'ANCELINE', 'ANCUTA', 'ANDA', 'ANDGELINA', 'ANDGIE', 'ANDHREE', 'ANDIE', 'ANDJALI', 'ANDJALY', 'ANDJELA', 'ANDJELINA', 'ANDORA', 'ANDRA', 'ANDRADA', 'ANDREANA', 'ANDREANE', 'ANDREANNE', 'ANDREEA', 'ANDREIA', 'ANDREINA', 'ANDRELINE', 'ANDRENA', 'ANDRESE', 'ANDRESIA', 'ANDRESINE', 'ANDRESSA', 'ANDRETTE', 'ANDREZE', 'ANDRIANA', 'ANDRIENNE', 'ANDRIJANA', 'ANDRINA', 'ANDRINE', 'ANDRISE', 'ANDRÉA', 'ANDRÉANE', 'ANDRÉANNE', 'ANDRÉE', 'ANDRÉE-ANNE', 'ANDRÉE-MARIE', 'ANDRÉIA', 'ANEA', 'ANEFEL', 'ANELA', 'ANELIA', 'ANELIE', 'ANELINE', 'ANELISE', 'ANELLA', 'ANELLE', 'ANELYA', 'ANELYNE', 'ANELYSE', 'ANEMONE', 'ANESA', 'ANESIE', 'ANESSA', 'ANETA', 'ANETTE', 'ANEYA', 'ANEÏA', 'ANFAAL', 'ANFAL', 'ANFALE', 'ANFEL', 'ANFIA', 'ANFINA', 'ANGE-LINE', 'ANGE-LYNE', 'ANGELA', 'ANGELE', 'ANGELES', 'ANGELIA', 'ANGELICA', 'ANGELIE', 'ANGELIKA', 'ANGELINA', 'ANGELINE', 'ANGELINES', 'ANGELIQUA', 'ANGELIQUE', 'ANGELITA', 'ANGELLA', 'ANGELLE', 'ANGELLINA', 'ANGELYNA', 'ANGELYNE', 'ANGELYS', 'ANGGUN', 'ANGHARAD', 'ANGHJULA', 'ANGHJULA-MARIA', 'ANGHJULINA', 'ANGIE', 'ANGIOLINA', 'ANGUELINA', 'ANGÈLA', 'ANGÈLE', 'ANGÈLINA', 'ANGÈLIQUE', 'ANGÉLA', 'ANGÉLIA', 'ANGÉLICA', 'ANGÉLIE', 'ANGÉLIKA', 'ANGÉLINA', 'ANGÉLINE', 'ANGÉLIQUE', 'ANGÉLYNA', 'ANGÉLYNE', 'ANH-THU', 'ANI', 'ANIA', 'ANIC', 'ANICA', 'ANICEE', 'ANICETTE', 'ANICHA', 'ANICIA', 'ANICK', 'ANICÉE', 'ANIDA', 'ANIE', 'ANIECE', 'ANIELA', 'ANIELE', 'ANIELLA', 'ANIELLE', 'ANIFA', 'ANIKA', 'ANILA', 'ANIMA', 'ANINA', 'ANIQUE', 'ANISA', 'ANISE', 'ANISHA', 'ANISHKA', 'ANISIA', 'ANISSA', 'ANISSAH', 'ANISSATI', 'ANISSIA', 'ANISSYA', 'ANISTON', 'ANITA', 'ANITE', 'ANITHA', 'ANIYA', 'ANJA', 'ANJALI', 'ANJALIE', 'ANJALY', 'ANJANA', 'ANJELA', 'ANJELINA', 'ANJU', 'ANJULI', 'ANKA', 'ANKE', 'ANLIA', 'ANLIATI', 'ANN', 'ANN-AEL', 'ANN-CHARLOTTE', 'ANN-GAELLE', 'ANN-LAURE', 'ANN-LISE', 'ANN-SOPHIE', 'ANNA', 'ANNA-BELLA', 'ANNA-BELLE', 'ANNA-CARLA', 'ANNA-CHRISTINA', 'ANNA-CHRISTINE', 'ANNA-CLARA', 'ANNA-DEA', 'ANNA-GAELLE', 'ANNA-ISABELLE', 'ANNA-JULIA', 'ANNA-LESIA', 'ANNA-LIA', 'ANNA-LINE', 'ANNA-LISA', 'ANNA-LIVIA', 'ANNA-LOU', 'ANNA-LOUISE', 'ANNA-LUCIA', 'ANNA-LÉA', 'ANNA-LÉNA', 'ANNA-MAE', 'ANNA-MARIA', 'ANNA-MARIE', 'ANNA-MAY', 'ANNA-PAOLA', 'ANNA-PAULA', 'ANNA-RITA', 'ANNA-ROSA', 'ANNA-ROSE', 'ANNA-SOFIA', 'ANNA-SOPHIA', 'ANNA-VICTORIA', 'ANNABEL', 'ANNABELA', 'ANNABELE', 'ANNABELLA', 'ANNABELLE', 'ANNAE', 'ANNAEL', 'ANNAELE', 'ANNAELLE', 'ANNAH', 'ANNAIC', 'ANNAICK', 'ANNAIG', 'ANNAIK', 'ANNAIS', 'ANNALIA', 'ANNALIE', 'ANNALINE', 'ANNALISA', 'ANNALYA', 'ANNALYNE', 'ANNAMARIA', 'ANNASTASIA', 'ANNAYA', 'ANNAYAH', 'ANNAÉ', 'ANNAË', 'ANNAËL', 'ANNAËLE', 'ANNAËLLE', 'ANNAÏCK', 'ANNAÏG', 'ANNAÏK', 'ANNAÏS', 'ANNE', 'ANNE-AEL', 'ANNE-ALEXANDRA', 'ANNE-ALICE', 'ANNE-AMÉLIE', 'ANNE-ASTRID', 'ANNE-AUDREY', 'ANNE-AURORE', 'ANNE-AURÉLIE', 'ANNE-AYMONE', 'ANNE-BENEDICTE', 'ANNE-BERANGERE', 'ANNE-BERENGERE', 'ANNE-BLANDINE', 'ANNE-BRIGITTE', 'ANNE-BÉATRICE', 'ANNE-BÉNÉDICTE', 'ANNE-CAMILLE', 'ANNE-CAPUCINE', 'ANNE-CARINE', 'ANNE-CAROLE', 'ANNE-CAROLINE', 'ANNE-CATHERINE', 'ANNE-CHANTAL', 'ANNE-CHARLINE', 'ANNE-CHARLOTTE', 'ANNE-CHLOÉ', 'ANNE-CHRISTEL', 'ANNE-CHRISTELLE', 'ANNE-CHRISTIE', 'ANNE-CHRISTINE', 'ANNE-CLAIRE', 'ANNE-CLARISSE', 'ANNE-CLAUDE', 'ANNE-CLOTHILDE', 'ANNE-CLOTILDE', 'ANNE-CLÉMENCE', 'ANNE-COLETTE', 'ANNE-CONSTANCE', 'ANNE-CORALIE', 'ANNE-CÉCILE', 'ANNE-CÉLINE', 'ANNE-DAUPHINE', 'ANNE-DOMINIQUE', 'ANNE-DOROTHEE', 'ANNE-ELEONORE', 'ANNE-ELISABETH', 'ANNE-ELISE', 'ANNE-ELIZABETH', 'ANNE-ELODIE', 'ANNE-EMILIE', 'ANNE-EMMANUELLE', 'ANNE-ESTELLE', 'ANNE-ESTHER', 'ANNE-EVA', 'ANNE-FLEUR', 'ANNE-FLORE', 'ANNE-FLORENCE', 'ANNE-FRANCE', 'ANNE-FRANÇOISE', 'ANNE-FRÉDÉRIQUE', 'ANNE-GABRIELLE', 'ANNE-GAEL', 'ANNE-GAELE', 'ANNE-GAELLE', 'ANNE-GAËL', 'ANNE-GAËLLE', 'ANNE-GENEVIÈVE', 'ANNE-HÉLOÏSE', 'ANNE-HÉLÈNE', 'ANNE-ISABELLE', 'ANNE-JOSEE', 'ANNE-JOSEPHE', 'ANNE-JOSÉ', 'ANNE-JOËLLE', 'ANNE-JULIE', 'ANNE-JULIETTE', 'ANNE-JUSTINE', 'ANNE-KARINE', 'ANNE-KATELL', 'ANNE-KELLY', 'ANNE-LAETITIA', 'ANNE-LAURE', 'ANNE-LAURENCE', 'ANNE-LAURIE', 'ANNE-LINE', 'ANNE-LISE', 'ANNE-LORE', 'ANNE-LORRAINE', 'ANNE-LOU', 'ANNE-LOUISE', 'ANNE-LUCE', 'ANNE-LUCIE', 'ANNE-LUCILE', 'ANNE-LYNE', 'ANNE-LYS', 'ANNE-LYSE', 'ANNE-LÉA', 'ANNE-MADELEINE', 'ANNE-MAELLE', 'ANNE-MARGUERITE', 'ANNE-MARIA', 'ANNE-MARIE', 'ANNE-MARINE', 'ANNE-MARTINE', 'ANNE-MARY', 'ANNE-MATHILDE', 'ANNE-MAUD', 'ANNE-MICHÈLE', 'ANNE-MONIQUE', 'ANNE-NOËLLE', 'ANNE-OLIVIA', 'ANNE-PASCALE', 'ANNE-PATRICIA', 'ANNE-PAULE', 'ANNE-PAULINE', 'ANNE-PERRINE', 'ANNE-PHILIPPE', 'ANNE-PIERRE', 'ANNE-RACHEL', 'ANNE-RAPHAELLE', 'ANNE-RENÉE', 'ANNE-ROSE', 'ANNE-SABINE', 'ANNE-SANDRINE', 'ANNE-SARAH', 'ANNE-SEVERINE', 'ANNE-SIXTINE', 'ANNE-SOAZIG', 'ANNE-SOIZIC', 'ANNE-SOLANGE', 'ANNE-SOLENN', 'ANNE-SOLENNE', 'ANNE-SOLÈNE', 'ANNE-SOPHIE', 'ANNE-STÉPHANIE', 'ANNE-SYLVIE', 'ANNE-SÉGOLÈNE', 'ANNE-THAIS', 'ANNE-THÉRÈSE', 'ANNE-TIPHAINE', 'ANNE-VALÉRIE', 'ANNE-VICTOIRE', 'ANNE-VICTORIA', 'ANNE-VIOLAINE', 'ANNE-VIRGINIE', 'ANNE-VÉRONIQUE', 'ANNE-YVONNE', 'ANNE-ZOÉ', 'ANNEA', 'ANNECIE', 'ANNECY', 'ANNELAURE', 'ANNELIE', 'ANNELIES', 'ANNELIESE', 'ANNELINE', 'ANNELISE', 'ANNELLE', 'ANNELORE', 'ANNELYNE', 'ANNELYSE', 'ANNEMARIE', 'ANNETTE', 'ANNI', 'ANNIA', 'ANNIC', 'ANNICETTE', 'ANNICIA', 'ANNICK', 'ANNICKE', 'ANNIE', 'ANNIE-CHRISTINE', 'ANNIE-CLAIRE', 'ANNIE-CLAUDE', 'ANNIE-FLORE', 'ANNIE-FRANCE', 'ANNIE-FRANÇOISE', 'ANNIE-LAURE', 'ANNIE-MARIE', 'ANNIE-PAULE', 'ANNIE-PIERRE', 'ANNIE-ROSE', 'ANNIE-THÉRÈSE', 'ANNIELLE', 'ANNIK', 'ANNIKA', 'ANNILY', 'ANNINA', 'ANNINE', 'ANNIQUE', 'ANNISE', 'ANNISSA', 'ANNITA', 'ANNLYSE', 'ANNONCIA', 'ANNONCIADE', 'ANNONCIATA', 'ANNONCIATE', 'ANNONCIATION', 'ANNOUCHKA', 'ANNOUCK', 'ANNOUK', 'ANNUNCIATA', 'ANNUNZIATA', 'ANNWEN', 'ANNWENN', 'ANNY', 'ANNY-CLAUDE', 'ANNY-FRANCE', 'ANNYA', 'ANNYCK', 'ANNYSSA', 'ANNYVONNE', 'ANOA', 'ANOUCH', 'ANOUCHA', 'ANOUCHE', 'ANOUCHKA', 'ANOUCK', 'ANOUK', 'ANOUSHA', 'ANOUSHKA', 'ANRAFA', 'ANSAM', 'ANTA', 'ANTEA', 'ANTHEA', 'ANTHELMETTE', 'ANTHELMINE', 'ANTHIA', 'ANTHINEA', 'ANTHONIA', 'ANTHYNEA', 'ANTHÉA', 'ANTIGONE', 'ANTINEA', 'ANTINÉA', 'ANTOINETTA', 'ANTOINETTE', 'ANTOINISE', 'ANTONELA', 'ANTONELLA', 'ANTONIA', 'ANTONIETA', 'ANTONIETTA', 'ANTONINA', 'ANTONINE', 'ANTONITA', 'ANTONNELLA', 'ANTONYA', 'ANTSA', 'ANUSHA', 'ANUSHKA', 'ANUSKA', 'ANWEN', 'ANY', 'ANYA', 'ANYAH', 'ANYCHA', 'ANYCIA', 'ANYICHA', 'ANYSE', 'ANYSIA', 'ANYSSA', 'ANYSSIA', 'ANYTA', 'ANYVONNE', 'ANZIZA', 'ANÉA', 'ANÉLIA', 'ANÉLIE', 'ANÉLYA', 'ANÉMONE', 'ANÉYA', 'ANÏCHA', 'AOI', 'AOIFE', 'AOITEF', 'AOITIF', 'AONA', 'AOUA', 'AOUATEF', 'AOUATIF', 'AOUDA', 'AOUICHA', 'AOUREGAN', 'AOURELL', 'APAULINE', 'APHELIE', 'APO', 'APOLINE', 'APOLLINA', 'APOLLINE', 'APOLLONIA', 'APOLLONIE', 'APOLLYNE', 'APOLONIA', 'APOLONIE', 'APOLYNE', 'APONI', 'APPOLINA', 'APPOLINE', 'APPOLLINE', 'APPOLLONIE', 'APPOLONIA', 'APPOLONIE', 'APPOLYNE', 'APRIL', 'APRILE', 'APRYL', 'AQSA', 'AQUILA', 'ARABA', 'ARABELLA', 'ARABELLE', 'ARACELI', 'ARAFA', 'ARAMATA', 'ARAMATOU', 'ARAME', 'ARANI', 'ARANTXA', 'ARBIA', \"ARC'HANTAEL\", 'ARCANGELA', 'ARCHANA', 'ARDIANA', 'AREEJ', 'ARETHA', 'ARFA', 'ARGANE', 'ARGENTINA', 'ARGENTINE', 'ARGINE', 'ARGITXU', 'ARIA', 'ARIADNA', 'ARIAH', 'ARIAM', 'ARIANA', 'ARIANE', 'ARIANNA', 'ARIANNE', 'ARICIA', 'ARICIE', 'ARIDJ', 'ARIELA', 'ARIELE', 'ARIELLA', 'ARIELLE', 'ARIETTE', 'ARIFA', 'ARIFE', 'ARIGE', 'ARIJ', 'ARIJE', 'ARIN', 'ARINA', 'ARINAS', 'ARINE', 'ARISHA', 'ARISTA', 'ARIYA', 'ARIZONA', 'ARIÈLE', 'ARJIN', 'ARLENE', 'ARLETE', 'ARLETTE', 'ARLETTY', 'ARLINDA', 'ARLINE', 'ARLÈNE', 'ARMANCE', 'ARMANDA', 'ARMANDE', 'ARMANDINA', 'ARMANDINE', 'ARMANTINE', 'ARMELA', 'ARMELE', 'ARMELIA', 'ARMELINE', 'ARMELLE', 'ARMENTINE', 'ARMIDA', 'ARMIDE', 'ARMINA', 'ARMINDA', 'ARMINE', 'ARMONI', 'ARMONIA', 'ARMONIE', 'ARMONY', 'ARMÈLE', 'ARNAUDE', 'ARNELA', 'AROUA', 'AROUNY', 'ARSELINE', 'ARSENA', 'ARSENIA', 'ARSENIE', 'ARSINOE', 'ARTA', 'ARTEMIS', 'ARTEMISE', 'ARTEMISIA', 'ARTHEMISE', 'ARTHI', 'ARTHIKA', 'ARTHURIA', 'ARTHURINE', 'ARTHY', 'ARTÉMIS', 'ARTÉMISE', 'ARWA', 'ARWEN', 'ARWENN', 'ARYAH', 'ARYAM', 'ARYANA', 'ARYANE', 'ARYANNA', 'ARYELLE', 'ARYEM', 'ARZHELA', 'ARZHELENN', 'ARZU', 'ASA', 'ASALIAH', 'ASCELINE', 'ASCENCION', 'ASCENSION', 'ASCHLEY', 'ASEEL', 'ASEL', 'ASELYA', 'ASENA', 'ASHA', 'ASHANA', 'ASHANTI', 'ASHANTY', 'ASHELEY', 'ASHLY', 'ASHLYN', 'ASHMITHA', 'ASHNA', 'ASHVIKA', 'ASHVINA', 'ASHVINI', 'ASHWINI', 'ASIA', 'ASIMA', 'ASINAT', 'ASIYA', 'ASIYAH', 'ASIYE', 'ASJA', 'ASLI', 'ASLIHAN', 'ASLINE', 'ASMA', 'ASMAA', 'ASMAE', 'ASMAH', 'ASMAHAN', 'ASMAHANE', 'ASMAOU', 'ASMAÂ', 'ASMIN', 'ASMINA', 'ASMINE', 'ASMITHA', 'ASMÂ', 'ASMÂA', 'ASNA', 'ASNATH', 'ASNIA', 'ASRA', 'ASRAR', 'ASSA', 'ASSAH', 'ASSALA', 'ASSANA', 'ASSANATOU', 'ASSATA', 'ASSATOU', 'ASSETA', 'ASSETOU', 'ASSIA', 'ASSIAH', 'ASSIAT', 'ASSIATA', 'ASSIATI', 'ASSIATOU', 'ASSIBA', 'ASSILA', 'ASSILE', 'ASSIMA', 'ASSIMINA', 'ASSINA', 'ASSINAT', 'ASSINATE', 'ASSINET', 'ASSITA', 'ASSITAN', 'ASSIYA', 'ASSIYAH', 'ASSMA', 'ASSMAA', 'ASSMAE', 'ASSNA', 'ASSOMPTION', 'ASSUNTA', 'ASSUNTINA', 'ASSYA', 'ASSYAH', 'ASSYLE', 'ASSÉTOU', 'ASSÏA', 'ASTA', 'ASTAN', 'ASTER', 'ASTERIE', 'ASTOU', 'ASTREE', 'ASTRID', 'ASTRIDE', 'ASTRIG', 'ASTRIK', 'ASTRYD', 'ASTRÉE', 'ASUDE', 'ASUKA', 'ASUMAN', 'ASUNA', 'ASUNCION', 'ASVIKA', 'ASVINI', 'ASYA', 'ATALIA', 'ATEA', 'ATENA', 'ATHALIA', 'ATHALIE', 'ATHANAIS', 'ATHANAISE', 'ATHANASIA', 'ATHANASIE', 'ATHEA', 'ATHENA', 'ATHENAIS', 'ATHENAISE', 'ATHENAÏS', 'ATHIA', 'ATHINA', 'ATHYNA', 'ATHÉA', 'ATHÉNA', 'ATHÉNAIS', 'ATHÉNAÏS', 'ATIKA', 'ATINA', 'ATIYA', 'ATLANTIS', 'ATOUMA', 'ATTIKA', 'ATTILIA', 'AUBANE', 'AUBANNE', 'AUBE', 'AUBELINE', 'AUBERI', 'AUBERIE', 'AUBERTE', 'AUBERTINE', 'AUBERY', 'AUBREE', 'AUCEANE', 'AUCÉANE', 'AUDE', 'AUDE-CLAIRE', 'AUDE-EMMANUELLE', 'AUDE-LINE', 'AUDE-LISE', 'AUDE-MARIE', 'AUDE-MARINE', 'AUDELIA', 'AUDELIE', 'AUDELINE', 'AUDESSA', 'AUDETTE', 'AUDRAY', 'AUDREE', 'AUDREY', 'AUDREY-ANNE', 'AUDREY-LAURE', 'AUDREY-ROSE', 'AUDRIE', 'AUDRINA', 'AUDRINE', 'AUDRÉE', 'AUDÉLIA', 'AUGUSTA', 'AUGUSTINA', 'AUGUSTINE', 'AULDE', 'AULONA', 'AUPHELIE', 'AUPHÉLIE', 'AURA', 'AURANE', 'AURANNE', 'AURE', 'AUREA', 'AUREANE', 'AUREANNE', 'AUREGAN', 'AUREGANE', 'AUREGANN', 'AURELA', 'AURELANE', 'AURELIA', 'AURELIANE', 'AURELIE', 'AURELIENNE', 'AURELINE', 'AURELLE', 'AURELY', 'AURELYA', 'AURELYNE', 'AURETTE', 'AURIA', 'AURIANA', 'AURIANE', 'AURIANNA', 'AURIANNE', 'AURIDA', 'AURIELLE', 'AURINE', 'AURISTELLE', 'AURLANE', 'AURORA', 'AURORE', 'AURYANA', 'AURYANE', 'AURÉA', 'AURÉANE', 'AURÉGANE', 'AURÉLIA', 'AURÉLIANE', 'AURÉLIE', 'AURÉLIE-ANNE', 'AURÉLINE', 'AURÉLY', 'AURÉLYA', 'AUTOMNE', 'AUTUMN', 'AUXANA', 'AUXANE', 'AUXANNE', 'AUXILIA', 'AVA', 'AVAH', 'AVANA', 'AVELINA', 'AVELINE', 'AVELYNE', 'AVENTINE', 'AVESTA', 'AVIDA', 'AVIGAEL', 'AVIGAELLE', 'AVIGAIL', 'AVIGAÏL', 'AVINE', 'AVITAL', 'AVITE', 'AVIVA', 'AVRILE', 'AVRILLETTE', 'AVSIN', 'AVZEM', 'AWA', 'AWATEF', 'AWATIF', 'AWENA', 'AWENN', 'AWENNA', 'AWÉNA', 'AXELE', 'AXELIA', 'AXELINE', 'AXELLANE', 'AXELLE', 'AXIANE', 'AXÈLE', 'AYA', 'AYAA', 'AYAAT', 'AYADA', 'AYAH', 'AYALA', 'AYAME', 'AYAMÉ', 'AYANA', 'AYANAH', 'AYANNA', 'AYANNAH', 'AYAT', 'AYATE', 'AYATTE', 'AYBUKE', 'AYCA', 'AYCAN', 'AYCHA', 'AYDA', 'AYELE', 'AYEM', 'AYESHA', 'AYET', 'AYETTE', 'AYFER', 'AYGLINE', 'AYGUL', 'AYIA', 'AYIANA', 'AYINA', 'AYISHA', 'AYLA', 'AYLAH', 'AYLANA', 'AYLEE', 'AYLEEN', 'AYLIA', 'AYLIE', 'AYLIN', 'AYLINA', 'AYLINE', 'AYLINN', 'AYLIS', 'AYLIZ', 'AYLLA', 'AYMA', 'AYMARA', 'AYMEE', 'AYMELINE', 'AYMIE', 'AYMIRA', 'AYMONE', 'AYNA', 'AYNAH', 'AYNI', 'AYNNA', 'AYNUR', 'AYOKO', 'AYRA', 'AYSE', 'AYSE-GUL', 'AYSE-NUR', 'AYSEGUL', 'AYSEGÜL', 'AYSEL', 'AYSEM', 'AYSEN', 'AYSENUR', 'AYSHA', 'AYSIMA', 'AYSSATA', 'AYSU', 'AYSUN', 'AYTEN', 'AYUMI', 'AYVI', 'AYVIE', 'AYZA', 'AYÉLÉ', 'AZA', 'AZADEH', 'AZALAIS', 'AZALAÏS', 'AZALEE', 'AZALIA', 'AZALYS', 'AZALÉA', 'AZALÉE', 'AZANIA', 'AZAÉ', 'AZELIA', 'AZELIE', 'AZELINE', 'AZELLE', 'AZELYA', 'AZELYS', 'AZEMA', 'AZEMIA', 'AZENOR', 'AZIA', 'AZILIS', 'AZILIZ', 'AZILYS', 'AZIME', 'AZIYADE', 'AZIZA', 'AZIZAH', 'AZNA', 'AZOU', 'AZRA', 'AZRA-NUR', 'AZRANUR', 'AZUCENA', 'AZURE', 'AZYLIS', 'AZZA', 'AZZIZA', 'AZZURA', 'AZZURRA', 'AZÈLE', 'AZÉLIA', 'AZÉLIE', 'AZÉLINE', 'AZÉLYA', 'AZÉLYS', 'AZÉNOR', 'AÇELYA', 'AÉLA', 'AÉLIA', 'AÉLIE', 'AÉLINE', 'AÉLIS', 'AÉLYA', 'AÉLYNE', 'AÉLYS', 'AÉNA', 'AÉNOR', 'AÉRIS', 'AÉRYS', 'AËLA', 'AËLIA', 'AËLIE', 'AËLINE', 'AËLIS', 'AËLLE', 'AËLY', 'AËLYA', 'AËLYAH', 'AËLYNE', 'AËLYNN', 'AËLYS', 'AÏANA', 'AÏCHA', 'AÏCHAT', 'AÏCHATA', 'AÏCHATOU', 'AÏCHÉ', 'AÏDA', 'AÏLA', 'AÏLEEN', 'AÏLINE', 'AÏLY', 'AÏLYNE', 'AÏLYS', 'AÏNA', 'AÏNAH', 'AÏNARA', 'AÏNHOA', 'AÏNI', 'AÏNNA', 'AÏNOA', 'AÏNOHA', 'AÏSHA', 'AÏSSATA', 'AÏSSATOU', 'AÏSSE', 'AÏSSETA', 'AÏSSETOU', 'AÏSSÉ', 'AÏTA', 'AÏTANA', 'AÏVY', 'AÏYANA', 'AÏZA']\n"
     ]
    }
   ],
   "source": [
    "print([x for x in prenoms.index if epicenity.get(x,0)>.9 and (x[0]=='A' and (x[1] not in ['A','B'] if len(x)>1 else True))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "4ed97117",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-30T10:45:11.859786Z",
     "start_time": "2023-04-30T10:45:10.214421Z"
    }
   },
   "outputs": [],
   "source": [
    "def epicenity_p(prenom):\n",
    "    d=trendprenom(prenom)\n",
    "    dx={y:d[y].get(2,0)/(d[y].get(1,0)+d[y].get(2,0)) for y in d}\n",
    "    return(dx)\n",
    "epicenity={prenom:np.average(list(epicenity_p(prenom).values())) for prenom in list(prenoms.index) if trendprenom(prenom)!={}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "f8f8c128",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-30T10:45:54.255516Z",
     "start_time": "2023-04-30T10:45:54.247516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001420187930685421"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epicenity['MARC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef2b40fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "7b127ffa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-30T09:07:00.436277Z",
     "start_time": "2023-04-30T09:06:58.941628Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "––– prénom ? Vesna\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotprenomx(prenomx):\n",
    "    prenom=prenomx.upper()\n",
    "    if prenom not in prenoms.index:\n",
    "        print('*** prénom inexistant')\n",
    "        return(False)\n",
    "    fig,(ax1,ax2)=plt.subplots(1,2,figsize=(7,3.5),width_ratios=[4, 1],sharex=True)\n",
    "    plt.suptitle(prenomx)\n",
    "    for sexe in ['F','M']:\n",
    "        tpfm=trendprenom_fm(prenom.upper(),sexe)\n",
    "        if sum(tpfm.values()): \n",
    "            ax1.scatter([x for x in sorted(tpfm)],[tpfm[x] for x in sorted(tpfm)],c={'F':'blue','M':'gray'}[sexe],s=.1,label=sexe)\n",
    "            #ax1.plot([x for x in sorted(tpfm)],[tpfm[x] for x in sorted(tpfm)],c={'F':'blue','M':'gray'}[sexe],linewidth=.25)\n",
    "            ax1.plot([x for x in sorted(tpfm)],[ma(tpfm,x,2) for x in sorted(tpfm)],\n",
    "                     c={'F':'blue','M':'gray'}[sexe],linewidth=.5)\n",
    "    ax1.legend(loc='upper left')\n",
    "    d=trendprenom(prenom)\n",
    "    dx={y:d[y][2]/d[y][1] for y in d if len(d[y])==2}\n",
    "    ax2.axhline(y = 1, color = 'r', linestyle = '-')\n",
    "    ax2.plot([x for x in sorted(dx)],[dx[x] for x in sorted(dx)],linewidth=.25,c='black')\n",
    "    ax2.scatter([x for x in sorted(dx)],[dx[x] for x in sorted(dx)],s=.3,c='black')\n",
    "    ax2.set_yscale('log')\n",
    "    ax2.set_yticks([.1,1,10],['M x10','=','F x10'])\n",
    "    ax2.yaxis.tick_right()\n",
    "    ax2.set_ylim([.1,10])\n",
    "    ax2.tick_params(axis='x', labelsize=4)\n",
    "    ax2.tick_params(axis='y', labelsize=6)\n",
    "    plt.show()\n",
    "plotprenomx(input('––– prénom ? '));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7badd391",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f16d6684",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "921ef3a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebc01010",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e59e2fc1",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162906f1",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3595a03f",
   "metadata": {},
   "source": [
    "## GARBAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9ccdba56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-27T23:18:21.547529Z",
     "start_time": "2023-01-27T23:18:21.522420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1900: (0.6141666666666666, 1937),\n",
       " 1901: (0.5289968652037618, 1951),\n",
       " 1902: (0.5569136745607334, 2038),\n",
       " 1903: (0.5719814241486069, 2031),\n",
       " 1904: (0.5163991625959525, 2173),\n",
       " 1905: (0.5470914127423823, 2234),\n",
       " 1906: (0.5239774330042313, 2161),\n",
       " 1907: (0.5230557467309016, 2213),\n",
       " 1908: (0.5148711169861203, 2292),\n",
       " 1909: (0.5036303630363036, 2278),\n",
       " 1910: (0.45671267252195735, 2322),\n",
       " 1911: (0.49469214437367304, 2112),\n",
       " 1912: (0.463496316141996, 2185),\n",
       " 1913: (0.4444444444444444, 2054),\n",
       " 1914: (0.5136268343815513, 2166),\n",
       " 1915: (0.5601374570446735, 1362),\n",
       " 1916: (0.46774193548387094, 1092),\n",
       " 1917: (0.5710754017305315, 1271),\n",
       " 1918: (0.5121412803532008, 1370),\n",
       " 1919: (0.47729672650475186, 1399),\n",
       " 1920: (0.44825313117996046, 2197),\n",
       " 1921: (0.47295423023578365, 2124),\n",
       " 1922: (0.4682124158563949, 1963),\n",
       " 1923: (0.43931088488645265, 1838),\n",
       " 1924: (0.422275641025641, 1775),\n",
       " 1925: (0.44377682403433477, 1682),\n",
       " 1926: (0.44242973708068906, 1591),\n",
       " 1927: (0.43482224247948953, 1574),\n",
       " 1928: (0.4336283185840708, 1458),\n",
       " 1929: (0.395690499510284, 1425),\n",
       " 1930: (0.4448051948051948, 1335),\n",
       " 1931: (0.3885955649419219, 1315),\n",
       " 1932: (0.4245742092457421, 1171),\n",
       " 1933: (0.4077025232403719, 1060),\n",
       " 1934: (0.35246995994659547, 1013),\n",
       " 1935: (0.4002932551319648, 955),\n",
       " 1936: (0.37132987910189985, 794),\n",
       " 1937: (0.47007042253521125, 835),\n",
       " 1938: (0.39080459770114945, 726),\n",
       " 1939: (0.49385245901639346, 729),\n",
       " 1940: (0.49047619047619045, 626),\n",
       " 1941: (0.4049586776859504, 510),\n",
       " 1942: (0.3963730569948187, 539),\n",
       " 1943: (0.40599455040871935, 516),\n",
       " 1944: (0.41569767441860467, 487),\n",
       " 1945: (0.4709480122324159, 481),\n",
       " 1946: (0.3579418344519016, 607),\n",
       " 1947: (0.35, 567),\n",
       " 1948: (0.4527777777777778, 523),\n",
       " 1949: (0.390745501285347, 541),\n",
       " 1950: (0.4512987012987013, 447),\n",
       " 1951: (0.41605839416058393, 388),\n",
       " 1952: (0.4594594594594595, 378),\n",
       " 1953: (0.4606741573033708, 390),\n",
       " 1954: (0.48695652173913045, 342),\n",
       " 1955: (0.4711111111111111, 331),\n",
       " 1956: (0.4306930693069307, 289),\n",
       " 1957: (0.5647058823529412, 266),\n",
       " 1958: (0.5723684210526315, 239),\n",
       " 1959: (0.49079754601226994, 243),\n",
       " 1960: (0.45454545454545453, 224),\n",
       " 1961: (0.5251798561151079, 212),\n",
       " 1962: (0.816, 227),\n",
       " 1963: (0.6762589928057554, 233),\n",
       " 1964: (0.8110236220472441, 230),\n",
       " 1965: (0.864, 233),\n",
       " 1966: (0.9375, 217),\n",
       " 1967: (1.197674418604651, 189),\n",
       " 1968: (1.2133333333333334, 166),\n",
       " 1969: (1.315217391304348, 213),\n",
       " 1970: (1.5616438356164384, 187),\n",
       " 1971: (1.7058823529411764, 230),\n",
       " 1972: (2.1857142857142855, 223),\n",
       " 1973: (2.2027027027027026, 237),\n",
       " 1974: (3.484375, 287),\n",
       " 1975: (3.2916666666666665, 309),\n",
       " 1976: (3.971830985915493, 353),\n",
       " 1977: (3.6574074074074074, 503),\n",
       " 1978: (3.5328467153284673, 621),\n",
       " 1979: (4.073684210526316, 964),\n",
       " 1980: (4.40990990990991, 1201),\n",
       " 1981: (4.199288256227758, 1461),\n",
       " 1982: (4.802816901408451, 1648),\n",
       " 1983: (3.6041666666666665, 1768),\n",
       " 1984: (4.6992481203007515, 2274),\n",
       " 1985: (5.613305613305613, 3181),\n",
       " 1986: (5.091911764705882, 3314),\n",
       " 1987: (6.143141153081511, 3593),\n",
       " 1988: (6.763478260869565, 4464),\n",
       " 1989: (7.702702702702703, 5474),\n",
       " 1990: (8.25975975975976, 6167),\n",
       " 1991: (8.56842105263158, 6363),\n",
       " 1992: (10.968253968253968, 6786),\n",
       " 1993: (12.234962406015038, 7041),\n",
       " 1994: (13.9800796812749, 7520),\n",
       " 1995: (15.217842323651452, 7817),\n",
       " 1996: (15.295983086680762, 7708),\n",
       " 1997: (13.820866141732283, 7529),\n",
       " 1998: (14.924, 7962),\n",
       " 1999: (17.08788598574822, 7615),\n",
       " 2000: (18.41, 7764),\n",
       " 2001: (18.540845070422534, 6937),\n",
       " 2002: (17.431137724550897, 6156),\n",
       " 2003: (15.727272727272727, 5704),\n",
       " 2004: (13.197007481296758, 5693),\n",
       " 2005: (13.091623036649215, 5383),\n",
       " 2006: (10.633474576271187, 5491),\n",
       " 2007: (9.624217118997912, 5089),\n",
       " 2008: (8.132042253521126, 5187),\n",
       " 2009: (7.507853403141361, 4875),\n",
       " 2010: (5.7924791086350975, 4877),\n",
       " 2011: (4.344097995545657, 4799),\n",
       " 2012: (4.164983164983165, 4602),\n",
       " 2013: (3.5634674922600618, 4422),\n",
       " 2014: (3.2122736418511066, 4187),\n",
       " 2015: (2.8286516853932584, 4089),\n",
       " 2016: (2.5144454799627214, 3771),\n",
       " 2017: (2.2607449856733526, 3414),\n",
       " 2018: (1.8135444151275286, 3199),\n",
       " 2019: (1.7252336448598131, 2916),\n",
       " 2020: (1.4569789674952198, 2570),\n",
       " 2021: (1.302919708029197, 2524)}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dnorm(d): \n",
    "    # returns a yearly dictionary associating a ratio F/M and a total number of births \n",
    "    # for a given first name raw birth number dictionary d\n",
    "    return ({x:(d[x].get(2,0)/d[x].get(1,1),d[x].get(2,0)+d[x].get(1,0)) for x in sorted([y for y in d if y!='XXXX'])})\n",
    "dnorm(trendprenom('CAMILLE'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "e5ae9c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:59:22.393997Z",
     "start_time": "2023-01-28T21:59:22.214225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prenom='JENNIFER'\n",
    "dp=dnorm(trendprenom(prenom))\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.scatter([int(y) for y in dp],[dp[y][0] for y in dp],s=[np.sqrt(dp[y][1]) for y in dp])\n",
    "#plt.plot([int(y) for y in dp],[dp[y][0] for y in dp])\n",
    "plt.title(prenom)\n",
    "plt.xticks(np.arange(1900, 2030, step=10),map(str,np.arange(1900, 2030, step=10)))\n",
    "plt.yscale('log')\n",
    "plt.xlim(1900,2022)\n",
    "plt.ylim([.005,200])\n",
    "plt.yticks([.01,.1,1,10,100],['M x100','M x10','=','F x10','F x100'])\n",
    "plt.axline((1900,1),(2022,1),linewidth=.5,color='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd707a5f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f34ccc23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-27T23:18:27.402576Z",
     "start_time": "2023-01-27T23:18:27.392810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.986016085146504"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def epicenity(prenom): \n",
    "    # VERY POSITIVE IF VERY FEMALE, VERY NEGATIVE IF VERY MALE, OVER THE CENTURY\n",
    "    d=dnorm(trendprenom(prenom))\n",
    "    v=[(np.log(x[0]) if x[0] else 0) for x in d.values()]\n",
    "    w=[x[0] for x in d.values()]\n",
    "    return(np.average(v,weights=w) if sum(w) else 0)\n",
    "epicenity('CAMILLE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "67a94116",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:29:36.037742Z",
     "start_time": "2023-01-28T21:29:36.020321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7237726967047747"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pepicenity(prenom): \n",
    "    d=trendprenom(prenom)\n",
    "    v=[d[y].get(2,0)/(d[y].get(2,0)+d[y].get(1,0)) for y in d]\n",
    "    w=[sum(d[y].values()) for y in d]\n",
    "    return(np.average(v,weights=w) if sum(w) else .5)\n",
    "pepicenity('CAMILLE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "68322dcd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:29:55.323936Z",
     "start_time": "2023-01-28T21:29:37.046732Z"
    }
   },
   "outputs": [],
   "source": [
    "def ftotalprenom(p):\n",
    "    return(sum([sum(trendprenom(p)[y].values()) for y in trendprenom(p) if y !='XXXX']))\n",
    "totalprenom={p:ftotalprenom(p) for p in prenoms.index}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "3e8f75e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:31:53.755419Z",
     "start_time": "2023-01-28T21:31:52.476927Z"
    }
   },
   "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>prenom</th>\n",
       "      <th>#</th>\n",
       "      <th>pep</th>\n",
       "      <th>epep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AADIL</td>\n",
       "      <td>153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AALIYA</td>\n",
       "      <td>232</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AALIYAH</td>\n",
       "      <td>2935</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AALYA</td>\n",
       "      <td>141</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AALYAH</td>\n",
       "      <td>243</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11569</th>\n",
       "      <td>ÉWEN</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11570</th>\n",
       "      <td>ÉZIO</td>\n",
       "      <td>379</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11571</th>\n",
       "      <td>ÉZÉCHIEL</td>\n",
       "      <td>292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11572</th>\n",
       "      <td>ÉZÉKIEL</td>\n",
       "      <td>176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>ÖMER</td>\n",
       "      <td>514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11574 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         prenom     #  pep  epep\n",
       "0         AADIL   153  0.0   0.0\n",
       "1        AALIYA   232  1.0   0.0\n",
       "2       AALIYAH  2935  1.0   0.0\n",
       "3         AALYA   141  1.0   0.0\n",
       "4        AALYAH   243  1.0   0.0\n",
       "...         ...   ...  ...   ...\n",
       "11569      ÉWEN   109  0.0   0.0\n",
       "11570      ÉZIO   379  0.0   0.0\n",
       "11571  ÉZÉCHIEL   292  0.0   0.0\n",
       "11572   ÉZÉKIEL   176  0.0   0.0\n",
       "11573      ÖMER   514  0.0   0.0\n",
       "\n",
       "[11574 rows x 4 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=[[p,totalprenom[p],pepicenity(p),1-2*np.abs(.5-pepicenity(p))] for p in prenoms.index if totalprenom[p]>100]\n",
    "df=pd.DataFrame(tmp,columns=['prenom','#','pep','epep'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "1a4d92a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:35:23.989102Z",
     "start_time": "2023-01-28T21:35:23.966588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ENOA', 'EDENN', 'HARLEY', 'NAHID', 'SHILO', 'CHARLIE', 'DESTINY', 'YAEL', 'KENDALL', 'ROSARIO', 'LOÉ', 'ILIA', 'YANNE', 'JAËL', 'ELIN', 'XUAN', 'MEDINE', '_PRENOMS_RARES', 'KANDE', 'CARMEL', 'SINA', 'FELICE', 'FAUSTE', 'MILANE', 'LIWEN', 'ADDA', 'GABY', 'MADY', 'LE', 'ELIAH', 'KELIANE', 'ROBYN', 'NAZAR', 'NAÉ', 'NEHEMIE', 'SHAMS', 'DOROTHE', 'JANICK', 'ANGY', 'IVANE', 'PHILOGONE', 'PARIS', 'ZAYANE', 'MAÏLEY', 'MYLANE', 'LOUISON', 'LOETARE', 'TAHIS', 'SORA', 'KELYANE', 'CHAMS', 'YAËL', 'ALA', 'YAKINE', 'HEAVEN', 'ANGE-MARIE', 'CHAN', 'NIMA', 'NICOMEDE', 'JANNICK', 'HAMIDE', 'NATHY', 'GWEN', 'ORPHÉE', 'AELIG', 'IHSEN', 'AZEL', 'KRISTEN', 'CHAHED', 'JOA', 'DARA', 'NATY', 'LORRY', 'AXELL', 'MAELIG', 'ORPHEE', 'KENSY', 'JANIK', 'MAË', 'MALI', 'SASHA', 'FATI', 'JANY', 'LENAICK', 'CAMERONE', 'ANAEL', 'GABRIELE', 'SYDNEY', 'KILLIANE', 'IZAYA', 'ANH', 'HONORAT', 'PRAISE', 'NOUHA', 'TENZIN', 'TAHÏS', 'SADIO', 'YAHEL', 'ACIL', 'THANH', 'ISA', 'ZIYA', 'LILO', 'DOMINIQUE', 'SWANNY', 'JANNY', 'DALY', 'MERRY', 'ELVAN', 'ALVINE', 'SEYHAN', 'SIDJY', 'WALLIS', 'KENCY', 'AMANE', 'ENJY', 'NIKI', 'TAHYS', 'BROOKLYN', 'VENANCE', 'JEANICK', 'GAYA', 'CALLISTE', 'MAELANN', 'BAO', 'ELIKIA', 'SAINTE', 'LORIN', 'SOA', 'ELY', 'ANONYME', 'SHILOH', 'ASSYL', 'MENDY', 'DARCY', 'LEE', 'AMARA', 'SIDNEY', 'GLOIRE', 'KÉZIAH', 'JORDANE', 'MAËLANN', 'KABA', 'MAE', 'MORGANN', 'SAYANE', 'JEANNICK', 'NATTY', 'MIRACLE', 'CLAUDY', 'RAYHANE', 'SYAM', 'KIMI', 'KEVINE', 'FLORY', 'AMOUR', 'SOLANE', 'SABAS', 'CORY', 'EUTROPE', 'HAÏLEY', 'THEOTINE', 'LEV', 'IHSANE', 'JAEL', 'ANDOCHE', 'GEORGIE', 'SULPICE', 'CYRIEL', 'TENNESSEE', 'KALYS', 'INDY', 'MODESTE', 'EDEN', 'JANIS', 'AUBIERGE', 'MEWENN', 'GWENN', 'PIE', 'RAVEN', 'MISHA', 'NOAME', 'LY', 'SHEMS', 'DENIZ', 'GILLIAN', 'LISANDRE', 'IKHLAS', 'GWENHAEL', 'WISSAME', 'DANICK', 'BARAA', 'OLWEN', 'NIKITA', 'JO', 'NAE', 'ELISHA', 'IKBAL', 'MAMERT', 'HILAL', 'EUSTASE', 'CEYLAN', 'PERPETUE', 'ALYX', 'EOLE', 'LÉNAÏCK', 'ANAËL', 'ANGELY', 'HOANG', 'JAZZ', 'FARA', 'YVANE', 'JUNO', 'JILLIAN', 'TAYSSIR', 'CYAN', 'LOE', 'SHALOM', 'ILYA', 'SIMPLICE', 'MAYWEN', 'CANDIDE', 'CHRISTY', 'SAFI', 'LOIS', 'SIAM', 'EULOGE', 'SATYA', 'NGOC', 'ASSIL', 'CLARENCE', 'OUISSAM', 'MANOHA', 'ADONAÏ', 'ELISÉE', 'JODY', 'YOHANNE', 'SULTAN', 'WARIS', 'YUMA', 'SHAYANE', 'LOÏS', 'EZEL', 'CHANCE', 'DIAMOND', 'SEPTIME', 'DANY', 'HADIL', 'IRENEE', 'OPTAT', 'NIL', 'AZUR', 'LAYAN', 'KERRY', 'NICAISE', 'SWANY', 'BABYLAS', 'KELIG', 'NESTA', 'MARIEL', 'KILIANE', 'TAYLOR', 'SOANE', 'AIKO', 'MALEK', 'ENOGAT', 'NÉHÉMIE', 'ILKAY', 'TAÏS', 'BRANDY', 'GERALDE', 'DIALLA', 'MAÏLAN', 'SIAN', 'ADDISON', 'NONCE', 'SHANE', 'JOUDE', 'ADAMA', 'KINSLEY', 'YONAH', 'BORA', 'SWANN', 'ARCADE', 'SAADA', 'KENZY', 'LENAIK', 'ISLEM', 'LYSSANDRE', 'CAMILLE', 'BAYAN', 'JOANY', 'NOLANE', 'AGNAN', 'GEORGE', 'ANDREA', 'LOUENN', 'KEZIAH', 'LENAÏC', 'ZANA']\n"
     ]
    }
   ],
   "source": [
    "print(list(df.sort_values(by='epep',ascending=False)['prenom'])[:300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "6c5de70d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:31:55.286444Z",
     "start_time": "2023-01-28T21:31:55.193537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x30cc0f550>]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorted(df['epep']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "ffad0177",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T21:17:33.915368Z",
     "start_time": "2023-01-28T21:17:33.786459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.histplot([np.log(x) for x in sorted(df['#'])])\n",
    "plt.xlabel('log #births')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "cb82b3b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T18:54:52.996239Z",
     "start_time": "2023-01-28T18:54:50.641973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AADHIRA': 1.956149224914787,\n",
       " 'AALEYAH': 1.7681592225479748,\n",
       " 'AALIA': 1.7759769597495743,\n",
       " 'AALIYA': 2.5492993515494744,\n",
       " 'AALIYAH': 5.056162512962521,\n",
       " 'AALYA': 2.052848348569004,\n",
       " 'AALYAH': 2.5152723269787027,\n",
       " 'AANOR': 1.187129849422504,\n",
       " 'AARICIA': 1.0986122886681098,\n",
       " 'AARON': -3.343591462628495,\n",
       " 'AATHIRA': 1.5070439327578633,\n",
       " 'AAYA': 1.496525555700168,\n",
       " 'ABASSIA': 1.3862943611198906,\n",
       " 'ABBIE': 3.1237093356274865,\n",
       " 'ABBIGAELLE': 1.0986122886681098,\n",
       " 'ABBIGAËLLE': 1.2136851176488221,\n",
       " 'ABBY': 4.972628036717753,\n",
       " 'ABBY-GAELLE': 2.0815854222124632,\n",
       " 'ABBY-GAËLLE': 2.3259468405617505,\n",
       " 'ABBYGAEL': 2.09123326517655,\n",
       " 'ABBYGAELLE': 3.0292580810687353,\n",
       " 'ABBYGAIL': 1.9715464325628602,\n",
       " 'ABBYGAËL': 2.4689608003781935,\n",
       " 'ABBYGAËLLE': 3.5239698930471572,\n",
       " 'ABBYGAÏL': 1.6649609139451957,\n",
       " 'ABDON': -0.9581156249851451,\n",
       " 'ABDONIE': 1.2136851176488221,\n",
       " 'ABDONISE': 1.3862943611198906,\n",
       " 'ABEER': 1.0986122886681098,\n",
       " 'ABEL': -3.784330355351251,\n",
       " 'ABELIA': 1.7488946013266928,\n",
       " 'ABELINA': 1.0986122886681098,\n",
       " 'ABELINE': 1.4081544895344098,\n",
       " 'ABELLA': 1.3465439001708168,\n",
       " 'ABELLE': 1.5068029286073554,\n",
       " 'ABI': 1.5865609583520315,\n",
       " 'ABIBA': 1.6824206338371321,\n",
       " 'ABIBATA': 1.3862943611198906,\n",
       " 'ABIBATOU': 1.6013165660122937,\n",
       " 'ABIDA': 1.1446414202603947,\n",
       " 'ABIE': 1.3193206840519371,\n",
       " 'ABIGAEL': 4.0688668323145585,\n",
       " 'ABIGAELLE': 3.992658878587357,\n",
       " 'ABIGAHEL': 2.0509686647073786,\n",
       " 'ABIGAIL': 3.958888363175596,\n",
       " 'ABIGAËL': 4.296231405009138,\n",
       " 'ABIGAËLLE': 4.769621549116686,\n",
       " 'ABIGAÏL': 3.6823696726787007,\n",
       " 'ABIHA': 1.3456027159105814,\n",
       " 'ABINA': 1.0986122886681098,\n",
       " 'ABINAYA': 1.9973898933610332,\n",
       " 'ABIR': 2.4574885879597983,\n",
       " 'ABIRA': 1.22984430754498,\n",
       " 'ABIRAMI': 1.187129849422504,\n",
       " 'ABIRAMY': 1.0986122886681098,\n",
       " 'ABIRE': 1.0986122886681098,\n",
       " 'ABISHA': 1.8024609427047333,\n",
       " 'ABLA': 2.1829148831217178,\n",
       " 'ABOU': -1.7346010553881062,\n",
       " 'ABRAR': 2.0433118221129574,\n",
       " 'ABRIL': 1.6556625398396154,\n",
       " 'ABSA': 1.3170494967791306,\n",
       " 'ABSATOU': 1.2096613373128904,\n",
       " 'ABY': 3.5549235556704213,\n",
       " 'ABY-GAELLE': 1.2630020443548418,\n",
       " 'ABY-GAËLLE': 1.0986122886681098,\n",
       " 'ABYGAEL': 2.9092626013179035,\n",
       " 'ABYGAELLE': 3.651886639541658,\n",
       " 'ABYGAIL': 2.177152971641647,\n",
       " 'ABYGAËL': 3.38650925194354,\n",
       " 'ABYGAËLLE': 4.014973044628913,\n",
       " 'ABYGAÏL': 2.173593653253109,\n",
       " 'ABÈLE': 1.5607104090414066,\n",
       " 'ABÉLIA': 1.4162746659059535,\n",
       " 'ACACIA': 2.139049448932944,\n",
       " 'ACELIA': 1.3308057540162872,\n",
       " 'ACELYA': 3.0923773286590963,\n",
       " 'ACHATA': 1.0986122886681098,\n",
       " 'ACHIDA': 1.0986122886681098,\n",
       " 'ACHILLE': -2.1981722975474987,\n",
       " 'ACHLEY': 1.3078356140875869,\n",
       " 'ACHOUAK': 1.150918120022979,\n",
       " 'ACHRAF': -1.4269328892354403,\n",
       " 'ACHTA': 1.2630020443548418,\n",
       " 'ACHWAK': 1.2630020443548418,\n",
       " 'ACIA': 1.3522141776446448,\n",
       " 'ACIL': 0.2553264826527862,\n",
       " 'ACILE': 2.074891628117284,\n",
       " 'ACYA': 1.0986122886681098,\n",
       " 'ACYL': -0.01692247485010479,\n",
       " 'ADA': 2.7051503382471993,\n",
       " 'ADALGISA': 1.9459101490553132,\n",
       " 'ADALINA': 1.5607104090414066,\n",
       " 'ADALIND': 1.4934953005616172,\n",
       " 'ADALINE': 4.078018405033786,\n",
       " 'ADALYNE': 2.342894169631398,\n",
       " 'ADALYNN': 1.5995548959746273,\n",
       " 'ADAM': -5.046767752244885,\n",
       " 'ADAMA': -0.13687463566222874,\n",
       " 'ADAME': -2.055189630670409,\n",
       " 'ADASSA': 1.1779728603789459,\n",
       " 'ADAYA': 1.2136851176488221,\n",
       " 'ADDA': 1.260355963272693,\n",
       " 'ADDISON': 2.062401590466435,\n",
       " 'ADDOLORATA': 1.5070439327578633,\n",
       " 'ADDYSON': 1.554849598107373,\n",
       " 'ADEA': 1.5677591420391848,\n",
       " 'ADEL': -3.917010546939185,\n",
       " 'ADELA': 2.313770620738909,\n",
       " 'ADELAIDA': 1.2810500114416778,\n",
       " 'ADELAIDE': 4.7781221327652785,\n",
       " 'ADELAIS': 1.4020863323208723,\n",
       " 'ADELAÏDE': 2.94752512733121,\n",
       " 'ADELE': 5.271075714516246,\n",
       " 'ADELEINE': 1.3862943611198906,\n",
       " 'ADELHEID': 1.3810667690573855,\n",
       " 'ADELIA': 2.3566909059063934,\n",
       " 'ADELICE': 1.150918120022979,\n",
       " 'ADELIE': 3.93184026204897,\n",
       " 'ADELINA': 2.9724411069408587,\n",
       " 'ADELINE': 6.983152695061562,\n",
       " 'ADELISA': 1.2810500114416778,\n",
       " 'ADELISE': 1.291816232106844,\n",
       " 'ADELLA': 1.0986122886681098,\n",
       " 'ADELLE': 1.6554894984678685,\n",
       " 'ADELMA': 1.0986122886681098,\n",
       " 'ADELPHINE': 2.4705595420142203,\n",
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       " 'ANA-LUISA': 1.0986122886681098,\n",
       " 'ANA-MARIA': 3.049546391270942,\n",
       " 'ANA-MARIE': 1.0986122886681098,\n",
       " 'ANA-PAOLA': 1.5102630007388962,\n",
       " 'ANA-PAULA': 2.3491318399630043,\n",
       " 'ANA-ROSA': 1.6094379124341003,\n",
       " 'ANA-ROSE': 1.7655448892113974,\n",
       " 'ANA-SOFIA': 1.2339920874689478,\n",
       " 'ANABEL': 2.607038549276007,\n",
       " 'ANABELA': 3.470065716644542,\n",
       " 'ANABELE': 1.3078356140875869,\n",
       " 'ANABELLA': 2.3992384654149004,\n",
       " 'ANABELLE': 3.8540730107710144,\n",
       " 'ANABIA': 2.1913358696034155,\n",
       " 'ANAE': 5.3529222286612095,\n",
       " 'ANAEE': 2.1633008645699365,\n",
       " 'ANAEL': -0.1878320412681989,\n",
       " 'ANAELE': 2.7514403338612246,\n",
       " 'ANAELI': 1.3308057540162872,\n",
       " 'ANAELL': 1.3862943611198906,\n",
       " 'ANAELLA': 1.0986122886681098,\n",
       " 'ANAELLE': 6.415946072072992,\n",
       " 'ANAH': 1.5064407986055603,\n",
       " 'ANAHE': 1.6976147270826571,\n",
       " 'ANAHI': 1.9807049417599307,\n",
       " 'ANAHID': 1.4233168789763515,\n",
       " 'ANAHIDE': 1.3565122385688866,\n",
       " 'ANAHIT': 1.9713669654523471,\n",
       " 'ANAHITA': 1.668147222176357,\n",
       " 'ANAHY': 1.6094379124341003,\n",
       " 'ANAHÉ': 2.743291878189318,\n",
       " 'ANAHË': 1.2136851176488221,\n",
       " 'ANAIA': 2.1023970060516657,\n",
       " 'ANAICK': 1.517344103337933,\n",
       " 'ANAID': 1.3456027159105814,\n",
       " 'ANAIDE': 1.290201694022392,\n",
       " 'ANAIG': 1.519982734813759,\n",
       " 'ANAIK': 1.5862546717656367,\n",
       " 'ANAIKA': 1.4043423282067269,\n",
       " 'ANAIS': 6.116939076748509,\n",
       " 'ANAISA': 1.9286383786782402,\n",
       " 'ANAISE': 2.8880918966831457,\n",
       " 'ANAISSA': 1.0986122886681098,\n",
       " 'ANAISSE': 1.5607104090414066,\n",
       " 'ANAITA': 1.0986122886681098,\n",
       " 'ANAIZ': 1.0986122886681098,\n",
       " 'ANALIA': 2.366590921606563,\n",
       " 'ANALIE': 1.674863355877064,\n",
       " 'ANALINE': 1.2136851176488221,\n",
       " 'ANALOU': 1.4073503227211994,\n",
       " 'ANALY': 1.0986122886681098,\n",
       " 'ANALYA': 2.332623265169164,\n",
       " 'ANALYNE': 1.0986122886681098,\n",
       " 'ANAM': 1.2630020443548418,\n",
       " 'ANAMARIA': 1.8250187850323263,\n",
       " 'ANAMIKA': 1.4115615150414613,\n",
       " 'ANANDA': 1.6392924344195576,\n",
       " 'ANANE': 1.0986122886681098,\n",
       " 'ANANYA': 1.6777487207358077,\n",
       " 'ANAROSE': 1.2630020443548418,\n",
       " 'ANAROZE': 1.4693051025875112,\n",
       " 'ANASTACIA': 2.285990934932186,\n",
       " 'ANASTASE': -1.1473397920608033,\n",
       " 'ANASTASIA': 5.1985803760347435,\n",
       " 'ANASTASIE': 3.754307144049435,\n",
       " 'ANASTASIJA': 1.4112267836744625,\n",
       " 'ANASTASIYA': 1.4993663745133239,\n",
       " 'ANASTASSIA': 2.374156853001239,\n",
       " 'ANASTASYA': 1.9514966073094575,\n",
       " 'ANASTAZIA': 1.597470756145238,\n",
       " 'ANASTAZJA': 1.4073503227211994,\n",
       " 'ANASTHASIA': 2.6028236682084276,\n",
       " 'ANASTHASIE': 2.0513457277095997,\n",
       " 'ANATALIE': 1.2136851176488221,\n",
       " 'ANATH': 1.0986122886681098,\n",
       " 'ANATHALIE': 1.3780492435635467,\n",
       " 'ANATHILDE': 1.0986122886681098,\n",
       " 'ANATOLE': -1.9247743479566535,\n",
       " 'ANATOLIE': 1.780912191221937,\n",
       " 'ANATOLINE': 1.269018033383787,\n",
       " 'ANAVAI': 1.0986122886681098,\n",
       " 'ANAYA': 4.553125061957832,\n",
       " 'ANAYAH': 2.8701321014222767,\n",
       " 'ANAYS': 2.166542922123099,\n",
       " 'ANAÉ': 6.053949132414534,\n",
       " 'ANAÉE': 2.5019864212192813,\n",
       " 'ANAË': 4.831218924622296,\n",
       " 'ANAËL': 0.5685659733433043,\n",
       " 'ANAËLE': 2.063305348336161,\n",
       " 'ANAËLLE': 5.805907343599582,\n",
       " 'ANAÏA': 3.4387756383014354,\n",
       " 'ANAÏCK': 1.4216292718168058,\n",
       " 'ANAÏDE': 1.2630020443548418,\n",
       " 'ANAÏG': 1.4622659150793218,\n",
       " 'ANAÏK': 1.0986122886681098,\n",
       " 'ANAÏKA': 1.4216292718168055,\n",
       " 'ANAÏS': 8.052578459792823,\n",
       " 'ANAÏSE': 1.1986756182165552,\n",
       " ...}"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epic={p:epicenity(p) for p in prenoms.index if epicenity(p)!=0}\n",
    "epic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "bb132bb5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-28T18:55:09.375679Z",
     "start_time": "2023-01-28T18:55:09.307002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'QUINCY': 0.005549224491322337,\n",
       " 'LAYAN': -0.006563082324214025,\n",
       " 'TIMOTHÉE': 0.010201005261469854,\n",
       " 'AWEN': -0.013628389756155579,\n",
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       " 'DAMASE': 0.02018318526676463,\n",
       " 'MAÏLAN': 0.022322268458134138,\n",
       " 'OCULI': 0.02654623833152187,\n",
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       " 'NONCE': 0.029980183808632804,\n",
       " 'WISAM': 0.03158394240196326,\n",
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       " 'BÉNEDICTE': 1.0986122886681098,\n",
       " 'CACILDA': 1.0986122886681098,\n",
       " 'CADIA': 1.0986122886681098,\n",
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       " 'CALLISTINE': 1.0986122886681098,\n",
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       " 'CALYSSE': 1.0986122886681098,\n",
       " 'CAMILLE-MARIE': 1.0986122886681098,\n",
       " 'CAMILLE-ROSE': 1.0986122886681098,\n",
       " 'CAMILLETTE': 1.0986122886681098,\n",
       " 'CAMMY': 1.0986122886681098,\n",
       " 'CANDACE': 1.0986122886681098,\n",
       " 'CANDELARIA': 1.0986122886681098,\n",
       " 'CANDYSSE': 1.0986122886681098,\n",
       " 'CAREL': 1.0986122886681098,\n",
       " 'CARELE': 1.0986122886681098,\n",
       " 'CARIN': 1.0986122886681098,\n",
       " 'CARLENE': 1.0986122886681098,\n",
       " 'CARMÉLA': 1.0986122886681098,\n",
       " 'CAROLAN': 1.0986122886681098,\n",
       " 'CASSIANA': 1.0986122886681098,\n",
       " 'CASSIDI': 1.0986122886681098,\n",
       " 'CATENA': 1.0986122886681098,\n",
       " 'CATERINE': 1.0986122886681098,\n",
       " 'CATHIANA': 1.0986122886681098,\n",
       " 'CATHLYNE': 1.0986122886681098,\n",
       " 'CATHÉRINE': 1.0986122886681098,\n",
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       " 'CELERINE': 1.0986122886681098,\n",
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       " 'CHAHINAIZE': 1.0986122886681098,\n",
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       " 'CHAHINESSE': 1.0986122886681098,\n",
       " 'CHAHRA-ZED': 1.0986122886681098,\n",
       " 'CHAIRA': 1.0986122886681098,\n",
       " 'CHAMSE': 1.0986122886681098,\n",
       " 'CHANCELVIE': 1.0986122886681098,\n",
       " 'CHANE': 1.0986122886681098,\n",
       " 'CHARAZAD': 1.0986122886681098,\n",
       " 'CHARIHANE': 1.0986122886681098,\n",
       " 'CHARIS': 1.0986122886681098,\n",
       " 'CHARLOTTINE': 1.0986122886681098,\n",
       " 'CHARLYNN': 1.0986122886681098,\n",
       " \"CHAYMA'A\": 1.0986122886681098,\n",
       " 'CHEDINE': 1.0986122886681098,\n",
       " 'CHEDLIA': 1.0986122886681098,\n",
       " 'CHEHINEZ': 1.0986122886681098,\n",
       " 'CHEHLA': 1.0986122886681098,\n",
       " 'CHEKINA': 1.0986122886681098,\n",
       " 'CHEN': 1.0986122886681098,\n",
       " 'CHERIN': 1.0986122886681098,\n",
       " 'CHERINA': 1.0986122886681098,\n",
       " 'CHERLINE': 1.0986122886681098,\n",
       " 'CHERRINE': 1.0986122886681098,\n",
       " 'CHERYLINE': 1.0986122886681098,\n",
       " 'CHEYNA': 1.0986122886681098,\n",
       " 'CHI': 1.0986122886681098,\n",
       " 'CHIMA': 1.0986122886681098,\n",
       " 'CHINA': 1.0986122886681098,\n",
       " 'CHIRYNE': 1.0986122886681098,\n",
       " 'CHLORANE': 1.0986122886681098,\n",
       " 'CHOUCHANE': 1.0986122886681098,\n",
       " 'CHOUMICHA': 1.0986122886681098,\n",
       " 'CHRISLYNE': 1.0986122886681098,\n",
       " 'CHRISLÈNE': 1.0986122886681098,\n",
       " 'CHRISTALINE': 1.0986122886681098,\n",
       " 'CHRISTANA': 1.0986122886681098,\n",
       " 'CHRISTELA': 1.0986122886681098,\n",
       " 'CHRYSLAINE': 1.0986122886681098,\n",
       " 'CHRYSTINA': 1.0986122886681098,\n",
       " 'CICEK': 1.0986122886681098,\n",
       " 'CICILIA': 1.0986122886681098,\n",
       " 'CIHEM': 1.0986122886681098,\n",
       " 'CILEM': 1.0986122886681098,\n",
       " 'CILLIA': 1.0986122886681098,\n",
       " 'CIYDEM': 1.0986122886681098,\n",
       " 'CLADY': 1.0986122886681098,\n",
       " 'CLAIRE-AGNÈS': 1.0986122886681098,\n",
       " 'CLAIRE-ALICE': 1.0986122886681098,\n",
       " 'CLAIRE-ALIX': 1.0986122886681098,\n",
       " ...}"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{k: v for k, v in sorted(epic.items(), key=lambda item: np.abs(item[1]))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "580ddaee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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