630 lines
16 KiB
Plaintext
630 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "16c86730-6982-4338-a98c-49f0c5b603ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"from functools import cache\n",
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"import pandas as pd\n",
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"\n",
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"pd.set_option(\"display.max_columns\", None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "754c0d91-a10d-4e84-9dc2-02b01ec1ac57",
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"metadata": {},
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"outputs": [],
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"source": [
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"@cache\n",
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"def get_dataset_for(year):\n",
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" return pd.read_csv(f\"datasets/sinac{year}DatosAbiertos.csv\", dtype=object)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d03c787f-f963-4eff-8294-63bc7b073d04",
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"metadata": {},
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"outputs": [],
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"source": [
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"_df = get_dataset_for(2013)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9b688263-b6ea-4cec-a1e3-8b7a9935d909",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"estados_mexicanos = {\n",
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" \"AGUASCALIENTES\",\n",
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" \"BAJA CALIFORNIA\",\n",
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" \"BAJA CALIFORNIA SUR\",\n",
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" \"CAMPECHE\",\n",
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" \"CHIAPAS\",\n",
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" \"CHIHUAHUA\",\n",
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" \"COAHUILA DE ZARAGOZA\",\n",
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" \"COLIMA\",\n",
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" \"DISTRITO FEDERAL\",\n",
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" \"DURANGO\",\n",
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" \"GUANAJUATO\",\n",
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" \"GUERRERO\",\n",
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" \"HIDALGO\",\n",
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" \"JALISCO\",\n",
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" \"MEXICO\",\n",
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" \"MICHOACAN DE OCAMPO\",\n",
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" \"MORELOS\",\n",
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" \"NAYARIT\",\n",
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" \"NUEVO LEON\",\n",
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" \"OAXACA\",\n",
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" \"PUEBLA\",\n",
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" \"QUERETARO DE ARTEAGA\",\n",
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" \"QUINTANA ROO\",\n",
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" \"SAN LUIS POTOSI\",\n",
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" \"SINALOA\",\n",
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" \"SONORA\",\n",
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" \"TABASCO\",\n",
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" \"TAMAULIPAS\",\n",
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" \"TLAXCALA\",\n",
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" \"VERACRUZ DE IGNACIO DE LA LLAVE\",\n",
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" \"YUCATAN\",\n",
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" \"ZACATECAS\",\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bbfc26f0-7bae-444c-9f08-cbf77096713a",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"_df.sample(n=20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f793f126-f1a6-417c-8801-b39fdd9b740d",
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"metadata": {},
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"outputs": [],
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"source": [
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"columns = [\n",
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" \"ENTIDAD_NACMAD\",\n",
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" \"FECH_NACM\",\n",
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" \"ESTADO_CIVIL\",\n",
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" \"ENTIDAD_RESMAD\",\n",
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" \"NUMERO_EMBARAZOS\",\n",
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" \"NACIDOS_MUERTOS\",\n",
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" \"NACIDOS_VIVOS\",\n",
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" \"SOBREVIVIENTES\",\n",
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" \"ANTERIOR_NACIO\",\n",
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" \"VIVE_AUN\",\n",
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" \"ORDEN_NAC\",\n",
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" \"ATENCION_PRENA\",\n",
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" \"TRIMESTR_ATEN\",\n",
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" \"SOBREVIVIO_PARTO\",\n",
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" \"ESCOLARIDAD\",\n",
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" \"DESC_OCUPHAB\",\n",
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" \"OCUPACION_HABITUAL\",\n",
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" \"TRABAJA_ACTUALMENTE\",\n",
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" \"FECH_NACH\",\n",
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" \"HORA_NACH\",\n",
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" \"SEXO_RN\",\n",
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" \"GESTACH\",\n",
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" \"TALLAH\",\n",
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" \"PESOH\",\n",
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" \"APGARH\",\n",
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" \"SILVERMAN\",\n",
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" \"NACIMIENTOS\",\n",
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" \"MES_NACI\",\n",
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" \"Producto\",\n",
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" \"CIE10\",\n",
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" \"CIE10_2da\",\n",
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" \"PROCEDIMIENTO\",\n",
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" \"OTRO_PROCEDIMIENTO\",\n",
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" \"LUGAR_NACIM\",\n",
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" \"ENTIDAD_NACIM\",\n",
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" \"ENTIDAD_CERTIF\",\n",
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" \"FECHA_CERTIF\",\n",
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"]\n",
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"columns = [x.lower() for x in columns]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3a0a131b-b6d5-490c-afe8-b38e01f67afa",
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"metadata": {},
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"outputs": [],
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"source": [
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"# len(set(columns) - set(df.columns.to_list()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "22d00a15-0b36-4da8-be7a-ea5081c0e20b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# columns_original = get_dataset_for(2013).columns.to_list()\n",
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"columns_selected = [\n",
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" \"edo_captura\",\n",
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" \"edo_nac_madre\",\n",
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" \"fecha_nac_madre\",\n",
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" \"edad_madre\",\n",
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" \"estado_conyugal\",\n",
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" \"entidad_residencia_madre\",\n",
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" \"numero_embarazos\",\n",
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" \"hijos_nacidos_muertos\",\n",
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" \"hijos_nacidos_vivos\",\n",
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" \"hijos_sobrevivientes\",\n",
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" \"el_hijo_anterior_nacio\",\n",
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" \"vive_aun_hijo_anterior\",\n",
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" \"orden_nacimiento\",\n",
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" \"recibio_atencion_prenatal\",\n",
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" \"trimestre_recibio_primera_consulta\",\n",
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" \"total_consultas_recibidas\",\n",
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" \"madre_sobrevivio_al_parto\",\n",
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" \"escolaridad_madre\",\n",
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" \"ocupacion_habitual_madre\",\n",
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" \"trabaja_actualmente\",\n",
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" \"fecha_nacimiento_nac_vivo\",\n",
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" \"hora_nacimiento_nac_vivo\",\n",
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" \"sexo_nac_vivo\",\n",
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" \"semanas_gestacion_nac_vivo\",\n",
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" \"talla_nac_vivo\",\n",
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" \"peso_nac_vivo\",\n",
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" \"valoracion_apgar_nac_vivo\",\n",
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" \"valoracion_silverman_nac_vivo\",\n",
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" \"producto_de_un_embarazo\",\n",
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" \"codigo_anomalia\",\n",
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" \"anomalia_congenita_nac_vivo\",\n",
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" \"entidad_nacimiento\",\n",
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" \"entidad_certifico\",\n",
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"]\n",
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"print(\" - \", end=\"\")\n",
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"print(*sorted(columns_selected), sep=\"\\n - \")\n",
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"len(columns_selected)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1938d1bb-b1a5-4d85-9e7e-6df8636bef9b",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"print(\"Año - #Cols - #Faltantes\")\n",
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"for year in range(2008, 2020):\n",
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" df = get_dataset_for(year)\n",
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" columns = set(df.columns.to_list())\n",
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" print(year, \" - \", len(columns), \" - \", len(set(columns_selected) - columns))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c9ebdd00-ee89-4806-82aa-589e4bd7e3df",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.concat([get_dataset_for(year)[columns_selected] for year in range(2010, 2017)])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "95ae7344-2c95-4f22-b381-faddec655ee8",
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"metadata": {},
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"outputs": [],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "28ab2aac-ab57-4c3c-ad7d-3af70bbdeb60",
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"metadata": {},
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"outputs": [],
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"source": [
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"def _ano_nacimiento_vivo_func(str_date):\n",
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" try:\n",
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" return str_date.split(\"/\")[-1]\n",
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" except:\n",
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" return \"\"\n",
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"\n",
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"\n",
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"df[\"año_de_nacimiento_vivo\"] = df[\"fecha_nacimiento_nac_vivo\"].apply(\n",
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" _ano_nacimiento_vivo_func\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4dd783b6-3639-4ab6-9582-438f97a26e9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.edad_madre = df.edad_madre.astype(int)\n",
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"df = df[df.edad_madre < 120]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f941bb5f-dc2f-40d3-a626-00d948370241",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d962779a-4747-4bc3-9095-236459bb3c64",
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"metadata": {},
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"outputs": [],
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"source": [
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"set(df[\"año_de_nacimiento_vivo\"].to_list())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23b1d148-e7f8-44b2-9213-cd97b66762b8",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_trisomias = df[df[\"codigo_anomalia\"].apply(lambda x: \"Q9\" in x)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "46e4d052-cc6d-4ad8-908b-57d5a156d8a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_trisomias"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eeac7dfa-51f1-4942-8c7e-8932640aff02",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_trisomias[[\"edad_madre\"]].describe()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "199d6f0e-f957-42c5-8527-1e1e8496147f",
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"metadata": {},
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"source": [
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"# Agrupación por estado lugar de nacimiento de la madre, y año del registro, agregación de edad de la madre"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "204a3e36-b24c-44e3-ad2c-b440538c6fe2",
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"metadata": {},
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"outputs": [],
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"source": [
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"consulta = df.groupby([\"edo_captura\", \"edo_nac_madre\", \"año_de_nacimiento_vivo\"]).agg(\n",
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" {\n",
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" \"edad_madre\": [\n",
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" \"count\",\n",
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" \"mean\",\n",
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" \"std\",\n",
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" \"min\",\n",
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" \"max\",\n",
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" ],\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "69e1796d-cb0f-4704-ae9d-aafe7701707b",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"[row_name for row_name in consulta.index.to_list() if row_name[0] == \"JALISCO\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ed9657d0-4d2e-4400-b665-18dae6dfe777",
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"metadata": {},
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"outputs": [],
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"source": [
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"edos_fronterizos = {\n",
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" \"BAJA CALIFORNIA\",\n",
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" \"SONORA\",\n",
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" \"CHIHUAHUA\",\n",
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" \"COAHUILA DE ZARAGOZA\",\n",
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" \"NUEVO LEON\",\n",
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" \"TAMAULIPAS\",\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dbaedd5e-5ddb-4cc3-ad0d-3eccf610e78e",
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"metadata": {},
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"outputs": [],
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"source": [
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"edo_nac_madre_de_interes = {\n",
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" \"ESTADOS UNIDOS DE NORTEAMERICA\",\n",
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" \"OTROS PAISES\",\n",
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"} # \"OTROS PAISES DE LATINOAMERICA\"}\n",
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"consulta_edo_madre_especifico = consulta.loc[\n",
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" [\n",
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" row_name\n",
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" for row_name in consulta.index.to_list()\n",
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" if row_name[1] in edo_nac_madre_de_interes\n",
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" # and row_name[0] in edos_fronterizos\n",
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" ]\n",
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"]"
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]
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},
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{
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"cell_type": "raw",
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"id": "0897a47d-d9b7-4cde-a843-82a78418a77c",
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"metadata": {},
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"source": [
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"with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n",
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" display(consulta_edo_madre_especifico)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d3a4a225-caaa-4980-aa65-6c9c043b6971",
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"metadata": {},
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"outputs": [],
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"source": [
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"# consulta_edo_madre_especifico.index, consulta_edo_madre_especifico.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bfaa53b3-4006-4ded-8ff1-124ebd82d029",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib notebook\n",
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"from matplotlib.figure import Figure"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0e46df9b-891e-424e-9d56-21a7355f417c",
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"metadata": {},
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"outputs": [],
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"source": [
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"{x[0] for x in consulta_edo_madre_especifico.index.to_list()}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf52e20f-e51f-4e26-bbea-e7a318c17b44",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = Figure(figsize=(12, 6))\n",
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"ax = fig.add_subplot()\n",
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"edo_nac_madre_deseado = \"ESTADOS UNIDOS DE NORTEAMERICA\"\n",
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"_df = consulta_edo_madre_especifico.reset_index()\n",
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"edo = \"BAJA CALIFORNIA\"\n",
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"for edo in {x[0] for x in consulta_edo_madre_especifico.index.to_list()}:\n",
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" _to_plot = _df[\n",
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" (_df[\"edo_nac_madre\"] == edo_nac_madre_deseado) & (_df[\"edo_captura\"] == edo)\n",
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" ][[(\"año_de_nacimiento_vivo\", \"\"), (\"edad_madre\", \"count\")]]\n",
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" _to_plot.columns = [\"Año\", \"Cantidad\"]\n",
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" _to_plot = _to_plot.set_index(\"Año\")\n",
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"\n",
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" _to_plot.plot(y=\"Cantidad\", ax=ax, label=edo, linestyle=\"--\", grid=True)\n",
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"\n",
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"ax.legend(loc=\"upper left\")\n",
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"ax.set_xlabel(\"Año\")\n",
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"ax.set_ylabel(\"Cantidad de Nacimientos\")\n",
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"fig"
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3df800dd-8ca3-4ef0-9f86-e3ccddbd3f27",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"consulta_edo_madre_especifico.reset_index().columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0607aeff-41b8-49e0-985f-708a8be45d64",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Agrupación por estado de captura, y año"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "503b0648-5a53-4e10-97a5-6141d9c9c6b8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Edades de madres\n",
|
|
"consulta = df.groupby([\"edo_captura\", \"año_de_nacimiento_vivo\"]).agg(\n",
|
|
" {\n",
|
|
" \"edad_madre\": [\n",
|
|
" \"count\",\n",
|
|
" \"mean\",\n",
|
|
" \"std\",\n",
|
|
" \"min\",\n",
|
|
" \"max\",\n",
|
|
" ],\n",
|
|
" }\n",
|
|
")\n",
|
|
"consulta2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1016421b-f5d4-4d99-bf10-5994abf0377f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"_df = consulta2.reset_index()\n",
|
|
"_df.columns = [\"_\".join(x).rstrip(\"_\") for x in _df.columns.to_flat_index()]\n",
|
|
"_df = _df.rename(columns={\"año_de_nacimiento_vivo\": \"año\"})\n",
|
|
"_df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2fe9eafa-549f-45c0-923c-b59f5fdd5cc5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig = Figure(figsize=(12, 6))\n",
|
|
"ax = fig.add_subplot()\n",
|
|
"_df = consulta2.reset_index()\n",
|
|
"_df.columns = [\"_\".join(x).rstrip(\"_\") for x in _df.columns.to_flat_index()]\n",
|
|
"_df = _df.rename(columns={\"año_de_nacimiento_vivo\": \"año\"})\n",
|
|
"edo = \"BAJA CALIFORNIA\"\n",
|
|
"for edo in estados_mexicanos:\n",
|
|
" _to_plot = _df[(_df[\"edo_captura\"] == edo)][\n",
|
|
" [\"año\", \"edad_madre_mean\", \"edad_madre_std\"]\n",
|
|
" ]\n",
|
|
" _to_plot = _to_plot.set_index(\"año\")\n",
|
|
" _to_plot.plot(y=\"edad_madre_mean\", ax=ax, label=edo, linestyle=\"--\", grid=True)\n",
|
|
"\n",
|
|
"\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7f2d498f-afad-427d-b403-749dd2e7ac07",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig = Figure(figsize=(12, 6))\n",
|
|
"ax = fig.add_subplot()\n",
|
|
"_df = consulta2.reset_index()\n",
|
|
"for edo in {x[0] for x in consulta.index.to_list()}:\n",
|
|
" _to_plot = _df[(_df[\"edo_captura\"] == edo)][\n",
|
|
" [(\"año_de_nacimiento_vivo\", \"\"), (\"edad_madre\", \"count\")]\n",
|
|
" ]\n",
|
|
" _to_plot.columns = [\"Año\", \"Cantidad\"]\n",
|
|
" _to_plot = _to_plot.set_index(\"Año\")\n",
|
|
"\n",
|
|
" _to_plot.plot(y=\"Cantidad\", ax=ax, label=edo, linestyle=\"--\", grid=True)\n",
|
|
"\n",
|
|
"ax.legend(loc=\"upper left\")\n",
|
|
"ax.set_xlabel(\"Año\")\n",
|
|
"ax.set_ylabel(\"Cantidad de Nacimientos\")\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "80f2928c-b4b7-49ea-84c9-6a4aba677be2",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d97e103b-5384-4bae-b7b8-ebbdb7ec6a68",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Filtrar a solo observar los:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f60b5c93-ffb7-41d6-90a8-9a0e32d396f7",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Top GDP"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ee3bea25-e8bb-4af9-b246-b84b46a79e94",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Agrupación por zonas metropolitanas\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a99f74c8-33d2-44fe-b78f-b0a9e5267251",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|