diff --git a/notebooks/code_CASD/extractions_erfs/statistiques_descriptives_cas_types_dixiemes.ipynb b/notebooks/code_CASD/extractions_erfs/statistiques_descriptives_cas_types_dixiemes.ipynb
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index 0000000000000000000000000000000000000000..a80910e072119e91b75a64bb05bef76d403ad5ac
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@@ -0,0 +1,1415 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ce script tourne avec le kernel jupyter leximpact2024 issu de l'environnement conda leximpa construit à partir des dépendances de leximpact-prepare-data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from openfisca_survey_manager.statshelpers import mark_weighted_percentiles\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.express as px \n",
+ "from plotly.subplots import make_subplots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path_erfs_brut = \"//casd.fr/casdfs/Projets/LEXIMPA/Data/ERFS_ERFS_2021\"\n",
+ "output_path = \"C:/Users/Public/Documents/TRAVAIL/code_cas_type_ERFS_2021/sorties\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages = pd.read_parquet(f\"{path_erfs_brut}/menage21_aus.parquet\")\n",
+ "print(f\"nb menages avant selection : {len(menages)}\")\n",
+ "print(f\"poids menages avant selection : {round(menages.wprm.sum())}\")\n",
+ "menages = menages.loc[menages['champm'] == 1 ].loc[menages[\"nivviem\"] > 0].sort_values(\"nivviem\").reset_index()\n",
+ "print(f\"nb menages après selection : {len(menages)}\")\n",
+ "print(f\"poids menages après selection : {round(menages.wprm.sum())}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "individus = pd.read_parquet(f\"{path_erfs_brut}/indivi21_aus.parquet\")\n",
+ "print(f\"nb individus avant selection : {len(individus)}\")\n",
+ "print(f\"poids individus avant selection : {round(individus.wprm.sum())}\")\n",
+ "individus = individus.loc[individus['ident21'].isin(set(menages.ident21.unique().tolist()))].sort_values(\"nivviem\").reset_index()\n",
+ "print(f\"nb individus après selection : {len(individus)}\")\n",
+ "print(f\"poids individus après selection : {round(individus.wprm.sum())}\")\n",
+ "individus[\"rev_activite_decl\"] = individus.zindi + individus.zsali\n",
+ "individus.rename(columns = {\"zchoi\":\"chomage_decl\",\"zperi\":\"pensions_decl\"}, inplace = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# individus.groupby(\"ident21\").agg({'noindiv':'count',\n",
+ "# 'wprm':'min'}).groupby('noindiv').agg({\"wprm\":\"sum\"})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Type de ménage\n",
+ "\n",
+ "- 1: personne seule\n",
+ "- 2: famille monoparentale\n",
+ "- 3: couple sans enfant\n",
+ "- 4: couple avec enfant\n",
+ "- 5: autre"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "personne_reference = individus.loc[individus[\"noi\"] == \"01\"][[\"ident21\", \"TYPMEN5\",\"ETAMATRI_Y_comp\",\"NBENFMENCHAMP\",\"rev_activite_decl\", \"chomage_decl\", \"pensions_decl\"]]\n",
+ "personne_reference.TYPMEN5.isna().sum()\n",
+ "personne_reference.groupby(\"TYPMEN5\").agg({'ident21':'count'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conjoint = individus.loc[individus[\"noi\"] == \"02\"][[\"ident21\", \"rev_activite_decl\", \"chomage_decl\", \"pensions_decl\"]].rename(columns = {\"rev_activite_decl\":\"rev_activite_conj\",\"chomage_decl\":\"chomage_conj\",\"pensions_decl\":\"pensions_conj\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages = pd.merge(menages,personne_reference, how='outer', on='ident21')\n",
+ "menages = pd.merge(menages,conjoint, how='outer', on='ident21')\n",
+ "menages.TYPMEN5.isna().sum()\n",
+ "menages[\"TYPMEN5\"] = np.where(menages.TYPMEN5.isna(),\"5\",menages[\"TYPMEN5\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for conj_col in [\"rev_activite_decl\", \"chomage_decl\", \"pensions_decl\",\"rev_activite_conj\", \"chomage_conj\", \"pensions_conj\"]:\n",
+ " menages[conj_col] = menages[conj_col].fillna(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"acteu5pr_lab\"] = menages.acteu5pr.map({\"1\":\"salarié\", \"2\":\"indépendant\",\"3\":\"chomeur\",\"4\":\"retraité\",\"5\":\"inactif autre\"})\n",
+ "menages[\"acteu5cj_lab\"] = menages.acteu5cj.map({\"1\":\"salarié\", \"2\":\"indépendant\",\"3\":\"chomeur\",\"4\":\"retraité\",\"5\":\"inactif autre\"})\n",
+ "menages[\"TYPMEN5_lab\"] = menages.TYPMEN5.map({\"1\":\"personne seule\", \"2\":\"famille monoparentale\",\"3\":\"couple sans enfant\",\"4\":\"couple avec enfant(s)\",\"5\":\"autre / n.a\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ret, quant = mark_weighted_percentiles(a = menages['nivviem'], labels = np.arange(1, 11), weights = menages['wprm'], method = 2, return_quantiles = True)\n",
+ "menages[\"dixieme\"] = ret"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "quant"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages.groupby(\"dixieme\").agg({\"nivviem\": \"min\"})\n",
+ "# Les résultats sont cohérants avec la distribution publiée par l'INSEE en date du 14/11/2023 \"Distribution des niveaux de vie, données annuelles de 1996 à 2021\"\n",
+ "# --> Prendre les résutlats de cette publication pour les dixièmes de cas types dans l'interface"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## nombre d'enfants dans le ménage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages['categ_nbenfc'] = np.where(menages[\"NBENFMENCHAMP\"] > 3,3,menages[\"NBENFMENCHAMP\"])\n",
+ "menages['categ_nbenfc'] = menages.categ_nbenfc.map({0.0:\"Pas d'enfant\", 1.0:\"1 enfant\",2.0:\"2 enfants\",3.0:\"3 enfants et plus\"})\n",
+ "nb_enfc = menages.groupby([\"dixieme\", \"categ_nbenfc\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_enfc[\"prop\"] = round(100 * nb_enfc.wprm /nb_enfc.groupby(\"dixieme\")['wprm'].transform(\"sum\"))\n",
+ "nb_enfc[\"distrib\"] = round(100 * nb_enfc.wprm /nb_enfc.groupby(\"categ_nbenfc\")['wprm'].transform(\"sum\"))\n",
+ "nb_enfc.rename(columns={\"ident21\":\"count\"},inplace = True)\n",
+ "nb_enfc.to_csv(f\"{output_path}/nb_encf.csv\")\n",
+ "print(\"Nombre de ménages selon le nombre d'enfants dans le ménage : \")\n",
+ "nb_enfc.groupby(\"categ_nbenfc\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du nombre d'enfants dans ménage par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du nombre d'enfants\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## nombre de parents / couple"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_typmen = menages.groupby([\"dixieme\", \"TYPMEN5_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_typmen[\"prop\"] = round(100 * nb_typmen.wprm /nb_typmen.groupby(\"dixieme\")['wprm'].transform(\"sum\"))\n",
+ "nb_typmen[\"distrib\"] = round(100 * nb_typmen.wprm /nb_typmen.groupby(\"TYPMEN5_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_typmen.rename(columns={\"ident21\":\"count\"},inplace = True)\n",
+ "nb_typmen.to_csv(f\"{output_path}/nb_typmen.csv\")\n",
+ "nb_typmen.groupby(\"TYPMEN5_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_typmen,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"TYPMEN5_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du type de ménage par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_typmen,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"TYPMEN5_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction de leur type\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### focus sur le nombre d'enfants en fonction de en couple / familles monoparentales"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_enfc_couple = menages.loc[menages[\"categ_nbenfc\"] != \"Pas d'enfant\"].groupby([\"dixieme\", \"TYPMEN5\", \"TYPMEN5_lab\", \"categ_nbenfc\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_enfc_couple.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_enfc_couple[\"prop\"] = round(100 * nb_enfc_couple.wprm /nb_enfc_couple.groupby([\"dixieme\",\"TYPMEN5\",\"TYPMEN5_lab\"])['wprm'].transform(\"sum\"))\n",
+ "nb_enfc_couple[\"distrib\"] = round(100 * nb_enfc_couple.wprm /nb_enfc_couple.groupby([\"categ_nbenfc\", \"TYPMEN5\",\"TYPMEN5_lab\"])['wprm'].transform(\"sum\"))\n",
+ "nb_enfc_couple[\"somme_categ_parents\"] = nb_enfc_couple.groupby([\"categ_nbenfc\", \"TYPMEN5\",\"TYPMEN5_lab\"])['wprm'].transform(\"sum\")\n",
+ "nb_enfc_couple[\"wprm\"] = np.where((nb_enfc_couple.categ_nbenfc != \"1 enfant\") & (nb_enfc_couple.dixieme >= 6), np.nan, nb_enfc_couple[\"wprm\"])\n",
+ "nb_enfc_couple = nb_enfc_couple.loc[nb_enfc_couple.wprm.isna() == False]\n",
+ "nb_enfc_couple.to_csv(f\"{output_path}/nb_enfc_couple.csv\")\n",
+ "nb_enfc_couple.groupby(\"categ_nbenfc\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_fig = nb_enfc_couple[[\"categ_nbenfc\", \"somme_categ_parents\",\"TYPMEN5\",\"TYPMEN5_lab\"]].drop_duplicates()\n",
+ "fig = make_subplots(\n",
+ " rows = 1,\n",
+ " cols = 2,\n",
+ " specs=[\n",
+ " [{\"type\":\"domain\"},{\"type\":\"domain\"}]\n",
+ " ],\n",
+ " subplot_titles= [f\"Familles monoparentales : {round(data_fig.loc[nb_enfc_couple['TYPMEN5'] == '2'].somme_categ_parents.sum()/1e6, ndigits = 2)} millions de ménages\", f\"Couples : {round(data_fig.loc[nb_enfc_couple['TYPMEN5'] == '4'].somme_categ_parents.sum()/1e6, ndigits = 2)} millions de ménages\"]\n",
+ ")\n",
+ "\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = data_fig.loc[data_fig[\"TYPMEN5\"] == \"2\"].categ_nbenfc,\n",
+ " values = data_fig.loc[data_fig[\"TYPMEN5\"] == \"2\"].somme_categ_parents,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Famille monoparentale\"\n",
+ " ),\n",
+ " 1,\n",
+ " 1\n",
+ ")\n",
+ "\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = data_fig.loc[data_fig[\"TYPMEN5\"] == \"4\"].categ_nbenfc,\n",
+ " values = data_fig.loc[data_fig[\"TYPMEN5\"] == \"4\"].somme_categ_parents,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Couple\"\n",
+ " ),\n",
+ " 1,\n",
+ " 2\n",
+ ")\n",
+ "fig.update_layout(legend = dict(title = \"Nombre d'enfants\"))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc_couple,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " text_auto = True,\n",
+ " nbins = 10,\n",
+ " facet_col = \"TYPMEN5_lab\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du nombre d'enfants à charge par dixieme de niveau de vie, à gauche les familles monoparentales, à droite les couples\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc_couple,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " facet_col = \"TYPMEN5_lab\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du nombre d'enfants, à gauche les familles monoparentales, à droite les couples\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### statut marital des couples\n",
+ "\n",
+ "- 1: Marié.e\n",
+ "- 2: Pacsé.e\n",
+ "- 3: union libre\n",
+ "- 4: veuf.ve\n",
+ "- 5: divorcé.e\n",
+ "- 6: dépacsé.e\n",
+ "- 7: séparé.e\n",
+ "- 8: célibataire\n",
+ "- 9: non répose"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages['categ_etamatri'] = menages.ETAMATRI_Y_comp.map({\"1\":\"Marié/Pacsé\",\"2\":\"Marié/Pacsé\",\"3\":\"Union libre\",\"4\":\"Célibataire\",\"5\":\"Célibataire\",\"6\":\"Célibataire\",\"7\":\"Célibataire\",\"8\":\"Célibataire\",\"9\":\"n.a\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_enfc_couple = menages.groupby([\"dixieme\", \"TYPMEN5\", \"categ_etamatri\"]).agg({\"wprm\":\"sum\"}).reset_index()\n",
+ "nb_enfc_couple[\"prop\"] = round(100 * nb_enfc_couple.wprm /nb_enfc_couple.groupby([\"dixieme\",\"TYPMEN5\"])['wprm'].transform(\"sum\"))\n",
+ "nb_enfc_couple[\"distrib\"] = round(100 * nb_enfc_couple.wprm /nb_enfc_couple.groupby([\"categ_etamatri\", \"TYPMEN5\"])['wprm'].transform(\"sum\"))\n",
+ "nb_enfc_couple.groupby(\"categ_etamatri\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc_couple,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_etamatri\",\n",
+ " text_auto = True,\n",
+ " nbins = 10,\n",
+ " facet_col = \"TYPMEN5\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du nombre d'enfants à charge par dixieme de niveau de vie, à gauche les familles monoparentales, à droite les couples\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc_couple,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_etamatri\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " facet_col = \"TYPMEN5\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du nombre d'enfants, à gauche les familles monoparentales, à droite les couples\")\n",
+ "fig.show()\n",
+ "\n",
+ "# - 1: Marié.e\n",
+ "# - 2: Pacsé.e\n",
+ "# - 3: union libre\n",
+ "# - 4: veuf.ve\n",
+ "# - 5: divorcé.e\n",
+ "# - 6: dépacsé.e\n",
+ "# - 7: séparé.e\n",
+ "# - 8: célibataire\n",
+ "# - 9: non répose"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Statuts d'activité de la personne de référence et du conjoint\n",
+ "\n",
+ "- 1: salarié\n",
+ "- 2: indépendant\n",
+ "- 3: chômeur\n",
+ "- 4: retraité \n",
+ "- 5: inactif non retraité"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"Nombre de na pour le conjoint : {menages.acteu5cj.isna().sum()}\")\n",
+ "menages.groupby(\"acteu5cj_lab\").agg({\"wprm\":\"count\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages.loc[menages.acteu5cj.isna()].groupby(\"TYPMEN5_lab\").agg({\"wprm\": \"sum\"}) # que 300 \"anomalie\" de couple où pas de conjoint retrouvé"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages.loc[menages.acteu5cj.isna() == False].groupby(\"TYPMEN5_lab\").agg({\"wprm\": \"sum\"}) # plus d'anomalie"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"Nombre de na pour la personne de référence : {menages.acteu5pr.isna().sum()}\")\n",
+ "menages.groupby(\"acteu5pr_lab\").agg({\"wprm\":\"count\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb = menages.groupby([\"dixieme\", \"acteu5pr_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb[\"prop\"] = round(100 * nb.wprm /nb.groupby(\"dixieme\")['wprm'].transform(\"sum\"))\n",
+ "nb[\"distrib\"] = round(100 * nb.wprm /nb.groupby(\"acteu5pr_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb.to_csv(f\"{output_path}/nb_acteu5pr.csv\")\n",
+ "nb.groupby(\"acteu5pr_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"acteu5pr_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du statut d'activité de la personne de référence par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"acteu5pr_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du statut d'activité de la personne de référence\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb = menages.loc[menages[\"TYPMEN5\"] != \"1\"].loc[menages[\"TYPMEN5\"] != \"2\"].loc[menages[\"TYPMEN5\"] != \"5\"].groupby([\"dixieme\", \"acteu5cj_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb[\"prop\"] = round(100 * nb.wprm /nb.groupby(\"dixieme\")['wprm'].transform(\"sum\"))\n",
+ "nb[\"distrib\"] = round(100 * nb.wprm /nb.groupby(\"acteu5cj_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb.to_csv(f\"{output_path}/nb_acteu5cj.csv\")\n",
+ "nb.groupby(\"acteu5cj_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#new_name = {\"1\":\"salarié.e\", \"2\":\"indépendant.e\",\"3\":\"chomeureuse\",\"4\":\"retraité\",\"5\":\"inactif.ve autre\"}\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"acteu5cj_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du statut d'activité du conjoint par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"acteu5cj_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du statut d'activité du conjoint\")\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb = menages.loc[menages[\"TYPMEN5\"] != \"1\"].loc[menages[\"TYPMEN5\"] != \"2\"].loc[menages[\"TYPMEN5\"] != \"5\"].groupby([\"dixieme\", \"acteu5pr\", \"acteu5cj\"]).agg({\"wprm\":\"sum\"}).reset_index()\n",
+ "nb[\"prop\"] = round(100 * nb.wprm /nb.groupby([\"dixieme\",\"acteu5pr\"])['wprm'].transform(\"sum\"))\n",
+ "nb[\"distrib\"] = round(100 * nb.wprm /nb.groupby([\"acteu5cj\", \"acteu5pr\"])['wprm'].transform(\"sum\"))\n",
+ "nb.groupby(\"acteu5cj\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = make_subplots(\n",
+ " rows = 3,\n",
+ " cols = 2,\n",
+ " specs=[\n",
+ " [{\"type\":\"domain\"},{\"type\":\"domain\"}],\n",
+ " [{\"type\":\"domain\"},{\"type\":\"domain\"}],\n",
+ " [{\"type\":\"domain\"},{\"type\":\"domain\"}]\n",
+ " ],\n",
+ " horizontal_spacing=0,\n",
+ " vertical_spacing=0\n",
+ " #subplot_titles= [f\"Familles monoparentales : {round(nb_enfc_couple.loc[nb_enfc_couple['TYPMEN5'] == '2'].wprm.sum()/1e6, ndigits = 2)} millions de ménages\", f\"Couples : {round(nb_enfc_couple.loc[nb_enfc_couple['TYPMEN5'] == '4'].wprm.sum()/1e6, ndigits = 2)} millions de ménages\"]\n",
+ ")\n",
+ "\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = nb.loc[nb[\"acteu5pr\"] == \"1\"].acteu5cj,\n",
+ " values = nb.loc[nb[\"acteu5pr\"] == \"1\"].wprm,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Salarié\"\n",
+ " ),\n",
+ " 1,\n",
+ " 1\n",
+ ")\n",
+ "\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = nb.loc[nb[\"acteu5pr\"] == \"2\"].acteu5cj,\n",
+ " values = nb.loc[nb[\"acteu5pr\"] == \"2\"].wprm,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Indépendant.e\"\n",
+ " ),\n",
+ " 1,\n",
+ " 2\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = nb.loc[nb[\"acteu5pr\"] == \"3\"].acteu5cj,\n",
+ " values = nb.loc[nb[\"acteu5pr\"] == \"3\"].wprm,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Chomeureuse\"\n",
+ " ),\n",
+ " 2,\n",
+ " 1\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = nb.loc[nb[\"acteu5pr\"] == \"4\"].acteu5cj,\n",
+ " values = nb.loc[nb[\"acteu5pr\"] == \"4\"].wprm,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Retraité.e\"\n",
+ " ),\n",
+ " 2,\n",
+ " 2\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = nb.loc[nb[\"acteu5pr\"] == \"5\"].acteu5cj,\n",
+ " values = nb.loc[nb[\"acteu5pr\"] == \"5\"].wprm,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Inactif.ve autre\"\n",
+ " ),\n",
+ " 3,\n",
+ " 1\n",
+ ")\n",
+ "fig.update_layout(legend = dict(title = \"Statut d'activité\",x= 0.75,y=0.005), margin = dict(l=10,r=10,b=0,t=0))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"acteu5cj\",\n",
+ " text_auto = True,\n",
+ " nbins = 10,\n",
+ " facet_col = \"acteu5pr\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du nombre d'enfants à charge par dixieme de niveau de vie, à gauche les familles monoparentales, à droite les couples\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"acteu5cj\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " facet_col = \"acteu5pr\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du nombre d'enfants, à gauche les familles monoparentales, à droite les couples\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## statut d'occupation du logement \n",
+ "- 1 accédant à la propriété\n",
+ "- 2 propriétaire\n",
+ "- 3 locataire HLM\n",
+ "- 4 locataire vide\n",
+ "- 5 locataire meublé\n",
+ "- 6 logé gratuitement\n",
+ "- 7 usufruitier\n",
+ "- 9 non réponse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages.groupby(\"logT\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages['categ_logmnt'] = np.where(menages[\"logT\"] == \"5\",\"4\",menages[\"logT\"])\n",
+ "menages['categ_logmnt'] = np.where(menages[\"logT\"] == \"9\",\"6\", menages[\"categ_logmnt\"])\n",
+ "menages['categ_logmnt'] = np.where(menages[\"logT\"] == \"1\",\"2\", menages[\"categ_logmnt\"])\n",
+ "menages['categ_logmnt_lab'] = menages.categ_logmnt.map({\"2\":\"Propriétaire\",\"3\":\"Locataire HLM\",\"4\":\"Locataire privé\",\"6\":\"Autre\"})\n",
+ "nb_logtm = menages.groupby([\"dixieme\", \"categ_logmnt_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"dixieme\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_logtm.to_csv(f\"{output_path}/nb_logtm.csv\")\n",
+ "nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#new_name = {\"2\":\"Propriétaire\",\"3\":\"Locataire HLM\",\"4\":\"Locataire privé\",\"6\":\"Autre\"}\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_logmnt_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_logmnt_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction de leur statut d'occupation du logement\")\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Part des propriétaires en fonction du statut d'occupation du type de ménage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_logtm = menages.groupby([\"dixieme\", \"categ_logmnt_lab\", \"TYPMEN5_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby([\"dixieme\", \"TYPMEN5_lab\"])['wprm'].transform(\"sum\"))\n",
+ "#nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_logtm = nb_logtm.loc[nb_logtm[\"categ_logmnt_lab\"] == \"Propriétaire\"]\n",
+ "nb_logtm = nb_logtm.loc[nb_logtm[\"TYPMEN5_lab\"] != \"autre / n.a\"]\n",
+ "nb_logtm.to_csv(f\"{output_path}/nb_logtm_typmen.csv\")\n",
+ "#nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " barmode=\"group\",\n",
+ " color = \"TYPMEN5_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_logtm = menages.groupby([\"dixieme\", \"categ_logmnt_lab\", \"acteu5pr_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby([\"dixieme\", \"acteu5pr_lab\"])['wprm'].transform(\"sum\"))\n",
+ "#nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_logtm = nb_logtm.loc[nb_logtm[\"categ_logmnt_lab\"] == \"Propriétaire\"]\n",
+ "nb_logtm.to_csv(f\"{output_path}/nb_logtm_acteu5pr.csv\")\n",
+ "#nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " barmode=\"group\",\n",
+ " color = \"acteu5pr_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des propriétaires en fonction du statut d'activité de la personne de référence\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Revenus déclarés"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "revdispm =\n",
+ "\n",
+ "+ revperm (revdecm (ztsam (zsalm + zchom) + zperm (zrstm + zpim + zrtom + zalrm) + zragm + zricm + zrncm + zfonm + zvamm + zvalm + zracm + zetrm -zalvm) + revexom + produitfin - csgim (csgsalim + csghsupim + csgchoim + csgrstim + csgragim + csgricim + csgrncim + csgpiim) - crdsm ( crdssalm + crdshsupm + crdschom + crdsrstm + crdsragm + crdsricm + crdsrncm + crdspim) - csgimpm (produits financiers imputés) - csgvalm (csg crds valeurs mobilières) - zimpvalm (prélèvement libératoire sur valeur mobilière du ménage))\n",
+ "+ psocm (pfamm + mibim + logtm)\n",
+ "- zimpom (impot sur le revenu)\n",
+ "- zthabm\n",
+ "- csgpatm (csg-crds sur revenus du patrimoine)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "prelevements = zimpom + zthabm + csgpatm + zimpvalm + csgvalm + csgimpm + crdsm + csgim\n",
+ "\n",
+ "prestations = pfamm + minim + logtm\n",
+ "\n",
+ "revenus = revenus_activite + revenus_remplacement + revenus_foncier_capital\n",
+ "\n",
+ "revenus_activite = zsalm + revexom + zragm + zricm + zrncm + zracm - zalvm + zetrm\n",
+ "revenus_remplacement = zchom + zrstm + zpim + zrtom + zalrm\n",
+ "revenus_foncier_capital = zfonm + zvamm + zvalm + produitfin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"prelevements\"] = menages.zimpom + menages.zthabm + menages.csgpatm + menages.zimpvalm + menages.csgvalm + menages.csgimpm + menages.crdsm + menages.csgim\n",
+ "menages[\"impots\"] = menages.zimpom + menages.zthabm + menages.zimpvalm\n",
+ "# prelevements = impots + csg imposable + crds\n",
+ "menages.rename(columns = {\"psocm\":\"prestations\"},inplace = True)\n",
+ "menages[\"revenus_activite\"] = menages.zsalm + menages.revexom + menages.zragm + menages.zricm + menages.zrncm + menages.zracm - menages.zalvm + menages.zetrm\n",
+ "# traitement et salaires hors chomage, revenus exonérés (primes / heures sup.) + revenus des indépendants + revenus étrangers - pensions alimentaires versées\n",
+ "menages[\"revenus_remplacement\"] = menages.zchom + menages.zrstm + menages.zpim + menages.zrtom + menages.zalrm\n",
+ "# chomage + retraite + pension invalidite + rente viagière à titre onéreux + pension alimentaire recue\n",
+ "menages[\"revenus_foncier_capital\"] = menages.zfonm + menages.zvamm + menages.zvalm + menages.produitfin\n",
+ "menages[\"revenus\"] = menages[\"revenus_activite\"] + menages[\"revenus_remplacement\"] + menages[\"revenus_foncier_capital\"]\n",
+ "menages[\"diff\"] = menages[\"revdispm\"] - (menages[\"revenus\"] - menages[\"prelevements\"] + menages[\"prestations\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sum(abs(menages[\"diff\"]) > 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"revdecm_net\"] = menages[\"revdecm\"] - menages[\"csgdm\"]\n",
+ "# revenus = [\n",
+ "# \"ztsam\", # zchom + zsalm\n",
+ "# \"zchom\",\n",
+ "# \"zsalm\",\n",
+ "# \"zperm\",\n",
+ "# \"zalrm\", # pensions alim recues, dans zalrm\n",
+ "# \"zragm\",\n",
+ "# \"zricm\",\n",
+ "# \"zrncm\",\n",
+ "# \"zfonm\",\n",
+ "# \"zvamm\",\n",
+ "# \"zvalm\",\n",
+ "# \"zracm\",\n",
+ "# \"zetrm\",\n",
+ "# \"zalvm\",\n",
+ "# ]\n",
+ "\n",
+ "revenus = [\n",
+ " \"revenus_activite\",\n",
+ " \"revenus_remplacement\",\n",
+ " \"revenus_foncier_capital\"\n",
+ "]\n",
+ "\n",
+ "prelevements = [\"prelevements\", \"impots\"]\n",
+ "\n",
+ "rev_tot = [ \n",
+ " \"revdecm\",\n",
+ " \"revdecm_net\",\n",
+ " \"revdispm\",\n",
+ " \"revenus\",\n",
+ " \"prestations\"] \n",
+ "for rev in revenus + rev_tot + prelevements:\n",
+ " menages[f\"{rev}_pond\"] = menages[rev].fillna(0) * menages.wprm\n",
+ " menages[f\"{rev}_uc_pond\"] = menages[rev].fillna(0) / menages[\"nb_UCI\"].fillna(1) * menages.wprm\n",
+ "\n",
+ "agg = {f\"{rev}_pond\":\"sum\" for rev in revenus + rev_tot + prelevements}\n",
+ "agg.update({\"wprm\":\"sum\",\"ident21\":\"count\"})\n",
+ "agg.update({f\"{rev}_uc_pond\":\"sum\" for rev in revenus + rev_tot + prelevements})\n",
+ "menages[\"revenus_individus\"] = (np.maximum(0,menages[\"rev_activite_conj\"]) + menages[\"chomage_conj\"] + menages[\"pensions_conj\"] + np.maximum(0,menages[\"rev_activite_decl\"]) + menages[\"chomage_decl\"] + menages[\"pensions_decl\"])\n",
+ "menages[\"part_rev_decl\"] = np.where(menages[\"revenus_individus\"] > 0, np.maximum(0,menages[\"rev_activite_decl\"]) + menages[\"chomage_decl\"] + menages[\"pensions_decl\"] / menages[\"revenus_individus\"],0)\n",
+ "menages[\"prestations_positives\"] = (menages[\"prestations\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_positifs\"] = (menages[\"revenus\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_act_positifs\"] = (menages[\"revenus_activite\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_remp_positifs\"] = (menages[\"revenus_remplacement\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_fon_cap_positifs\"] = (menages[\"revenus_foncier_capital\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"impot_negatif\"] = (menages[\"impots\"] > 0) * menages[\"wprm\"]# si impot positif alors le menage paie de l'impot\n",
+ "menages[\"ni_impot_ni_presta\"] =(menages[\"prestations\"] == 0) * (menages[\"impots\"] == 0) * menages[\"wprm\"]\n",
+ "agg.update({\"impot_negatif\":\"sum\",\"revenus_positifs\":\"sum\",\"revenus_act_positifs\":\"sum\",\"revenus_remp_positifs\":\"sum\",\"revenus_fon_cap_positifs\":\"sum\",\"prestations_positives\":\"sum\",\"ni_impot_ni_presta\":\"sum\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# agg = {f\"{rev}\":\"max\" for rev in revenus + rev_tot + prelevements}\n",
+ "# nb_max = menages.groupby([\"dixieme\"]).agg(agg).reset_index()\n",
+ "# nb_max.rename(columns = {rev:f\"{rev}_max\" for rev in nb_max.columns if rev != \"dixieme\"}, inplace = True)\n",
+ "# agg = {f\"{rev}\":\"sum\" for rev in revenus + rev_tot + prelevements}\n",
+ "# nb = menages.groupby([\"dixieme\"]).agg(agg).reset_index()\n",
+ "# nb = pd.merge(nb_max,nb,on = \"dixieme\")\n",
+ "# for rev in revenus + rev_tot + prelevements:\n",
+ "# nb[f\"{rev}_part_max\"] = nb[f\"{rev}_max\"] / nb[f\"{rev}\"]\n",
+ "# nb.to_csv(f\"{output_path}/check_part_max_nb_revenus.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb = menages.groupby([\"dixieme\"]).agg(agg).reset_index()\n",
+ "nb.to_csv(f\"{output_path}/nb_revenus.csv\")\n",
+ "for rev in revenus + rev_tot + prelevements:\n",
+ " nb[f\"{rev}_prop\"] = nb[f\"{rev}_pond\"] / nb[\"revenus_pond\"]\n",
+ " nb[f\"{rev}_moy\"] = nb[f\"{rev}_pond\"] / nb[\"wprm\"]\n",
+ " nb[f\"{rev}_uc_prop\"] = nb[f\"{rev}_uc_pond\"] / nb[\"revenus_uc_pond\"]\n",
+ " nb[f\"{rev}_uc_moy\"] = nb[f\"{rev}_uc_pond\"] / nb[\"wprm\"]\n",
+ "nb[\"prestations_positives_prop\"] = nb[\"prestations_positives\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_positifs_prop\"] = nb[\"revenus_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_act_positifs_prop\"] = nb[\"revenus_act_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_remp_positifs_prop\"] = nb[\"revenus_remp_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_fon_cap_positifs_prop\"] = nb[\"revenus_fon_cap_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"impot_negatif_prop\"] = nb[\"impot_negatif\"] / nb[\"wprm\"]\n",
+ "nb[\"ni_impot_ni_presta_prop\"] = nb[\"ni_impot_ni_presta\"] / nb[\"wprm\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig.add_trace(\n",
+ " go.Line(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revdecm_uc_moy\"],\n",
+ " name = \"revdecm_uc_moy\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Line(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revdispm_uc_moy\"],\n",
+ " name = \"revdispm_uc_moy\",\n",
+ " )\n",
+ ")\n",
+ "#fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "for rev in revenus:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}_prop\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des différentes catégories de revenus par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "for rev in revenus:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}_uc_prop\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des différentes catégories de revenus par uc par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "for rev in [\"prestations_positives_prop\", \"impot_negatif_prop\", \"ni_impot_ni_presta_prop\",\"revenus_positifs_prop\",\"revenus_act_positifs_prop\",\"revenus_remp_positifs_prop\",\"revenus_fon_cap_positifs_prop\"]:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des personnes payant des impots, recevant des prestations sociales ou aucun des deux par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "#for rev in [\"prestations_positives_prop\", \"impot_negatif_prop\", \"ni_impot_ni_presta_prop\"]:\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"prestations_uc_pond\"] / nb[\"prestations_positives\"],\n",
+ " name = \"Montant moyen de prestation\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"impots_uc_pond\"] / nb[\"impot_negatif\"],\n",
+ " name = \"Montant moyen d'impot\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_activite_uc_pond\"] / nb[\"revenus_act_positifs\"],\n",
+ " name = \"Montant moyen de revenus d'activité\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_remplacement_uc_pond\"] / nb[\"revenus_remp_positifs\"],\n",
+ " name = \"Montant moyen de revenus de remplacement\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_foncier_capital_uc_pond\"] / nb[\"revenus_fon_cap_positifs\"],\n",
+ " name = \"Montant moyen de revenus fonciers et du capital\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_uc_pond\"] / nb[\"revenus_positifs\"],\n",
+ " name = \"Montant moyen de revenus\",\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des personnes payant des impots, recevant des prestations sociales ou aucun des deux par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "for rev in [\"revenus\",\"prestations\",\"revenus_activite\",\"revenus_remplacement\"]:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}_uc_moy\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Montants moyens de revenus imposables et de prestations par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analyse par dixième "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"acteu5pr\"] = menages.acteu5pr.map({\"1\":\"1\", \"2\":\"1\",\"3\":\"2\",\"4\":\"3\",\"5\":\"2\"})\n",
+ "menages[\"acteu5cj\"] = menages.acteu5cj.map({\"1\":\"1\", \"2\":\"1\",\"3\":\"2\",\"4\":\"3\",\"5\":\"2\"})\n",
+ "menages[\"acteu5pr_lab\"] = menages.acteu5pr.map({\"1\":\"en emploi\", \"2\":\"sans emploi\",\"3\":\"retraité\"})\n",
+ "menages[\"acteu5cj_lab\"] = menages.acteu5cj.map({\"1\":\"en emploi\", \"2\":\"sans emploi\",\"3\":\"retraité\"})\n",
+ "menages[\"categ_logmnt_lab\"] = np.where(menages[\"categ_logmnt_lab\"] == \"Autre\", 'Propriétaire',menages[\"categ_logmnt_lab\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test = menages.groupby([\"dixieme\",\"TYPMEN5_lab\",\"acteu5pr_lab\"]).agg({\"revenus_uc_pond\":\"sum\",\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index().sort_values([\"dixieme\",\"TYPMEN5_lab\", \"wprm\"])\n",
+ "test_check = menages.groupby([\"dixieme\",\"TYPMEN5_lab\",\"acteu5pr_lab\"]).agg({\"revenus_uc_pond\":\"max\"}).reset_index()\n",
+ "test_check.rename(columns = {\"revenus_uc_pond\":\"revenus_uc_max\"}, inplace = True)\n",
+ "test = pd.merge(test_check,test, on = [\"dixieme\",\"TYPMEN5_lab\",\"acteu5pr_lab\"])\n",
+ "test[\"check_poids_max\"] = test[\"revenus_uc_max\"] / test[\"revenus_uc_pond\"]\n",
+ "test[\"revenus_uc_moy\"] = test.revenus_uc_pond / test.wprm\n",
+ "test = test.loc[test[\"TYPMEN5_lab\"]!= \"autre / n.a\"]\n",
+ "test.rename(columns = {'ident21':'count'},inplace = True)\n",
+ "test = test.loc[test[\"wprm\"] > 200000]\n",
+ "test.to_csv(f\"{output_path}/check_cas_majoritaire_dixieme.csv\")\n",
+ "test = test.drop(columns = [\"check_poids_max\", \"revenus_uc_max\"])\n",
+ "test.to_csv(f\"{output_path}/cas_majoritaire_dixieme.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# SMIC net 2021 après csg et crds, source série chornologique INSEE, pour 35h de travail \n",
+ "smic = 9 * 1230.6 + 3 * 1258.22\n",
+ "smic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"part_rev_decl_pond\"] = menages.part_rev_decl * menages.wprm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_moy(data, group_var):\n",
+ " moy = data.groupby(group_var).agg({\"ident21\":\"count\",\"wprm\":\"sum\",\"revenus_pond\":\"sum\",\"prestations_pond\":\"sum\",\"agprl_pond\":\"sum\",\"revenus_remplacement_pond\":\"sum\",\"revenus_activite_pond\":\"sum\",\"revenus_foncier_capital_pond\":\"sum\",\"part_rev_decl_pond\":\"sum\"})\n",
+ " moy.rename(columns={\"ident21\":\"count\"},inplace = True)\n",
+ " moy[\"revenus_moy\"] = moy.revenus_pond / moy.wprm\n",
+ " moy[\"revenus_remplacement_moy\"] = moy.revenus_remplacement_pond / moy.wprm\n",
+ " moy[\"revenus_activite_moy\"] = moy.revenus_activite_pond / moy.wprm\n",
+ " moy[\"revenus_fon_cap_moy\"] = moy.revenus_foncier_capital_pond / moy.wprm\n",
+ " moy[\"prestations_moy\"] = moy.prestations_pond / moy.wprm\n",
+ " moy[\"age_moy\"] = moy.agprl_pond / moy.wprm\n",
+ " #moy[\"part_rev_decl_moy\"] = moy.part_rev_decl_pond / moy.wprm\n",
+ " moy[\"rev_pourc_smic\"] = moy.revenus_moy / smic\n",
+ " moy = moy.drop([\"revenus_pond\",\"prestations_pond\",\"agprl_pond\",\"revenus_remplacement_pond\",\"revenus_activite_pond\",\"revenus_foncier_capital_pond\",\"part_rev_decl_pond\"], axis = 1)\n",
+ " return moy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_logmnt(data,group_var):\n",
+ " nb_logtm = data.groupby([group_var, \"categ_logmnt_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ " nb_logtm.rename(columns={\"ident21\":\"count\"},inplace = True)\n",
+ " nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(group_var)['wprm'].transform(\"sum\"))\n",
+ " nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ " nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")\n",
+ " print(nb_logtm)\n",
+ " fig = go.Figure()\n",
+ " fig = px.histogram(nb_logtm,\n",
+ " x = group_var,\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_logmnt_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 5\n",
+ " )\n",
+ " fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyse_personne_seule(data):\n",
+ " print(\"Répartition des statut d'activité de la personne : \")\n",
+ " print(100*data.groupby('acteu5pr_lab').agg({\"wprm\":\"sum\"})/data.wprm.sum())\n",
+ "\n",
+ " moy = compute_moy(data, \"acteu5pr_lab\")\n",
+ " print(\"Moyenne des revenus par catégorie d'actitivité : \")\n",
+ " print(moy)\n",
+ "\n",
+ " compute_logmnt(data, \"acteu5pr_lab\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_statut_couple(data):\n",
+ " nb = data.groupby([\"acteu5pr_lab\", \"acteu5cj_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ " nb.rename(columns = {\"ident21\":\"count\"})\n",
+ " nb[\"prop\"] = round(100 * nb.wprm /nb.groupby(\"acteu5pr_lab\")['wprm'].transform(\"sum\"))\n",
+ " nb[\"distrib\"] = round(100 * nb.wprm /nb.groupby(\"acteu5cj_lab\")['wprm'].transform(\"sum\"))\n",
+ " nb.groupby(\"acteu5cj_lab\")['wprm'].agg(\"sum\")\n",
+ " print(nb)\n",
+ " fig = go.Figure()\n",
+ " fig = px.histogram(nb,\n",
+ " x = \"acteu5pr_lab\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"acteu5cj\",\n",
+ " color = \"acteu5cj_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 5\n",
+ " )\n",
+ " fig.update_layout(title = \"Part des menages en fonction de leur statut d'activité\", bargap = 0.2)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyse_couple(data):\n",
+ " print(\"Statut activité adulte 1\")\n",
+ " print(100*data.groupby('acteu5pr_lab').agg({\"wprm\":\"sum\"})/data.wprm.sum())\n",
+ " print(\"Statut activité adulte 2\")\n",
+ " print(100*data.groupby('acteu5cj_lab').agg({\"wprm\":\"sum\"})/data.wprm.sum())\n",
+ " compute_statut_couple(data)\n",
+ " print(\"Nombre d'apporteur de ressources du ménage\")\n",
+ " #temp = data.groupby('nbres').agg({\"wprm\":\"sum\",\"part_rev_decl_pond\":\"sum\"})\n",
+ " #temp[\"part_rev_decl_moy\"] = 100*temp.part_rev_decl_pond / temp.wprm\n",
+ " temp = data.groupby('nbres').agg({\"wprm\":\"sum\"}).reset_index()\n",
+ " temp[\"wprm\"] = 100 * temp.wprm/data.wprm.sum()\n",
+ " #print(temp[[\"wprm\",\"part_rev_decl_moy\"]])\n",
+ " print(temp.loc[temp['nbres'] < 3][[\"wprm\"]])\n",
+ " moy = compute_moy(data, \"acteu5pr_lab\")\n",
+ " print(\"Moyenne des revenus par catégorie d'actitivité de l'adulte 1 : \")\n",
+ " print(moy)\n",
+ " compute_logmnt(data, \"acteu5pr_lab\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyse_quantile(quantile):\n",
+ " print(f\" \\n __________________ QUANTILE {quantile} ____________________\")\n",
+ "\n",
+ " print(\" \\n ***** Personne seule ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"1\"]\n",
+ " if quantile >= 5:\n",
+ " focus_type = focus_type.loc[focus.acteu5pr != \"2\"] # on enlève les sans emploi dans le haut de la distribution car pas assez de monde\n",
+ " analyse_personne_seule(focus_type)\n",
+ " #menages = menages.loc[menages[\"TYPMEN5\"] != \"5\"]\n",
+ " print(\" \\n ***** Couple sans enfant ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"3\"]\n",
+ " analyse_couple(focus_type)\n",
+ " print(\" \\n ***** Couple avec enfant(s) ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"4\"]\n",
+ " focus_type['acteu5pr_lab'] = np.where(focus_type['acteu5pr_lab'] == \"retraité\",\"sans emploi\",focus_type['acteu5pr_lab'])\n",
+ " focus_type['acteu5cj_lab'] = np.where(focus_type['acteu5cj_lab'] == \"retraité\",\"sans emploi\",focus_type['acteu5cj_lab'])\n",
+ " analyse_couple(focus_type)\n",
+ "\n",
+ " if quantile < 4:\n",
+ " print(\" \\n ***** Famille monoparentale ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"2\"]\n",
+ " focus_type['acteu5pr_lab'] = np.where(focus_type['acteu5pr_lab'] == \"retraité\",\"sans emploi\",focus_type['acteu5pr_lab'])\n",
+ " analyse_personne_seule(focus_type)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(1,2):\n",
+ " analyse_quantile(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for quantile in range(1,11):\n",
+ " analyse_quantile(quantile)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "leximpact2024-kernel",
+ "language": "python",
+ "name": "leximpact2024-kernel"
+ },
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diff --git a/notebooks/memos/memo_dixieme_niveau_de_vie.ipynb b/notebooks/memos/memo_dixieme_niveau_de_vie.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f29fd85ee97c32d2bbe5f96967dfa5cb9c6c4256
--- /dev/null
+++ b/notebooks/memos/memo_dixieme_niveau_de_vie.ipynb
@@ -0,0 +1,12889 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ce script tourne avec le kernel jupyter leximpact2024 issu de l'environnement conda leximpa construit à partir des dépendances de leximpact-prepare-data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from openfisca_survey_manager.statshelpers import mark_weighted_percentiles\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.express as px \n",
+ "from plotly.subplots import make_subplots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = \"/mnt/data-in/casd_extract/erfs/20240306_erfs_2021_stats_dixieme\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Type de ménage\n",
+ "\n",
+ "- 1: personne seule\n",
+ "- 2: famille monoparentale\n",
+ "- 3: couple sans enfant\n",
+ "- 4: couple avec enfant\n",
+ "- 5: autre"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## nombre d'enfants dans le ménage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
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+ "source": [
+ "nb_enfc = pd.read_csv(f\"{data_path}/nb_encf.csv\")\n",
+ "\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du nombre d'enfants dans ménage par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du nombre d'enfants\")\n",
+ "fig.show()"
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+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## nombre de parents / couple"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
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+ },
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+ "source": [
+ "nb_typmen = pd.read_csv(f\"{data_path}/nb_typmen.csv\")\n",
+ "\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_typmen,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"TYPMEN5_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du type de ménage par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_typmen,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"TYPMEN5_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction de leur type\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### focus sur le nombre d'enfants en fonction de en couple / familles monoparentales"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_enfc_couple = menages.loc[menages[\"categ_nbenfc\"] != \"Pas d'enfant\"].groupby([\"dixieme\", \"TYPMEN5\", \"TYPMEN5_lab\", \"categ_nbenfc\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_enfc_couple.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_enfc_couple[\"prop\"] = round(100 * nb_enfc_couple.wprm /nb_enfc_couple.groupby([\"dixieme\",\"TYPMEN5\",\"TYPMEN5_lab\"])['wprm'].transform(\"sum\"))\n",
+ "nb_enfc_couple[\"distrib\"] = round(100 * nb_enfc_couple.wprm /nb_enfc_couple.groupby([\"categ_nbenfc\", \"TYPMEN5\",\"TYPMEN5_lab\"])['wprm'].transform(\"sum\"))\n",
+ "nb_enfc_couple[\"somme_categ_parents\"] = nb_enfc_couple.groupby([\"categ_nbenfc\", \"TYPMEN5\",\"TYPMEN5_lab\"])['wprm'].transform(\"sum\")\n",
+ "nb_enfc_couple[\"wprm\"] = np.where((nb_enfc_couple.categ_nbenfc != \"1 enfant\") & (nb_enfc_couple.dixieme >= 6), np.nan, nb_enfc_couple[\"wprm\"])\n",
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+ "nb_enfc_couple.to_csv(f\"{output_path}/nb_enfc_couple.csv\")\n",
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+ "nb_enfc_couple = pd.read_csv(f\"{data_path}/nb_enfc_couple.csv\")\n",
+ "\n",
+ "data_fig = nb_enfc_couple[[\"categ_nbenfc\", \"somme_categ_parents\",\"TYPMEN5\",\"TYPMEN5_lab\"]].drop_duplicates()\n",
+ "fig = make_subplots(\n",
+ " rows = 1,\n",
+ " cols = 2,\n",
+ " specs=[\n",
+ " [{\"type\":\"domain\"},{\"type\":\"domain\"}]\n",
+ " ],\n",
+ " subplot_titles= [f\"Familles monoparentales : {round(data_fig.loc[nb_enfc_couple['TYPMEN5'] == 2].somme_categ_parents.sum()/1e6, ndigits = 2)} millions de ménages\", f\"Couples : {round(data_fig.loc[nb_enfc_couple['TYPMEN5'] == 4].somme_categ_parents.sum()/1e6, ndigits = 2)} millions de ménages\"]\n",
+ ")\n",
+ "\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = data_fig.loc[data_fig[\"TYPMEN5\"] == 2].categ_nbenfc,\n",
+ " values = data_fig.loc[data_fig[\"TYPMEN5\"] == 2].somme_categ_parents,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Famille monoparentale\"\n",
+ " ),\n",
+ " 1,\n",
+ " 1\n",
+ ")\n",
+ "\n",
+ "fig.add_trace(\n",
+ " go.Pie(\n",
+ " labels = data_fig.loc[data_fig[\"TYPMEN5\"] == 4].categ_nbenfc,\n",
+ " values = data_fig.loc[data_fig[\"TYPMEN5\"] == 4].somme_categ_parents,\n",
+ " scalegroup=\"one\",\n",
+ " name = \"Couple\"\n",
+ " ),\n",
+ " 1,\n",
+ " 2\n",
+ ")\n",
+ "fig.update_layout(legend = dict(title = \"Nombre d'enfants\"))\n",
+ "fig.show()"
+ ]
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+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc_couple,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " text_auto = True,\n",
+ " nbins = 10,\n",
+ " facet_col = \"TYPMEN5_lab\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du nombre d'enfants à charge par dixieme de niveau de vie, à gauche les familles monoparentales, à droite les couples\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_enfc_couple,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_nbenfc\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " facet_col = \"TYPMEN5_lab\"\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du nombre d'enfants, à gauche les familles monoparentales, à droite les couples\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### statut marital des couples\n",
+ "\n",
+ "- 1: Marié.e\n",
+ "- 2: Pacsé.e\n",
+ "- 3: union libre\n",
+ "- 4: veuf.ve\n",
+ "- 5: divorcé.e\n",
+ "- 6: dépacsé.e\n",
+ "- 7: séparé.e\n",
+ "- 8: célibataire\n",
+ "- 9: non répose"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Statuts d'activité de la personne de référence et du conjoint\n",
+ "\n",
+ "- 1: salarié\n",
+ "- 2: indépendant\n",
+ "- 3: chômeur\n",
+ "- 4: retraité \n",
+ "- 5: inactif non retraité"
+ ]
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+ " #barmode=\"group\",\n",
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+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du statut d'activité de la personne de référence par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
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+ " color = \"acteu5pr_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du statut d'activité de la personne de référence\")\n",
+ "fig.show()"
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+ "source": [
+ "nb = pd.read_csv(f\"{data_path}/nb_acteu5cj.csv\")\n",
+ "#new_name = {\"1\":\"salarié.e\", \"2\":\"indépendant.e\",\"3\":\"chomeureuse\",\"4\":\"retraité\",\"5\":\"inactif.ve autre\"}\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"acteu5cj_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction du statut d'activité du conjoint par dixieme de niveau de vie\", bargap = 0.2)\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"acteu5cj_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10,\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction du statut d'activité du conjoint\")\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## statut d'occupation du logement \n",
+ "- 1 accédant à la propriété\n",
+ "- 2 propriétaire\n",
+ "- 3 locataire HLM\n",
+ "- 4 locataire vide\n",
+ "- 5 locataire meublé\n",
+ "- 6 logé gratuitement\n",
+ "- 7 usufruitier\n",
+ "- 9 non réponse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages['categ_logmnt'] = np.where(menages[\"logT\"] == \"5\",\"4\",menages[\"logT\"])\n",
+ "menages['categ_logmnt'] = np.where(menages[\"logT\"] == \"9\",\"6\", menages[\"categ_logmnt\"])\n",
+ "menages['categ_logmnt'] = np.where(menages[\"logT\"] == \"1\",\"2\", menages[\"categ_logmnt\"])\n",
+ "menages['categ_logmnt_lab'] = menages.categ_logmnt.map({\"2\":\"Propriétaire\",\"3\":\"Locataire HLM\",\"4\":\"Locataire privé\",\"6\":\"Autre\"})\n",
+ "nb_logtm = menages.groupby([\"dixieme\", \"categ_logmnt_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"dixieme\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_logtm.to_csv(f\"{output_path}/nb_logtm.csv\")\n",
+ "nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_logtm = pd.read_csv(f\"{data_path}/nb_logtm.csv\")\n",
+ "\n",
+ "#new_name = {\"2\":\"Propriétaire\",\"3\":\"Locataire HLM\",\"4\":\"Locataire privé\",\"6\":\"Autre\"}\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_logmnt_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"distrib\",\n",
+ " color = \"categ_logmnt_lab\",\n",
+ " barmode = \"group\",\n",
+ " text_auto = True,\n",
+ " nbins= 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Distribution des ménages en fonction de leur statut d'occupation du logement\")\n",
+ "#fig.for_each_trace(lambda t: t.update(name = new_name[t.name]))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Part des propriétaires en fonction du statut d'occupation du type de ménage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_logtm = menages.groupby([\"dixieme\", \"categ_logmnt_lab\", \"TYPMEN5_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby([\"dixieme\", \"TYPMEN5_lab\"])['wprm'].transform(\"sum\"))\n",
+ "#nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_logtm = nb_logtm.loc[nb_logtm[\"categ_logmnt_lab\"] == \"Propriétaire\"]\n",
+ "nb_logtm = nb_logtm.loc[nb_logtm[\"TYPMEN5_lab\"] != \"autre / n.a\"]\n",
+ "nb_logtm.to_csv(f\"{output_path}/nb_logtm_typmen.csv\")\n",
+ "#nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " barmode=\"group\",\n",
+ " color = \"TYPMEN5_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb_logtm = menages.groupby([\"dixieme\", \"categ_logmnt_lab\", \"acteu5pr_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ "nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby([\"dixieme\", \"acteu5pr_lab\"])['wprm'].transform(\"sum\"))\n",
+ "#nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ "nb_logtm.rename(columns = {\"ident21\":\"count\"},inplace = True)\n",
+ "nb_logtm = nb_logtm.loc[nb_logtm[\"categ_logmnt_lab\"] == \"Propriétaire\"]\n",
+ "nb_logtm.to_csv(f\"{output_path}/nb_logtm_acteu5pr.csv\")\n",
+ "#nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig = px.histogram(nb_logtm,\n",
+ " x = \"dixieme\",\n",
+ " y = \"prop\",\n",
+ " barmode=\"group\",\n",
+ " color = \"acteu5pr_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 10\n",
+ " )\n",
+ "fig.update_layout(title = \"Part des propriétaires en fonction du statut d'activité de la personne de référence\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Revenus déclarés"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "revdispm =\n",
+ "\n",
+ "+ revperm (revdecm (ztsam (zsalm + zchom) + zperm (zrstm + zpim + zrtom + zalrm) + zragm + zricm + zrncm + zfonm + zvamm + zvalm + zracm + zetrm -zalvm) + revexom + produitfin - csgim (csgsalim + csghsupim + csgchoim + csgrstim + csgragim + csgricim + csgrncim + csgpiim) - crdsm ( crdssalm + crdshsupm + crdschom + crdsrstm + crdsragm + crdsricm + crdsrncm + crdspim) - csgimpm (produits financiers imputés) - csgvalm (csg crds valeurs mobilières) - zimpvalm (prélèvement libératoire sur valeur mobilière du ménage))\n",
+ "+ psocm (pfamm + mibim + logtm)\n",
+ "- zimpom (impot sur le revenu)\n",
+ "- zthabm\n",
+ "- csgpatm (csg-crds sur revenus du patrimoine)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "prelevements = zimpom + zthabm + csgpatm + zimpvalm + csgvalm + csgimpm + crdsm + csgim\n",
+ "\n",
+ "prestations = pfamm + minim + logtm\n",
+ "\n",
+ "revenus = revenus_activite + revenus_remplacement + revenus_foncier_capital\n",
+ "\n",
+ "revenus_activite = zsalm + revexom + zragm + zricm + zrncm + zracm - zalvm + zetrm\n",
+ "revenus_remplacement = zchom + zrstm + zpim + zrtom + zalrm\n",
+ "revenus_foncier_capital = zfonm + zvamm + zvalm + produitfin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"prelevements\"] = menages.zimpom + menages.zthabm + menages.csgpatm + menages.zimpvalm + menages.csgvalm + menages.csgimpm + menages.crdsm + menages.csgim\n",
+ "menages[\"impots\"] = menages.zimpom + menages.zthabm + menages.zimpvalm\n",
+ "# prelevements = impots + csg imposable + crds\n",
+ "menages.rename(columns = {\"psocm\":\"prestations\"},inplace = True)\n",
+ "menages[\"revenus_activite\"] = menages.zsalm + menages.revexom + menages.zragm + menages.zricm + menages.zrncm + menages.zracm - menages.zalvm + menages.zetrm\n",
+ "# traitement et salaires hors chomage, revenus exonérés (primes / heures sup.) + revenus des indépendants + revenus étrangers - pensions alimentaires versées\n",
+ "menages[\"revenus_remplacement\"] = menages.zchom + menages.zrstm + menages.zpim + menages.zrtom + menages.zalrm\n",
+ "# chomage + retraite + pension invalidite + rente viagière à titre onéreux + pension alimentaire recue\n",
+ "menages[\"revenus_foncier_capital\"] = menages.zfonm + menages.zvamm + menages.zvalm + menages.produitfin\n",
+ "menages[\"revenus\"] = menages[\"revenus_activite\"] + menages[\"revenus_remplacement\"] + menages[\"revenus_foncier_capital\"]\n",
+ "menages[\"diff\"] = menages[\"revdispm\"] - (menages[\"revenus\"] - menages[\"prelevements\"] + menages[\"prestations\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sum(abs(menages[\"diff\"]) > 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"revdecm_net\"] = menages[\"revdecm\"] - menages[\"csgdm\"]\n",
+ "# revenus = [\n",
+ "# \"ztsam\", # zchom + zsalm\n",
+ "# \"zchom\",\n",
+ "# \"zsalm\",\n",
+ "# \"zperm\",\n",
+ "# \"zalrm\", # pensions alim recues, dans zalrm\n",
+ "# \"zragm\",\n",
+ "# \"zricm\",\n",
+ "# \"zrncm\",\n",
+ "# \"zfonm\",\n",
+ "# \"zvamm\",\n",
+ "# \"zvalm\",\n",
+ "# \"zracm\",\n",
+ "# \"zetrm\",\n",
+ "# \"zalvm\",\n",
+ "# ]\n",
+ "\n",
+ "revenus = [\n",
+ " \"revenus_activite\",\n",
+ " \"revenus_remplacement\",\n",
+ " \"revenus_foncier_capital\"\n",
+ "]\n",
+ "\n",
+ "prelevements = [\"prelevements\", \"impots\"]\n",
+ "\n",
+ "rev_tot = [ \n",
+ " \"revdecm\",\n",
+ " \"revdecm_net\",\n",
+ " \"revdispm\",\n",
+ " \"revenus\",\n",
+ " \"prestations\"] \n",
+ "for rev in revenus + rev_tot + prelevements:\n",
+ " menages[f\"{rev}_pond\"] = menages[rev].fillna(0) * menages.wprm\n",
+ " menages[f\"{rev}_uc_pond\"] = menages[rev].fillna(0) / menages[\"nb_UCI\"].fillna(1) * menages.wprm\n",
+ "\n",
+ "agg = {f\"{rev}_pond\":\"sum\" for rev in revenus + rev_tot + prelevements}\n",
+ "agg.update({\"wprm\":\"sum\",\"ident21\":\"count\"})\n",
+ "agg.update({f\"{rev}_uc_pond\":\"sum\" for rev in revenus + rev_tot + prelevements})\n",
+ "menages[\"revenus_individus\"] = (np.maximum(0,menages[\"rev_activite_conj\"]) + menages[\"chomage_conj\"] + menages[\"pensions_conj\"] + np.maximum(0,menages[\"rev_activite_decl\"]) + menages[\"chomage_decl\"] + menages[\"pensions_decl\"])\n",
+ "menages[\"part_rev_decl\"] = np.where(menages[\"revenus_individus\"] > 0, np.maximum(0,menages[\"rev_activite_decl\"]) + menages[\"chomage_decl\"] + menages[\"pensions_decl\"] / menages[\"revenus_individus\"],0)\n",
+ "menages[\"prestations_positives\"] = (menages[\"prestations\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_positifs\"] = (menages[\"revenus\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_act_positifs\"] = (menages[\"revenus_activite\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_remp_positifs\"] = (menages[\"revenus_remplacement\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"revenus_fon_cap_positifs\"] = (menages[\"revenus_foncier_capital\"] > 0) * menages[\"wprm\"]\n",
+ "menages[\"impot_negatif\"] = (menages[\"impots\"] > 0) * menages[\"wprm\"]# si impot positif alors le menage paie de l'impot\n",
+ "menages[\"ni_impot_ni_presta\"] =(menages[\"prestations\"] == 0) * (menages[\"impots\"] == 0) * menages[\"wprm\"]\n",
+ "agg.update({\"impot_negatif\":\"sum\",\"revenus_positifs\":\"sum\",\"revenus_act_positifs\":\"sum\",\"revenus_remp_positifs\":\"sum\",\"revenus_fon_cap_positifs\":\"sum\",\"prestations_positives\":\"sum\",\"ni_impot_ni_presta\":\"sum\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# agg = {f\"{rev}\":\"max\" for rev in revenus + rev_tot + prelevements}\n",
+ "# nb_max = menages.groupby([\"dixieme\"]).agg(agg).reset_index()\n",
+ "# nb_max.rename(columns = {rev:f\"{rev}_max\" for rev in nb_max.columns if rev != \"dixieme\"}, inplace = True)\n",
+ "# agg = {f\"{rev}\":\"sum\" for rev in revenus + rev_tot + prelevements}\n",
+ "# nb = menages.groupby([\"dixieme\"]).agg(agg).reset_index()\n",
+ "# nb = pd.merge(nb_max,nb,on = \"dixieme\")\n",
+ "# for rev in revenus + rev_tot + prelevements:\n",
+ "# nb[f\"{rev}_part_max\"] = nb[f\"{rev}_max\"] / nb[f\"{rev}\"]\n",
+ "# nb.to_csv(f\"{output_path}/check_part_max_nb_revenus.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nb = menages.groupby([\"dixieme\"]).agg(agg).reset_index()\n",
+ "nb.to_csv(f\"{output_path}/nb_revenus.csv\")\n",
+ "for rev in revenus + rev_tot + prelevements:\n",
+ " nb[f\"{rev}_prop\"] = nb[f\"{rev}_pond\"] / nb[\"revenus_pond\"]\n",
+ " nb[f\"{rev}_moy\"] = nb[f\"{rev}_pond\"] / nb[\"wprm\"]\n",
+ " nb[f\"{rev}_uc_prop\"] = nb[f\"{rev}_uc_pond\"] / nb[\"revenus_uc_pond\"]\n",
+ " nb[f\"{rev}_uc_moy\"] = nb[f\"{rev}_uc_pond\"] / nb[\"wprm\"]\n",
+ "nb[\"prestations_positives_prop\"] = nb[\"prestations_positives\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_positifs_prop\"] = nb[\"revenus_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_act_positifs_prop\"] = nb[\"revenus_act_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_remp_positifs_prop\"] = nb[\"revenus_remp_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"revenus_fon_cap_positifs_prop\"] = nb[\"revenus_fon_cap_positifs\"] / nb[\"wprm\"]\n",
+ "nb[\"impot_negatif_prop\"] = nb[\"impot_negatif\"] / nb[\"wprm\"]\n",
+ "nb[\"ni_impot_ni_presta_prop\"] = nb[\"ni_impot_ni_presta\"] / nb[\"wprm\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "fig.add_trace(\n",
+ " go.Line(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revdecm_uc_moy\"],\n",
+ " name = \"revdecm_uc_moy\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Line(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revdispm_uc_moy\"],\n",
+ " name = \"revdispm_uc_moy\",\n",
+ " )\n",
+ ")\n",
+ "#fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "for rev in revenus:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}_prop\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des différentes catégories de revenus par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()\n",
+ "fig = go.Figure()\n",
+ "for rev in revenus:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}_uc_prop\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des différentes catégories de revenus par uc par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "for rev in [\"prestations_positives_prop\", \"impot_negatif_prop\", \"ni_impot_ni_presta_prop\",\"revenus_positifs_prop\",\"revenus_act_positifs_prop\",\"revenus_remp_positifs_prop\",\"revenus_fon_cap_positifs_prop\"]:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des personnes payant des impots, recevant des prestations sociales ou aucun des deux par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "#for rev in [\"prestations_positives_prop\", \"impot_negatif_prop\", \"ni_impot_ni_presta_prop\"]:\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"prestations_uc_pond\"] / nb[\"prestations_positives\"],\n",
+ " name = \"Montant moyen de prestation\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"impots_uc_pond\"] / nb[\"impot_negatif\"],\n",
+ " name = \"Montant moyen d'impot\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_activite_uc_pond\"] / nb[\"revenus_act_positifs\"],\n",
+ " name = \"Montant moyen de revenus d'activité\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_remplacement_uc_pond\"] / nb[\"revenus_remp_positifs\"],\n",
+ " name = \"Montant moyen de revenus de remplacement\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_foncier_capital_uc_pond\"] / nb[\"revenus_fon_cap_positifs\"],\n",
+ " name = \"Montant moyen de revenus fonciers et du capital\",\n",
+ " )\n",
+ ")\n",
+ "fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[\"revenus_uc_pond\"] / nb[\"revenus_positifs\"],\n",
+ " name = \"Montant moyen de revenus\",\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Part des personnes payant des impots, recevant des prestations sociales ou aucun des deux par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = go.Figure()\n",
+ "for rev in [\"revenus\",\"prestations\",\"revenus_activite\",\"revenus_remplacement\"]:\n",
+ " fig.add_trace(\n",
+ " go.Bar(\n",
+ " x = nb.dixieme,\n",
+ " y = nb[f\"{rev}_uc_moy\"],\n",
+ " name = rev,\n",
+ " )\n",
+ ")\n",
+ "fig.update_layout(title = \"Montants moyens de revenus imposables et de prestations par dixième de niveau de vie\", bargap = 0.2)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analyse par dixième "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"acteu5pr\"] = menages.acteu5pr.map({\"1\":\"1\", \"2\":\"1\",\"3\":\"2\",\"4\":\"3\",\"5\":\"2\"})\n",
+ "menages[\"acteu5cj\"] = menages.acteu5cj.map({\"1\":\"1\", \"2\":\"1\",\"3\":\"2\",\"4\":\"3\",\"5\":\"2\"})\n",
+ "menages[\"acteu5pr_lab\"] = menages.acteu5pr.map({\"1\":\"en emploi\", \"2\":\"sans emploi\",\"3\":\"retraité\"})\n",
+ "menages[\"acteu5cj_lab\"] = menages.acteu5cj.map({\"1\":\"en emploi\", \"2\":\"sans emploi\",\"3\":\"retraité\"})\n",
+ "menages[\"categ_logmnt_lab\"] = np.where(menages[\"categ_logmnt_lab\"] == \"Autre\", 'Propriétaire',menages[\"categ_logmnt_lab\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test = menages.groupby([\"dixieme\",\"TYPMEN5_lab\",\"acteu5pr_lab\"]).agg({\"revenus_uc_pond\":\"sum\",\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index().sort_values([\"dixieme\",\"TYPMEN5_lab\", \"wprm\"])\n",
+ "test_check = menages.groupby([\"dixieme\",\"TYPMEN5_lab\",\"acteu5pr_lab\"]).agg({\"revenus_uc_pond\":\"max\"}).reset_index()\n",
+ "test_check.rename(columns = {\"revenus_uc_pond\":\"revenus_uc_max\"}, inplace = True)\n",
+ "test = pd.merge(test_check,test, on = [\"dixieme\",\"TYPMEN5_lab\",\"acteu5pr_lab\"])\n",
+ "test[\"check_poids_max\"] = test[\"revenus_uc_max\"] / test[\"revenus_uc_pond\"]\n",
+ "test[\"revenus_uc_moy\"] = test.revenus_uc_pond / test.wprm\n",
+ "test = test.loc[test[\"TYPMEN5_lab\"]!= \"autre / n.a\"]\n",
+ "test.rename(columns = {'ident21':'count'},inplace = True)\n",
+ "test = test.loc[test[\"wprm\"] > 200000]\n",
+ "test.to_csv(f\"{output_path}/check_cas_majoritaire_dixieme.csv\")\n",
+ "test = test.drop(columns = [\"check_poids_max\", \"revenus_uc_max\"])\n",
+ "test.to_csv(f\"{output_path}/cas_majoritaire_dixieme.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# SMIC net 2021 après csg et crds, source série chornologique INSEE, pour 35h de travail \n",
+ "smic = 9 * 1230.6 + 3 * 1258.22\n",
+ "smic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "menages[\"part_rev_decl_pond\"] = menages.part_rev_decl * menages.wprm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_moy(data, group_var):\n",
+ " moy = data.groupby(group_var).agg({\"ident21\":\"count\",\"wprm\":\"sum\",\"revenus_pond\":\"sum\",\"prestations_pond\":\"sum\",\"agprl_pond\":\"sum\",\"revenus_remplacement_pond\":\"sum\",\"revenus_activite_pond\":\"sum\",\"revenus_foncier_capital_pond\":\"sum\",\"part_rev_decl_pond\":\"sum\"})\n",
+ " moy.rename(columns={\"ident21\":\"count\"},inplace = True)\n",
+ " moy[\"revenus_moy\"] = moy.revenus_pond / moy.wprm\n",
+ " moy[\"revenus_remplacement_moy\"] = moy.revenus_remplacement_pond / moy.wprm\n",
+ " moy[\"revenus_activite_moy\"] = moy.revenus_activite_pond / moy.wprm\n",
+ " moy[\"revenus_fon_cap_moy\"] = moy.revenus_foncier_capital_pond / moy.wprm\n",
+ " moy[\"prestations_moy\"] = moy.prestations_pond / moy.wprm\n",
+ " moy[\"age_moy\"] = moy.agprl_pond / moy.wprm\n",
+ " #moy[\"part_rev_decl_moy\"] = moy.part_rev_decl_pond / moy.wprm\n",
+ " moy[\"rev_pourc_smic\"] = moy.revenus_moy / smic\n",
+ " moy = moy.drop([\"revenus_pond\",\"prestations_pond\",\"agprl_pond\",\"revenus_remplacement_pond\",\"revenus_activite_pond\",\"revenus_foncier_capital_pond\",\"part_rev_decl_pond\"], axis = 1)\n",
+ " return moy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_logmnt(data,group_var):\n",
+ " nb_logtm = data.groupby([group_var, \"categ_logmnt_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ " nb_logtm.rename(columns={\"ident21\":\"count\"},inplace = True)\n",
+ " nb_logtm[\"prop\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(group_var)['wprm'].transform(\"sum\"))\n",
+ " nb_logtm[\"distrib\"] = round(100 * nb_logtm.wprm /nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].transform(\"sum\"))\n",
+ " nb_logtm.groupby(\"categ_logmnt_lab\")['wprm'].agg(\"sum\")\n",
+ " print(nb_logtm)\n",
+ " fig = go.Figure()\n",
+ " fig = px.histogram(nb_logtm,\n",
+ " x = group_var,\n",
+ " y = \"prop\",\n",
+ " #barmode=\"group\",\n",
+ " color = \"categ_logmnt_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 5\n",
+ " )\n",
+ " fig.update_layout(title = \"Part des menages en fonction de leur statut d'occupation du logement\", bargap = 0.2)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyse_personne_seule(data):\n",
+ " print(\"Répartition des statut d'activité de la personne : \")\n",
+ " print(100*data.groupby('acteu5pr_lab').agg({\"wprm\":\"sum\"})/data.wprm.sum())\n",
+ "\n",
+ " moy = compute_moy(data, \"acteu5pr_lab\")\n",
+ " print(\"Moyenne des revenus par catégorie d'actitivité : \")\n",
+ " print(moy)\n",
+ "\n",
+ " compute_logmnt(data, \"acteu5pr_lab\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_statut_couple(data):\n",
+ " nb = data.groupby([\"acteu5pr_lab\", \"acteu5cj_lab\"]).agg({\"wprm\":\"sum\",\"ident21\":\"count\"}).reset_index()\n",
+ " nb.rename(columns = {\"ident21\":\"count\"})\n",
+ " nb[\"prop\"] = round(100 * nb.wprm /nb.groupby(\"acteu5pr_lab\")['wprm'].transform(\"sum\"))\n",
+ " nb[\"distrib\"] = round(100 * nb.wprm /nb.groupby(\"acteu5cj_lab\")['wprm'].transform(\"sum\"))\n",
+ " nb.groupby(\"acteu5cj_lab\")['wprm'].agg(\"sum\")\n",
+ " print(nb)\n",
+ " fig = go.Figure()\n",
+ " fig = px.histogram(nb,\n",
+ " x = \"acteu5pr_lab\",\n",
+ " y = \"prop\",\n",
+ " #barmode=\"acteu5cj\",\n",
+ " color = \"acteu5cj_lab\",\n",
+ " text_auto = True,\n",
+ " nbins = 5\n",
+ " )\n",
+ " fig.update_layout(title = \"Part des menages en fonction de leur statut d'activité\", bargap = 0.2)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyse_couple(data):\n",
+ " print(\"Statut activité adulte 1\")\n",
+ " print(100*data.groupby('acteu5pr_lab').agg({\"wprm\":\"sum\"})/data.wprm.sum())\n",
+ " print(\"Statut activité adulte 2\")\n",
+ " print(100*data.groupby('acteu5cj_lab').agg({\"wprm\":\"sum\"})/data.wprm.sum())\n",
+ " compute_statut_couple(data)\n",
+ " print(\"Nombre d'apporteur de ressources du ménage\")\n",
+ " #temp = data.groupby('nbres').agg({\"wprm\":\"sum\",\"part_rev_decl_pond\":\"sum\"})\n",
+ " #temp[\"part_rev_decl_moy\"] = 100*temp.part_rev_decl_pond / temp.wprm\n",
+ " temp = data.groupby('nbres').agg({\"wprm\":\"sum\"}).reset_index()\n",
+ " temp[\"wprm\"] = 100 * temp.wprm/data.wprm.sum()\n",
+ " #print(temp[[\"wprm\",\"part_rev_decl_moy\"]])\n",
+ " print(temp.loc[temp['nbres'] < 3][[\"wprm\"]])\n",
+ " moy = compute_moy(data, \"acteu5pr_lab\")\n",
+ " print(\"Moyenne des revenus par catégorie d'actitivité de l'adulte 1 : \")\n",
+ " print(moy)\n",
+ " compute_logmnt(data, \"acteu5pr_lab\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyse_quantile(quantile):\n",
+ " print(f\" \\n __________________ QUANTILE {quantile} ____________________\")\n",
+ "\n",
+ " print(\" \\n ***** Personne seule ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"1\"]\n",
+ " if quantile >= 5:\n",
+ " focus_type = focus_type.loc[focus.acteu5pr != \"2\"] # on enlève les sans emploi dans le haut de la distribution car pas assez de monde\n",
+ " analyse_personne_seule(focus_type)\n",
+ " #menages = menages.loc[menages[\"TYPMEN5\"] != \"5\"]\n",
+ " print(\" \\n ***** Couple sans enfant ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"3\"]\n",
+ " analyse_couple(focus_type)\n",
+ " print(\" \\n ***** Couple avec enfant(s) ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"4\"]\n",
+ " focus_type['acteu5pr_lab'] = np.where(focus_type['acteu5pr_lab'] == \"retraité\",\"sans emploi\",focus_type['acteu5pr_lab'])\n",
+ " focus_type['acteu5cj_lab'] = np.where(focus_type['acteu5cj_lab'] == \"retraité\",\"sans emploi\",focus_type['acteu5cj_lab'])\n",
+ " analyse_couple(focus_type)\n",
+ "\n",
+ " if quantile < 4:\n",
+ " print(\" \\n ***** Famille monoparentale ***** \\n\")\n",
+ " focus_type = focus.loc[focus[\"TYPMEN5\"] == \"2\"]\n",
+ " focus_type['acteu5pr_lab'] = np.where(focus_type['acteu5pr_lab'] == \"retraité\",\"sans emploi\",focus_type['acteu5pr_lab'])\n",
+ " analyse_personne_seule(focus_type)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(1,2):\n",
+ " analyse_quantile(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for quantile in range(1,11):\n",
+ " analyse_quantile(quantile)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "leximpact-prepare-data-kernel",
+ "language": "python",
+ "name": "leximpact-prepare-data-kernel"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}