analyse_ventes_noblesse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "honey-edmonton",
   "metadata": {},
   "source": [
    "Données extraites de l'article:\n",
    "\n",
    "\n",
    "Lambert Étienne, « [Le marché foncier et la noblesse dans la région de Vassy (Normandie, 1760-1789)](https://doi.org/10.4000/histoiremesure.909)», Histoire & mesure 17(2002)1/2), pp. 163‑199. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "declared-budapest",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipysankeywidget import SankeyWidget\n",
    "from IPython.display import (\n",
    "    Image,\n",
    "    SVG\n",
    ")\n",
    "import base64\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hourly-patent",
   "metadata": {},
   "source": [
    "### Le tableau des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "civil-pocket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Année</th>\n",
       "      <th>Nb. transactions</th>\n",
       "      <th>Prix moyen seigle Domfront</th>\n",
       "      <th>Valeur total trans.</th>\n",
       "      <th>Valer hors nobles</th>\n",
       "      <th>Valeur nobles</th>\n",
       "      <th>% val noble</th>\n",
       "      <th>val noble corrige</th>\n",
       "      <th>valeurs soustraites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1760</td>\n",
       "      <td>125</td>\n",
       "      <td>3.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1761</td>\n",
       "      <td>195</td>\n",
       "      <td>3.8</td>\n",
       "      <td>70.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.571429</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1762</td>\n",
       "      <td>190</td>\n",
       "      <td>3.9</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1763</td>\n",
       "      <td>205</td>\n",
       "      <td>4.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1764</td>\n",
       "      <td>125</td>\n",
       "      <td>3.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Année  Nb. transactions  Prix moyen seigle Domfront  Valeur total trans.  \\\n",
       "0   1760               125                         3.5                 80.0   \n",
       "1   1761               195                         3.8                 70.0   \n",
       "2   1762               190                         3.9                 80.0   \n",
       "3   1763               205                         4.2                 70.0   \n",
       "4   1764               125                         3.5                 50.0   \n",
       "\n",
       "   Valer hors nobles  Valeur nobles  % val noble  val noble corrige  \\\n",
       "0               70.0           10.0    12.500000                 10   \n",
       "1               64.0            6.0     8.571429                  6   \n",
       "2               60.0           20.0    25.000000                 20   \n",
       "3               60.0           10.0    14.285714                 10   \n",
       "4               45.0            5.0    10.000000                  5   \n",
       "\n",
       "   valeurs soustraites  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'public_data/Lambert_2002_marche_foncier_donnees.csv'\n",
    "marche = pd.read_csv(file, sep='|')\n",
    "marche.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "organizational-loading",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = marche[marche['Année'] < 1779]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "revolutionary-meter",
   "metadata": {},
   "source": [
    "### Droite de régression comparant les effectifs des mouvements du marché et le prix du seigle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "caroline-stage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##\n",
    "# https://www.kite.com/python/answers/how-to-plot-a-linear-regression-line-on-a-scatter-plot-in-python\n",
    "\n",
    "x = np.array(df['Prix moyen seigle Domfront'])\n",
    "y = np.array(df['Nb. transactions'])\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 5]\n",
    "\n",
    "plt.plot(x, y, 'o')\n",
    "plt.title(\"Comparaison du prix du seigle et des mouvements du marché (effectifs)\")\n",
    "\n",
    "\n",
    "# m = slope, b=intercept\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "\n",
    "plt.plot(x, m*x + b)\n",
    "\n",
    "for i, label in enumerate(df['Année']):\n",
    "    plt.annotate(label, (x[i], y[i]))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "national-bruce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3245154797868773"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Coéfficient de corrélation de Pearsons\n",
    "# https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html\n",
    "np.corrcoef([x,y])[1][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "junior-dover",
   "metadata": {},
   "source": [
    "### Droite de régression comparant les valeurs totales des mouvements du marché et le prix du seigle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "suspended-bundle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##\n",
    "# https://www.kite.com/python/answers/how-to-plot-a-linear-regression-line-on-a-scatter-plot-in-python\n",
    "\n",
    "x = np.array(df['Prix moyen seigle Domfront'])\n",
    "y = np.array(df['Valeur total trans.']) #  Valeur total tans. / Valeur nobles  / val noble corrige\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 5]\n",
    "\n",
    "plt.plot(x, y, 'o')\n",
    "plt.title(\"Comparaison du prix du seigle et des mouvements du marché\")\n",
    "\n",
    "\n",
    "# m = slope, b=intercept\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "\n",
    "plt.plot(x, m*x + b)\n",
    "\n",
    "for i, label in enumerate(df['Année']):\n",
    "    plt.annotate(label, (x[i], y[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "radical-handbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4000805758182579"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Coéfficient de corrélation de Pearsons\n",
    "# https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html\n",
    "np.corrcoef([x,y])[1][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "level-talent",
   "metadata": {},
   "source": [
    "### Droite de régression comparant les valeurs des mouvements du marché avec acteurs non-nobles et le prix du seigle\n",
    "\n",
    "* Une variable cachée à mettre en évidence : les transactions des acteurs non-nobles, plus vraisemblablement impactées par le prix du blé"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "subject-spokesman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##\n",
    "# https://www.kite.com/python/answers/how-to-plot-a-linear-regression-line-on-a-scatter-plot-in-python\n",
    "\n",
    "x = np.array(df['Prix moyen seigle Domfront'])\n",
    "y = np.array(df['Valer hors nobles'])\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 5]\n",
    "\n",
    "plt.plot(x, y, 'o')\n",
    "plt.title(\"Comparaison du prix du seigle et des mouvements non.nobles du marché (Volume)\")\n",
    "\n",
    "\n",
    "# m = slope, b=intercept\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "\n",
    "plt.plot(x, m*x + b)\n",
    "\n",
    "for i, label in enumerate(df['Année']):\n",
    "    plt.annotate(label, (x[i], y[i]))\n",
    "\n",
    "plt.savefig('public_data/correlation.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "christian-following",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7506725079044456"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Coéfficient de corrélation de Pearsons – montre une relation entre les variables\n",
    "# https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html\n",
    "np.corrcoef([x,y])[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "roman-dallas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [16, 8]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "df['Valer hors nobles'].plot(kind='bar', color='red')\n",
    "\n",
    "va1 = [a*10 for a in df['Prix moyen seigle Domfront']]\n",
    "plt.plot(va1, marker='o', color='black', ms=10)\n",
    "ax.set_xticklabels(df['Année'])\n",
    "for i, v in enumerate(va1):\n",
    "    ax.text(i, v + 5, v/10, ha=\"center\", fontsize=20 )\n",
    "\n",
    "plt.title('Valeur des transactions (non nobles) et prix du seigle',fontsize=12)\n",
    "16\n",
    "plt.legend(['Prix du seigle', 'Volume transactions\\nen milliers de livres'],loc=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "australian-diamond",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "opening-credits",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "crude-alias",
   "metadata": {},
   "source": [
    "## Les flux des ventes de la noblesse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ambient-digit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noblesse</td>\n",
       "      <td>à Noblesse</td>\n",
       "      <td>20.950486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Noblesse</td>\n",
       "      <td>à Laboureur</td>\n",
       "      <td>7.885553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noblesse</td>\n",
       "      <td>à Paysan</td>\n",
       "      <td>2.158075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Noblesse</td>\n",
       "      <td>à Journalier</td>\n",
       "      <td>0.108338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noblesse</td>\n",
       "      <td>à Commerce</td>\n",
       "      <td>29.744837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     source        target      value\n",
       "0  Noblesse    à Noblesse  20.950486\n",
       "1  Noblesse   à Laboureur   7.885553\n",
       "2  Noblesse      à Paysan   2.158075\n",
       "3  Noblesse  à Journalier   0.108338\n",
       "4  Noblesse    à Commerce  29.744837"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = \"public_data/sankey_ventes.csv\"\n",
    "s1 = pd.read_csv(source, sep='|')\n",
    "s1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "magnetic-keeping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'source': 'Noblesse', 'target': 'à Noblesse', 'value': 20.9504864247597},\n",
       " {'source': 'Noblesse', 'target': 'à Laboureur', 'value': 7.88555292843078},\n",
       " {'source': 'Noblesse', 'target': 'à Paysan', 'value': 2.1580752366242297},\n",
       " {'source': 'Noblesse', 'target': 'à Journalier', 'value': 0.108338396445329},\n",
       " {'source': 'Noblesse', 'target': 'à Commerce', 'value': 29.7448374558045},\n",
       " {'source': 'Noblesse', 'target': 'à Artisanat', 'value': 0.5618224275120971},\n",
       " {'source': 'Noblesse',\n",
       "  'target': 'à Prof. libérale',\n",
       "  'value': 2.65978083435206},\n",
       " {'source': 'Noblesse', 'target': 'à Clergé', 'value': 4.88273100600985},\n",
       " {'source': 'Noblesse', 'target': 'à Inconnu', 'value': 1.7309437884766001},\n",
       " {'source': 'Laboureur', 'target': 'à Noblesse', 'value': 3.3008440146696003},\n",
       " {'source': 'Paysan', 'target': 'à Noblesse', 'value': 2.25836145495538},\n",
       " {'source': 'Journalier', 'target': 'à Noblesse', 'value': 0.18620661889041},\n",
       " {'source': 'Commerce', 'target': 'à Noblesse', 'value': 20.9655842587238},\n",
       " {'source': 'Artisanat', 'target': 'à Noblesse', 'value': 0.37918438755865297},\n",
       " {'source': 'Prof. libérale',\n",
       "  'target': 'à Noblesse',\n",
       "  'value': 0.354112832975865},\n",
       " {'source': 'Clergé', 'target': 'à Noblesse', 'value': 1.00926732499323},\n",
       " {'source': 'Inconnu', 'target': 'à Noblesse', 'value': 0.863870608817867}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sq_dict = s1.to_dict(orient='records')\n",
    "sq_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "nasty-variable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37b841792f484bdfaa00f0969171f92d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "SankeyWidget(layout=Layout(width='800'), links=[{'source': 'Noblesse', 'target': 'à Noblesse', 'value': 20.950…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w1 = SankeyWidget(links=sq_dict, layout=dict(width='800'), margins=dict(top=0, bottom=0, left=100, right=100))\n",
    "w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "elder-uniform",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: public_data/w1.svg: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!rm 'public_data/w1.svg'\n",
    "w1.save_svg('public_data/w1.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "julian-peninsula",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"public_data/w1.svg\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src=\"public_data/w1.svg\" />"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Py 3.7 HN sparql",
   "language": "python",
   "name": "py37_hn_sparql"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}